diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index e3e479e..f6c9399 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -30,7 +30,8 @@ jobs: python3 .github/scripts/coverage_obligations.py \ --receipt test/gpu-proof.json python3 .github/scripts/shard_scope_closure.py - python3 -m pytest test/test_bench_common.py -q + git check-ignore --quiet bench/solver_ladder_20990101_000000.txt + python3 -m pytest test/test_bench_common.py test/test_tune_isolation.py -q compile-smoke: runs-on: ubuntu-latest diff --git a/bench/.gitignore b/bench/.gitignore index 71e8822..2fdcc82 100644 --- a/bench/.gitignore +++ b/bench/.gitignore @@ -9,6 +9,7 @@ __pycache__/ # Raw timing captures written by bench/tune.py / the harnesses (large, per-run). # The curated *_RESULTS.md summaries and regenerated tables ARE tracked. mega_sweep_*.txt +solver_ladder_*.txt reduced_sweep_*.txt blas2_sweep_*.txt rect_sweep_*.txt diff --git a/bench/JETSON.md b/bench/JETSON.md index 04413bd..d929d2c 100644 --- a/bench/JETSON.md +++ b/bench/JETSON.md @@ -4,8 +4,9 @@ One capture per box (Orin AGX / Orin NX / Orin Nano — all `sm_87`). Each run produces a single `bench/jetson__.tar.gz` containing the timings plus a full device/JetPack provenance bundle; send those back for ingestion (figure columns + the `ideal_sm87` ladder table, spliced off-box via -`tune.py --from-ladder --from-nvt-valid --sm 870` when MathDx is available; -native-only captures need no confirmation companion). +`tune.py --from-ladder --from-solver-ladder --sm 870`). The fresh-input +solver companion is required with or without MathDx so destructive native and +vendor implementations are measured under the same input policy. ## 0. Pre-flight: get on the latest JetPack the box supports @@ -114,9 +115,9 @@ methodology). The script: CPUs, `nvpmodel -q`, `jetson_clocks --show`, a `cudaGetDeviceProperties` probe (SM count is what separates AGX/NX/Nano), and idle `tegrastats`; 2. builds everything for `sm_87`; -3. runs the timed legs serially — the 3-tier SIMT ladder - (`tune.py --allow-no-mathdx`; MathDx does not ship for Tegra, which is - itself a paper datum), the hostblas + single-call-latency + fusion +3. runs the timed legs serially — the native ladder plus its fresh-input + solver companion (`tune.py --allow-no-mathdx` falls back cleanly when a + copied MathDx tree is unavailable), the hostblas + single-call-latency + fusion harnesses (host cuBLAS/cuSOLVER are on JetPack), and the robotics micro-op sweep — with `tegrastats` logging alongside for energy/solve; 4. tars captures + provenance into `bench/jetson__.tar.gz`. @@ -125,8 +126,8 @@ methodology). The script: | Capture | Feeds | |---|---| -| `mega_sweep_*.txt` | `ideal_sm87` ladder table. Full MathDx replay: `python bench/tune.py --sm 870 --legs ladder --from-ladder --from-nvt-valid `; a native-only capture needs only `--from-ladder --allow-no-mathdx`. | -| `nvt_valid_*.txt` | Required valid-input veto companion when the ladder selects NVIDIA thread; pass it with `--from-nvt-valid` during off-box regeneration. | +| `mega_sweep_*.txt` | Non-destructive rows for the `ideal_sm87` ladder table. Replay with `python bench/tune.py --sm 870 --legs ladder --from-ladder --from-solver-ladder `. If a solver capture was interrupted, omit `--from-solver-ladder` to preserve this file and recapture only its fresh-input companion. | +| `solver_ladder_*.txt` | Required symmetric fresh-input POTRF/TRSV/POSV companion. It records every native/vendor execution plan and raw paired-round sample; pass it with `--from-solver-ladder` during off-box regeneration. | | `paper_hostblas_*.txt` | Jetson columns for the hostblas + latency figures | | `paper_fusion_*.txt` | Jetson fusion curves | | `robotics_sweep_*.txt` | Jetson robotics tier panels | diff --git a/bench/RESULTS.md b/bench/RESULTS.md index 99dd42e..77e4877 100644 --- a/bench/RESULTS.md +++ b/bench/RESULTS.md @@ -18,7 +18,12 @@ labor (2026-08-11 consolidation): is current only when its source digest, environment, timing date, and proximate signed correctness receipt are recorded together. -## ladder (mega sweep — native thread/warp/block plus NVIDIA block/thread) +## ladder (archived 2026-08-30 method; superseded for the next retune) + +The counts below describe the currently shipped historical tables. New tuning +runs replace the asymmetric NVIDIA-thread confirmation with the unified +fresh-input solver ladder documented in `TUNING.md`; update this section only +after the paired captures are accepted. The shipped tables were regenerated 2026-08-30 after adding cuSOLVERDx's thread-per-problem LAPACK interface as a fifth contender. The sm_120 table uses diff --git a/bench/TUNING.md b/bench/TUNING.md index bb7bc20..1976ace 100644 --- a/bench/TUNING.md +++ b/bench/TUNING.md @@ -168,20 +168,18 @@ The ladder-grammar harnesses share one measurement core busy GPU and invalidates a leg if a foreign compute PID appears after start. Correctness stays a separate gate; a timing capture does not promote peer agreement into a numerical oracle. -- **Mutation invariant**: the main ladder's reps run with no restore in the timed region, so - in-place ops re-factor their own output from rep 2 on. This is - suitable for branch-free steady-throughput characterization, but it is not - sufficient evidence for a solver default. Every main-ladder - `nvidia_thread` winner is therefore remeasured by `bench_nvt_valid.cu` - against native thread/warp/block using a bounded ring of independent valid - systems (one per timed launch; initialization untimed). It must clear the - same 5% margin or `tune.py` vetoes it to the valid-input native winner. - Missing confirmation fails regeneration, and the confirmation leg never - promotes a vendor path. The capture records each contender's three-trial - spread; if the observed min-to-max intervals overlap the 5% decision - boundary, regeneration fails closed instead of treating the nominal minima - as a verdict. Never use the main loop alone for a `CHECK`-gated, - pivoted, or other data-dependent op. +- **Fresh-input solver invariant**: the main ladder's back-to-back launches are + authoritative only for non-destructive operations. POTRF, TRSV, and POSV + overwrite inputs, so `bench_solver_ladder.cu` replaces those rows with a + symmetric measurement of every supported native block/warp/thread and + NVIDIA block/thread launch plan. Each timed launch consumes a separate valid + system from a bounded ring; initialization is untimed. Plans are randomized + within nine paired rounds, and the capture retains every raw sample plus the + exact launch shape. Any contender may win under the ordinary 5% dependency + and ±2% SIMT tie rules—there is no special NVIDIA-thread veto. Missing solver + cells fail regeneration. Correctness remains a separate signed-receipt gate. + Never use the destructive main-ladder loop alone for a `CHECK`-gated, + pivoted, or other data-dependent operation. - **Decisions absorb residual noise**: the 5% dependency margin, the ±2% SIMT tie band, and the `noise_floor` override (sub-granularity cells refuse to resolve a margin) all live in `tune_pick.py` — measured 4× on Jetson Orin diff --git a/bench/autotune.py b/bench/autotune.py index 1ca46ec..d733f9d 100755 --- a/bench/autotune.py +++ b/bench/autotune.py @@ -27,6 +27,8 @@ Requires MATHDX_ROOT set (cuBLASDx headers needed for the cuBLASDx leg). """ +from __future__ import annotations + import argparse import concurrent.futures import datetime @@ -814,12 +816,21 @@ def emit_results_md(md_path: pathlib.Path, # ─── main ─────────────────────────────────────────────────────────────────── -def emit_defaults_table(sweep_path, out_path, sms, margin=0.05): +def emit_defaults_table(sweep_path, solver_path, out_path, sms, margin=0.05): """Parse a bench_mega_sweep run (mega_sweep_*.txt) and emit a per-host override header for glass-defaults.cuh (native plus NVIDIA block/thread ladder). Uses the NPROB=8192 throughput regime for f32 + f64.""" text = pathlib.Path(sweep_path).read_text() cells = tp.parse_mega_sweep(text, nprob=8192) + solver_cells = tp.parse_solver_ladder( + pathlib.Path(solver_path).read_text(), nprob=8192) + expected = {key for key in cells if key[1] in {"potrf", "trsv", "posv"}} + missing = sorted(expected - set(solver_cells)) + if missing: + sys.exit(f"Fresh-input solver capture is incomplete: {missing[:6]}") + for key in expected: + cells[key] = {impl: row["ns"] + for impl, row in solver_cells[key].items()} winners = {} # (native_only, dtype, op) -> {N: backend} for (dtype, op, N), measured in cells.items(): for native_only in (False, True): @@ -851,6 +862,7 @@ def emit_op(op, dtype, native_only): # GLASS_DEFAULTS_TABLE_LOCAL hook), so this fragment must NOT re-open the namespace. lines = [ f"// Generated by bench/autotune.py --emit-defaults from {pathlib.Path(sweep_path).name}", + f"// Fresh-input solver rows: {pathlib.Path(solver_path).name}", f"// (sm {sms}, NPROB=8192 throughput regime). Do not edit by hand.", "// Included by glass-defaults.cuh inside namespace glass::defaults — no namespace wrapper.", "#define GLASS_DEFAULTS_HAVE_LOCAL", @@ -928,15 +940,21 @@ def main(): p.add_argument("--emit-defaults", metavar="SWEEP_TXT", default=None, help="Parse a bench_mega_sweep run (mega_sweep_*.txt) and emit a per-host " "glass-defaults.cuh override header (native and NVIDIA ladder), then exit.") + p.add_argument("--solver-ladder", metavar="SOLVER_TXT", default=None, + help="Fresh-input solver_ladder_*.txt companion required by " + "--emit-defaults") args = p.parse_args() sms = detect_sm() if args.sm == "AUTO" else int(args.sm) hostname = platform.node().replace(" ", "_") if args.emit_defaults: + if not args.solver_ladder: + p.error("--emit-defaults requires --solver-ladder") out = args.out or str(TUNING_DIR / f"{hostname}_defaults.cuh") TUNING_DIR.mkdir(parents=True, exist_ok=True) - emit_defaults_table(args.emit_defaults, out, sms, args.margin) + emit_defaults_table(args.emit_defaults, args.solver_ladder, + out, sms, args.margin) return requested_apis = [a.strip() for a in args.apis.split(",") if a.strip()] diff --git a/bench/bench_nvt_valid.cu b/bench/bench_nvt_valid.cu deleted file mode 100644 index be15ee2..0000000 --- a/bench/bench_nvt_valid.cu +++ /dev/null @@ -1,248 +0,0 @@ -// bench_nvt_valid.cu — valid-input confirmation for NVIDIA-thread defaults. -// -// The main ladder intentionally measures many back-to-back launches between -// restores. That is a useful steady-throughput workload, but in-place solvers -// see their own output after the first launch. This companion benchmark gives -// every timed launch a distinct, valid input batch. It is a veto gate only: -// tune.py may retain an NVIDIA-thread ladder winner when it also clears the -// native thread/warp/block winner here, but this leg never promotes a vendor -// backend that did not win the main ladder. -// -// Usage: ./bench_nvt_valid [nprob=8192] [requested_slots=64] [dtype=f32|f64] - -#include -#include -#include -#include -#include -#include - -#include "timing_common.cuh" -#include "../glass-nvidia.cuh" - -#define CUDA_OK(call) do { \ - cudaError_t glass_cuda_error_ = (call); \ - if (glass_cuda_error_ != cudaSuccess) { \ - std::fprintf(stderr, "CUDA error %s at %s:%d\n", \ - cudaGetErrorString(glass_cuda_error_), __FILE__, __LINE__); \ - std::exit(2); \ - } \ -} while (0) - -enum class Op { potrf, trsv, posv }; - -static int g_nprob = 8192; - -template -__global__ void init_valid(T* A, T* b, int problems) { - const size_t matrices = static_cast(problems) * N * N; - for (size_t q = static_cast(blockIdx.x) * blockDim.x + threadIdx.x; - q < matrices; q += static_cast(gridDim.x) * blockDim.x) { - const int e = static_cast(q % (N * N)); - const int i = e % N, j = e / N; - if (i == j) { - A[q] = static_cast(N + 2); - } else { - const int lo = i < j ? i : j, hi = i < j ? j : i; - A[q] = static_cast(0.02 * (1 + ((3 * lo + 5 * hi) % 7))); - } - } - const size_t vectors = static_cast(problems) * N; - for (size_t q = static_cast(blockIdx.x) * blockDim.x + threadIdx.x; - q < vectors; q += static_cast(gridDim.x) * blockDim.x) { - b[q] = static_cast(1 + (q % N) * 0.03125); - } -} - -template -__global__ void block_kernel(Op op, T* A, T* b) { - const int p = blockIdx.x; - T* Ap = A + static_cast(p) * N * N; - T* bp = b + static_cast(p) * N; - if (op == Op::potrf) glass::block::potrf(Ap); - if (op == Op::trsv) glass::block::trsv(Ap, bp); - if (op == Op::posv) glass::block::posv(Ap, bp); -} - -template -__global__ void warp_kernel(Op op, T* A, T* b, int problems) { - const int p = blockIdx.x * blockDim.y + threadIdx.y; - if (p >= problems) return; - T* Ap = A + static_cast(p) * N * N; - T* bp = b + static_cast(p) * N; - if (op == Op::potrf) glass::warp::potrf(Ap); - if (op == Op::trsv) glass::warp::trsv(Ap, bp); - if (op == Op::posv) glass::warp::posv(Ap, bp); -} - -template -__global__ void thread_kernel(Op op, T* A, T* b, int problems) { - const int p = blockIdx.x * blockDim.x + threadIdx.x; - if (p >= problems) return; - T a[N * N], bv[N]; - T* Ap = A + static_cast(p) * N * N; - T* bp = b + static_cast(p) * N; - for (int i = 0; i < N * N; ++i) a[i] = Ap[i]; - if (op != Op::potrf) - for (int i = 0; i < N; ++i) bv[i] = bp[i]; - if (op == Op::potrf) glass::thread::potrf(a); - if (op == Op::trsv) glass::thread::trsv(a, bv); - if (op == Op::posv) glass::thread::posv(a, bv); - if (op == Op::potrf) - for (int i = 0; i < N * N; ++i) Ap[i] = a[i]; - else - for (int i = 0; i < N; ++i) bp[i] = bv[i]; -} - -template -__global__ void nvt_kernel(Op op, T* A, T* b, int problems) { - const int p = blockIdx.x * blockDim.x + threadIdx.x; - if (p >= problems) return; - T* Ap = A + static_cast(p) * N * N; - T* bp = b + static_cast(p) * N; - if (op == Op::potrf) glass::nvidia::thread::potrf(Ap); - if (op == Op::trsv) glass::nvidia::thread::trsm(T(1), Ap, bp); - if (op == Op::posv) glass::nvidia::thread::posv(Ap, bp); -} - -struct Timing { - double ns = 1e30; - double spread = 0.0; - int shape = 0; -}; - -template -static Timing time_slots(Prepare prepare, LaunchSlot launch, int slots) { - cudaGetLastError(); - prepare(); - launch(0); - if (cudaDeviceSynchronize() != cudaSuccess || - cudaGetLastError() != cudaSuccess) return {}; - - double best = 1e30, worst = 0.0; - for (int trial = 0; trial < 3; ++trial) { - prepare(); - CUDA_OK(cudaDeviceSynchronize()); - timespec t0{}, t1{}; - clock_gettime(CLOCK_MONOTONIC, &t0); - for (int slot = 0; slot < slots; ++slot) launch(slot); - CUDA_OK(cudaDeviceSynchronize()); - clock_gettime(CLOCK_MONOTONIC, &t1); - const double ns = tc_elapsed_ms(t0, t1) * 1e6 / - (static_cast(slots) * g_nprob); - best = std::min(best, ns); - worst = std::max(worst, ns); - } - return {best, (worst / best - 1.0) * 100.0, 0}; -} - -static void retain(Timing& best, Timing candidate, int shape) { - if (candidate.ns < best.ns) { - best = candidate; - best.shape = shape; - } -} - -template -static void run_case(Op op, int requested_slots) { - const size_t bytes_per_slot = static_cast(g_nprob) * - (N * N + N) * sizeof(T); - size_t free_bytes = 0, total_bytes = 0; - CUDA_OK(cudaMemGetInfo(&free_bytes, &total_bytes)); - const size_t budget = std::min(size_t(1) << 30, free_bytes / 3); - const int slots = std::max(1, std::min(requested_slots, - static_cast(budget / bytes_per_slot))); - const size_t problems = static_cast(g_nprob) * slots; - T *A = nullptr, *b = nullptr; - CUDA_OK(cudaMalloc(&A, problems * N * N * sizeof(T))); - CUDA_OK(cudaMalloc(&b, problems * N * sizeof(T))); - - auto prepare = [&] { - const size_t elements = problems * N * N; - const int blocks = std::min(4096, (elements + 255) / 256); - init_valid<<>>(A, b, static_cast(problems)); - }; - auto slot_A = [&](int slot) { - return A + static_cast(slot) * g_nprob * N * N; - }; - auto slot_b = [&](int slot) { - return b + static_cast(slot) * g_nprob * N; - }; - - constexpr int TBS[] = {32, 64, 128, 256}; - constexpr int WPBS[] = {1, 2, 4, 8, 16, 32}; - Timing block, warp, thread, nvt; - for (int tb : TBS) { - retain(block, time_slots(prepare, [&](int s) { - block_kernel<<>>(op, slot_A(s), slot_b(s)); - }, slots), tb); - retain(thread, time_slots(prepare, [&](int s) { - const int grid = (g_nprob + tb - 1) / tb; - thread_kernel<<>>(op, slot_A(s), slot_b(s), g_nprob); - }, slots), tb); - retain(nvt, time_slots(prepare, [&](int s) { - const int grid = (g_nprob + tb - 1) / tb; - nvt_kernel<<>>(op, slot_A(s), slot_b(s), g_nprob); - }, slots), tb); - } - for (int wpb : WPBS) { - retain(warp, time_slots(prepare, [&](int s) { - const int grid = (g_nprob + wpb - 1) / wpb; - warp_kernel<<>>( - op, slot_A(s), slot_b(s), g_nprob); - }, slots), wpb); - } - - const char* opname = op == Op::potrf ? "potrf" : - op == Op::trsv ? "trsv" : "posv"; - std::printf("NVT_VALID op=%s N=%d dtype=%s nprob=%d slots=%d " - "block=%.4f block_shape=%d block_spread=%.2f " - "warp=%.4f warp_shape=%d warp_spread=%.2f " - "thread=%.4f thread_shape=%d thread_spread=%.2f " - "nvidia_thread=%.4f nvt_shape=%d nvt_spread=%.2f\n", - opname, N, std::is_same_v ? "f32" : "f64", - g_nprob, slots, - block.ns, block.shape, block.spread, - warp.ns, warp.shape, warp.spread, - thread.ns, thread.shape, thread.spread, - nvt.ns, nvt.shape, nvt.spread); - CUDA_OK(cudaFree(A)); - CUDA_OK(cudaFree(b)); -} - -template -static void run_size(int requested_slots) { - run_case(Op::potrf, requested_slots); - run_case(Op::trsv, requested_slots); - run_case(Op::posv, requested_slots); -} - -template -static void run_type(int requested_slots) { - run_size(requested_slots); - run_size(requested_slots); - run_size(requested_slots); - run_size(requested_slots); - run_size(requested_slots); - run_size(requested_slots); - run_size(requested_slots); -} - -int main(int argc, char** argv) { - g_nprob = argc > 1 ? std::atoi(argv[1]) : 8192; - const int slots = argc > 2 ? std::atoi(argv[2]) : 64; - const char* dtype = argc > 3 ? argv[3] : "f32"; - if (g_nprob <= 0 || slots <= 0) { - std::fprintf(stderr, "nprob and requested_slots must be positive\n"); - return 2; - } - std::printf("# nvidia-thread valid-input confirmation | NPROB=%d " - "requested_slots=%d dtype=%s | ns/problem, min of 3\n", - g_nprob, slots, dtype); - std::printf("# each timed launch consumes one independent valid batch; " - "initialization is outside the timed region; memory cap=1GiB\n"); - tc_warm_gpu(); - if (std::strcmp(dtype, "f64") == 0) run_type(slots); - else run_type(slots); - return 0; -} diff --git a/bench/bench_paper_fusion.cu b/bench/bench_paper_fusion.cu index f943584..974b558 100644 --- a/bench/bench_paper_fusion.cu +++ b/bench/bench_paper_fusion.cu @@ -21,7 +21,7 @@ // // PROTOCOL: as bench_solvers.cu — each rep = one fused launch / one full chain // spanning all B problems, cudaEvent-bracketed; ns/problem = ms*1e6/(reps*B), -// min of 3 trials with spread reported. CORRECTNESS: fused AND chain vs host double reference on +// median of 5 trials with raw samples and spread reported. CORRECTNESS: fused AND chain vs host double reference on // problem 0 before any timing (mismatch aborts). // // Compile: nvcc -std=c++17 -arch=sm_XX -O3 --expt-relaxed-constexpr @@ -64,6 +64,11 @@ static const int NBGRID = 6; static const size_t MEM_CAP = 4ull << 30; static int REPS = 50; +static double sample_median(std::vector values) { + std::sort(values.begin(), values.end()); + return values[values.size() / 2]; +} + // ─── deterministic host RNG ────────────────────────────────────────────────── static uint64_t rng_state = 1; static void rng_seed(uint64_t s) { rng_state = s ? s : 0x9E3779B97F4A7C15ull; } @@ -311,7 +316,7 @@ static void run_shape(Handles H, const char* dt) { if (!(me < tol)) { fprintf(stderr, "FATAL chain mismatch\n"); exit(4); } } - // timing: min over 3 trials of REPS event-bracketed reps + // timing: median over 5 trials of REPS event-bracketed reps cudaEvent_t ev_s, ev_e; CK(cudaEventCreate(&ev_s)); CK(cudaEventCreate(&ev_e)); struct Row { const char* impl; std::function work; }; @@ -327,8 +332,8 @@ static void run_shape(Handles H, const char* dt) { std::shuffle(rows.begin(), rows.end(), order_rng); for (auto& r : rows) { r.work(); CK(cudaDeviceSynchronize()); // warm-up - double best_ms = 1e300, worst_ms = 0.0; - for (int trial = 0; trial < 3; trial++) { + std::vector samples; + for (int trial = 0; trial < 5; trial++) { double ms_sum = 0; for (int rep = 0; rep < REPS; rep++) { CK(cudaEventRecord(ev_s)); @@ -338,12 +343,15 @@ static void run_shape(Handles H, const char* dt) { float ms; CK(cudaEventElapsedTime(&ms, ev_s, ev_e)); ms_sum += ms; } - if (ms_sum < best_ms) best_ms = ms_sum; - if (ms_sum > worst_ms) worst_ms = ms_sum; + samples.push_back(ms_sum * 1e6 / ((double)REPS * B)); } - printf("RESULT op=riccati dtype=%s NX=%u NU=%u B=%d impl=%s ns=%.2f spread=%.2f%%\n", - dt, NX, NU, B, r.impl, best_ms * 1e6 / ((double)REPS * B), - (worst_ms / best_ms - 1.0) * 100.0); + const auto bounds = std::minmax_element(samples.begin(), samples.end()); + printf("RESULT op=riccati dtype=%s NX=%u NU=%u B=%d impl=%s ns=%.2f spread=%.2f%% samples=", + dt, NX, NU, B, r.impl, sample_median(samples), + (*bounds.second / *bounds.first - 1.0) * 100.0); + for (size_t i = 0; i < samples.size(); ++i) + printf("%s%.2f", i ? "," : "", samples[i]); + printf("\n"); fflush(stdout); } CK(cudaEventDestroy(ev_s)); CK(cudaEventDestroy(ev_e)); diff --git a/bench/bench_paper_hostblas.cu b/bench/bench_paper_hostblas.cu index 5113f8a..73a916d 100644 --- a/bench/bench_paper_hostblas.cu +++ b/bench/bench_paper_hostblas.cu @@ -20,7 +20,7 @@ // global memory; each rep = ONE launch / ONE host API chain spanning all B, // bracketed by cudaEvents; mutated buffers RESTORED from pristine device // copies OUTSIDE the event window. ns/problem = ms_sum*1e6/(reps*B), min of -// 3 trials, with trial spread reported. GPU-event timing excludes host API overhead — conservative +// 5 trials, reported as the median with every raw sample and trial spread. GPU-event timing excludes host API overhead — conservative // TOWARD the vendor (their per-call CPU cost is amortized 1/B anyway). // // LATENCY PROTOCOL (batch=1, the MPC regime): R=200 pre-initialized problem @@ -76,6 +76,13 @@ static const size_t MEM_CAP = 4ull << 30; // skip cells whose buffers exceed static const int LAT_CALLS = 200; static int REPS = 50; static double last_spread_pct = 0.0; +static std::vector last_samples; + +static double sample_median(std::vector values) { + std::sort(values.begin(), values.end()); + const size_t mid = values.size() / 2; + return values.size() % 2 ? values[mid] : (values[mid - 1] + values[mid]) * 0.5; +} // ─── deterministic host RNG (xorshift64* + Box-Muller), as bench_solvers.cu ── static uint64_t rng_state = 1; @@ -289,15 +296,15 @@ static double elapsed_us(struct timespec a, struct timespec b) { return (double)(b.tv_sec - a.tv_sec) * 1e6 + (double)(b.tv_nsec - a.tv_nsec) * 1e-3; } -// One throughput measurement: min over 3 trials of (sum over REPS of one +// One throughput measurement: median over 5 trials of (sum over REPS of one // event-bracketed `work`, with `restore` run before it OUTSIDE the events). static double time_thru_ns_per_prob(int B, const std::function& restore, const std::function& work) { cudaEvent_t s, e; CK(cudaEventCreate(&s)); CK(cudaEventCreate(&e)); restore(); work(); CK(cudaDeviceSynchronize()); // warm-up (JIT, clocks) - double best_ms = 1e300, worst_ms = 0.0; - for (int trial = 0; trial < 3; trial++) { + std::vector samples; + for (int trial = 0; trial < 5; trial++) { double ms_sum = 0; for (int rep = 0; rep < REPS; rep++) { restore(); @@ -308,12 +315,13 @@ static double time_thru_ns_per_prob(int B, const std::function& restore, float ms; CK(cudaEventElapsedTime(&ms, s, e)); ms_sum += ms; } - if (ms_sum < best_ms) best_ms = ms_sum; - if (ms_sum > worst_ms) worst_ms = ms_sum; + samples.push_back(ms_sum * 1e6 / ((double)REPS * B)); } - last_spread_pct = (worst_ms / best_ms - 1.0) * 100.0; + const auto bounds = std::minmax_element(samples.begin(), samples.end()); + last_spread_pct = (*bounds.second / *bounds.first - 1.0) * 100.0; + last_samples = samples; CK(cudaEventDestroy(s)); CK(cudaEventDestroy(e)); - return best_ms * 1e6 / ((double)REPS * B); + return sample_median(samples); } // ─── per-(dtype, N) driver ─────────────────────────────────────────────────── @@ -560,8 +568,11 @@ static void run_for_N(Handles H, const char* dt, bool do_thru, bool do_lat) { std::shuffle(rows.begin(), rows.end(), order_rng); for (auto& r : rows) { double ns = time_thru_ns_per_prob(B, r.restore, r.work); - printf("RESULT section=thru op=%s dtype=%s N=%u B=%d impl=%s ns=%.2f spread=%.2f%%\n", + printf("RESULT section=thru op=%s dtype=%s N=%u B=%d impl=%s ns=%.2f spread=%.2f%% samples=", r.op, dt, N, B, r.impl, ns, last_spread_pct); + for (size_t i = 0; i < last_samples.size(); ++i) + printf("%s%.2f", i ? "," : "", last_samples[i]); + printf("\n"); fflush(stdout); } #if defined(GLASS_BENCH_MAGMA) @@ -636,8 +647,8 @@ static void run_for_N(Handles H, const char* dt, bool do_thru, bool do_lat) { std::mt19937 latency_order_rng(0x4c41544eu ^ (N << 8) ^ (uint32_t)sizeof(T)); std::shuffle(lats.begin(), lats.end(), latency_order_rng); for (auto& L : lats) { - double best_us = 1e300, worst_us = 0.0; - for (int trial = 0; trial < 3; trial++) { + std::vector samples; + for (int trial = 0; trial < 5; trial++) { // Pristine pool per trial; restoration is outside the timed window. CK(cudaMemcpy(dS, dS0, MM*(size_t)R*sizeof(T), cudaMemcpyDeviceToDevice)); CK(cudaMemcpy(db, db0, (size_t)N*R*sizeof(T), cudaMemcpyDeviceToDevice)); @@ -648,11 +659,15 @@ static void run_for_N(Handles H, const char* dt, bool do_thru, bool do_lat) { for (int r = 10; r < R; r++) { L.call(r); CK(cudaDeviceSynchronize()); } clock_gettime(CLOCK_MONOTONIC, &t1); double us = elapsed_us(t0, t1) / (R - 10); - if (us < best_us) best_us = us; - if (us > worst_us) worst_us = us; + samples.push_back(us); } - printf("RESULT section=lat op=%s dtype=%s N=%u impl=%s us=%.3f spread=%.2f%%\n", - L.op, dt, N, L.impl, best_us, (worst_us / best_us - 1.0) * 100.0); + const auto bounds = std::minmax_element(samples.begin(), samples.end()); + printf("RESULT section=lat op=%s dtype=%s N=%u impl=%s us=%.3f spread=%.2f%% samples=", + L.op, dt, N, L.impl, sample_median(samples), + (*bounds.second / *bounds.first - 1.0) * 100.0); + for (size_t i = 0; i < samples.size(); ++i) + printf("%s%.3f", i ? "," : "", samples[i]); + printf("\n"); fflush(stdout); } CK(cudaFree(dA)); CK(cudaFree(dB)); CK(cudaFree(dC)); CK(cudaFree(dS)); diff --git a/bench/bench_solver_ladder.cu b/bench/bench_solver_ladder.cu new file mode 100644 index 0000000..97d9a26 --- /dev/null +++ b/bench/bench_solver_ladder.cu @@ -0,0 +1,385 @@ +// bench_solver_ladder.cu — authoritative fresh-input solver placement sweep. +// +// The general ladder measures back-to-back launches and is appropriate for +// non-destructive operations. POTRF, TRSV, and POSV overwrite their inputs, +// so this companion leg is authoritative for *every* solver contender: +// native block/warp/thread, NVIDIA block, and NVIDIA thread. Every timed +// launch consumes a distinct valid system from a bounded ring; initialization +// is outside the timed region. +// +// Measurements are organized into paired rounds. Each contender is measured +// once per round, contender order is shuffled deterministically within the +// round, and every raw sample is emitted. This supports both the tuner's +// robust median decision and independent/held-out paper analysis. +// +// Usage: +// ./bench_solver_ladder [nprob=8192] [requested_slots=64] +// [dtype=f32|f64] [rounds=9] [seed=1] + +// Correctness remains a separate signed-receipt gate. This timing harness +// checks launch success and supplies mathematically valid inputs; it does not +// turn timing agreement into a numerical oracle. + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "timing_common.cuh" + +#if defined(GLASS_BENCH_CUSOLVERDX) +#include "../glass-nvidia.cuh" +#define SOLVER_HAVE_NVIDIA_BLOCK 1 +#else +#include "../glass.cuh" +#define SOLVER_HAVE_NVIDIA_BLOCK 0 +#endif + +#if defined(GLASS_HAVE_CUSOLVERDX_THREAD) && GLASS_HAVE_CUSOLVERDX_THREAD +#define SOLVER_HAVE_NVIDIA_THREAD 1 +#else +#define SOLVER_HAVE_NVIDIA_THREAD 0 +#endif + +#define CUDA_OK(call) do { \ + cudaError_t glass_cuda_error_ = (call); \ + if (glass_cuda_error_ != cudaSuccess) { \ + std::fprintf(stderr, "CUDA error %s at %s:%d\n", \ + cudaGetErrorString(glass_cuda_error_), __FILE__, __LINE__); \ + std::exit(2); \ + } \ +} while (0) + +enum class Op { potrf, trsv, posv }; + +static int g_nprob = 8192; +static int g_rounds = 9; +static unsigned long long g_seed = 1; + +#if SOLVER_HAVE_NVIDIA_BLOCK +static constexpr int NV_BLOCK_THREADS = 256; +namespace glass { namespace nvidia { namespace block { + #define SOLVER_CHOL_F32(N) DEFINE_NVIDIA_CHOL_BLOCKDIM(N, NV_BLOCK_THREADS) + #define SOLVER_TRSM_F32(N) DEFINE_NVIDIA_TRSM_BLOCKDIM(N, 1, NV_BLOCK_THREADS) + #define SOLVER_POSV_F32(N) DEFINE_NVIDIA_POSV_BLOCKDIM(N, 1, NV_BLOCK_THREADS) + #define SOLVER_CHOL_F64(N) DEFINE_NVIDIA_CHOL_BLOCKDIM_PREC(N, NV_BLOCK_THREADS, double) + #define SOLVER_TRSM_F64(N) DEFINE_NVIDIA_TRSM_BLOCKDIM_PREC(N, 1, NV_BLOCK_THREADS, double) + #define SOLVER_POSV_F64(N) DEFINE_NVIDIA_POSV_BLOCKDIM_PREC(N, 1, NV_BLOCK_THREADS, double) + #define SOLVER_F32(N) SOLVER_CHOL_F32(N) SOLVER_TRSM_F32(N) SOLVER_POSV_F32(N) + #define SOLVER_F64(N) SOLVER_CHOL_F64(N) SOLVER_TRSM_F64(N) SOLVER_POSV_F64(N) + SOLVER_F32(4) SOLVER_F32(6) SOLVER_F32(8) SOLVER_F32(12) + SOLVER_F32(16) SOLVER_F32(24) SOLVER_F32(32) SOLVER_F32(48) + SOLVER_F32(64) SOLVER_F32(96) SOLVER_F32(128) + SOLVER_F64(4) SOLVER_F64(6) SOLVER_F64(8) SOLVER_F64(12) + SOLVER_F64(16) SOLVER_F64(24) SOLVER_F64(32) SOLVER_F64(48) + SOLVER_F64(64) +}}} +#endif + +template +__global__ void init_valid(T* A, T* b, int problems) { + const size_t matrices = static_cast(problems) * N * N; + for (size_t q = static_cast(blockIdx.x) * blockDim.x + threadIdx.x; + q < matrices; q += static_cast(gridDim.x) * blockDim.x) { + const int e = static_cast(q % (N * N)); + const size_t p = q / (N * N); + const int i = e % N, j = e / N; + if (i == j) { + A[q] = static_cast(N + 2 + 0.01 * (p % 5)); + } else { + const int lo = i < j ? i : j, hi = i < j ? j : i; + A[q] = static_cast(0.02 * + (1 + ((3 * lo + 5 * hi + static_cast(p % 11)) % 7))); + } + } + const size_t vectors = static_cast(problems) * N; + for (size_t q = static_cast(blockIdx.x) * blockDim.x + threadIdx.x; + q < vectors; q += static_cast(gridDim.x) * blockDim.x) { + const size_t p = q / N; + b[q] = static_cast(1 + (q % N) * 0.03125 + (p % 7) * 0.015625); + } +} + +template +__global__ void block_kernel(Op op, T* A, T* b) { + const int p = blockIdx.x; + T* Ap = A + static_cast(p) * N * N; + T* bp = b + static_cast(p) * N; + if (op == Op::potrf) glass::block::potrf(Ap); + if (op == Op::trsv) glass::block::trsv(Ap, bp); + if (op == Op::posv) glass::block::posv(Ap, bp); +} + +template +__global__ void warp_kernel(Op op, T* A, T* b, int problems) { + const int p = blockIdx.x * blockDim.y + threadIdx.y; + if (p >= problems) return; + T* Ap = A + static_cast(p) * N * N; + T* bp = b + static_cast(p) * N; + if (op == Op::potrf) glass::warp::potrf(Ap); + if (op == Op::trsv) glass::warp::trsv(Ap, bp); + if (op == Op::posv) glass::warp::posv(Ap, bp); +} + +template +__global__ void thread_kernel(Op op, T* A, T* b, int problems) { + const int p = blockIdx.x * blockDim.x + threadIdx.x; + if (p >= problems) return; + T a[N * N], bv[N]; + T* Ap = A + static_cast(p) * N * N; + T* bp = b + static_cast(p) * N; + for (int i = 0; i < N * N; ++i) a[i] = Ap[i]; + if (op != Op::potrf) + for (int i = 0; i < N; ++i) bv[i] = bp[i]; + if (op == Op::potrf) glass::thread::potrf(a); + if (op == Op::trsv) glass::thread::trsv(a, bv); + if (op == Op::posv) glass::thread::posv(a, bv); + if (op == Op::potrf) + for (int i = 0; i < N * N; ++i) Ap[i] = a[i]; + else + for (int i = 0; i < N; ++i) bp[i] = bv[i]; +} + +#if SOLVER_HAVE_NVIDIA_BLOCK +template +__global__ void nvidia_block_kernel(Op op, T* A, T* b) { + extern __shared__ char scratch[]; + const int p = blockIdx.x; + T* Ap = A + static_cast(p) * N * N; + T* bp = b + static_cast(p) * N; + if (op == Op::potrf) + glass::nvidia::block::potrf(Ap, scratch); + if (op == Op::trsv) + glass::nvidia::block::trsm(T(1), Ap, bp, scratch); + if (op == Op::posv) + glass::nvidia::block::posv(Ap, bp, scratch); +} +#endif + +#if SOLVER_HAVE_NVIDIA_THREAD +template +__global__ void nvidia_thread_kernel(Op op, T* A, T* b, int problems) { + const int p = blockIdx.x * blockDim.x + threadIdx.x; + if (p >= problems) return; + T* Ap = A + static_cast(p) * N * N; + T* bp = b + static_cast(p) * N; + if (op == Op::potrf) glass::nvidia::thread::potrf(Ap); + if (op == Op::trsv) glass::nvidia::thread::trsm(T(1), Ap, bp); + if (op == Op::posv) glass::nvidia::thread::posv(Ap, bp); +} +#endif + +struct Candidate { + std::string impl; + std::string cfg; + std::function launch; + std::vector samples; +}; + +static double median(std::vector values) { + std::sort(values.begin(), values.end()); + const size_t mid = values.size() / 2; + return values.size() % 2 ? values[mid] : (values[mid - 1] + values[mid]) * 0.5; +} + +template +static double time_once(Prepare prepare, Candidate& candidate, int slots) { + prepare(); + CUDA_OK(cudaDeviceSynchronize()); + timespec t0{}, t1{}; + clock_gettime(CLOCK_MONOTONIC, &t0); + for (int slot = 0; slot < slots; ++slot) candidate.launch(slot); + CUDA_OK(cudaDeviceSynchronize()); + clock_gettime(CLOCK_MONOTONIC, &t1); + return tc_elapsed_ms(t0, t1) * 1e6 / + (static_cast(slots) * g_nprob); +} + +template +static void run_case(Op op, int requested_slots) { + const size_t bytes_per_slot = static_cast(g_nprob) * + (N * N + N) * sizeof(T); + size_t free_bytes = 0, total_bytes = 0; + CUDA_OK(cudaMemGetInfo(&free_bytes, &total_bytes)); + const size_t budget = std::min(size_t(1) << 30, free_bytes / 3); + const int slots = std::max(1, std::min(requested_slots, + static_cast(budget / bytes_per_slot))); + const size_t problems = static_cast(g_nprob) * slots; + T *A = nullptr, *b = nullptr; + CUDA_OK(cudaMalloc(&A, problems * N * N * sizeof(T))); + CUDA_OK(cudaMalloc(&b, problems * N * sizeof(T))); + + auto prepare = [&] { + const size_t elements = problems * N * N; + const int blocks = static_cast(std::min(4096, (elements + 255) / 256)); + init_valid<<>>(A, b, static_cast(problems)); + }; + auto slot_A = [&](int slot) { + return A + static_cast(slot) * g_nprob * N * N; + }; + auto slot_b = [&](int slot) { + return b + static_cast(slot) * g_nprob * N; + }; + + std::vector candidates; + for (int tb : {32, 64, 128, 256}) { + candidates.push_back({"block", "tb" + std::to_string(tb), [=](int slot) { + block_kernel<<>>(op, slot_A(slot), slot_b(slot)); + }, {}}); + } + for (int wpb : {1, 2, 4, 8, 16, 32}) { + candidates.push_back({"warp", "w" + std::to_string(wpb), [=](int slot) { + const int grid = (g_nprob + wpb - 1) / wpb; + warp_kernel<<>>( + op, slot_A(slot), slot_b(slot), g_nprob); + }, {}}); + } + if constexpr (N <= 64) { + for (int tb : {32, 64, 128, 256}) { + candidates.push_back({"thread", "t" + std::to_string(tb), [=](int slot) { + const int grid = (g_nprob + tb - 1) / tb; + thread_kernel<<>>( + op, slot_A(slot), slot_b(slot), g_nprob); + }, {}}); + } + } + +#if SOLVER_HAVE_NVIDIA_BLOCK + if constexpr (std::is_same_v || N <= 64) { + size_t smem = 0; + if (op == Op::potrf) + smem = glass::nvidia::block::potrf_scratch_bytes(); + if (op == Op::trsv) + smem = glass::nvidia::block::trsm_scratch_bytes(); + if (op == Op::posv) + smem = glass::nvidia::block::posv_scratch_bytes(); + int device = 0, optin = 0; + CUDA_OK(cudaGetDevice(&device)); + CUDA_OK(cudaDeviceGetAttribute(&optin, cudaDevAttrMaxSharedMemoryPerBlockOptin, device)); + if (smem <= static_cast(optin) && + (smem <= 48u * 1024u || + cudaFuncSetAttribute(nvidia_block_kernel, + cudaFuncAttributeMaxDynamicSharedMemorySize, + static_cast(smem)) == cudaSuccess)) { + candidates.push_back({"nvidia", "tb256", [=](int slot) { + nvidia_block_kernel<<>>( + op, slot_A(slot), slot_b(slot)); + }, {}}); + } else { + cudaGetLastError(); + } + } +#endif + +#if SOLVER_HAVE_NVIDIA_THREAD + if constexpr (N <= 32) { + for (int tb : {32, 64, 128, 256}) { + candidates.push_back({"nvidia_thread", "t" + std::to_string(tb), [=](int slot) { + const int grid = (g_nprob + tb - 1) / tb; + nvidia_thread_kernel<<>>( + op, slot_A(slot), slot_b(slot), g_nprob); + }, {}}); + } + } +#endif + + // Probe every candidate before timing; an infeasible launch is omitted and + // cannot masquerade as an implausibly fast result. + std::vector valid; + for (auto& candidate : candidates) { + cudaGetLastError(); + prepare(); + candidate.launch(0); + cudaError_t sync = cudaDeviceSynchronize(); + cudaError_t launch = cudaGetLastError(); + if (sync == cudaSuccess && launch == cudaSuccess) { + valid.push_back(std::move(candidate)); + } else { + std::printf("SOLVER_SKIP op=%s N=%d dtype=%s nprob=%d impl=%s cfg=%s reason=launch\n", + op == Op::potrf ? "potrf" : op == Op::trsv ? "trsv" : "posv", + N, std::is_same_v ? "f32" : "f64", g_nprob, + candidate.impl.c_str(), candidate.cfg.c_str()); + cudaGetLastError(); + } + } + candidates = std::move(valid); + + std::vector order(candidates.size()); + std::iota(order.begin(), order.end(), size_t(0)); + std::mt19937_64 rng(g_seed ^ (static_cast(N) << 32) ^ + (static_cast(g_nprob) << 4) ^ + static_cast(op)); + for (int round = 0; round < g_rounds; ++round) { + std::shuffle(order.begin(), order.end(), rng); + for (size_t index : order) { + candidates[index].samples.push_back( + time_once(prepare, candidates[index], slots)); + } + } + + const char* opname = op == Op::potrf ? "potrf" : + op == Op::trsv ? "trsv" : "posv"; + for (const auto& candidate : candidates) { + const auto bounds = std::minmax_element(candidate.samples.begin(), + candidate.samples.end()); + const double med = median(candidate.samples); + const double spread = (*bounds.second / *bounds.first - 1.0) * 100.0; + std::printf("SOLVER_RESULT op=%s N=%d dtype=%s nprob=%d slots=%d " + "impl=%s cfg=%s ns=%.6f spread=%.2f samples=", + opname, N, std::is_same_v ? "f32" : "f64", + g_nprob, slots, candidate.impl.c_str(), candidate.cfg.c_str(), + med, spread); + for (size_t i = 0; i < candidate.samples.size(); ++i) + std::printf("%s%.6f", i ? "," : "", candidate.samples[i]); + std::printf("\n"); + } + + CUDA_OK(cudaFree(A)); + CUDA_OK(cudaFree(b)); +} + +template +static void run_size(int slots) { + run_case(Op::potrf, slots); + run_case(Op::trsv, slots); + run_case(Op::posv, slots); +} + +template +static void run_type(int slots) { + run_size(slots); run_size(slots); + run_size(slots); run_size(slots); + run_size(slots); run_size(slots); + run_size(slots); run_size(slots); + run_size(slots); run_size(slots); + run_size(slots); +} + +int main(int argc, char** argv) { + g_nprob = argc > 1 ? std::atoi(argv[1]) : 8192; + const int slots = argc > 2 ? std::atoi(argv[2]) : 64; + const char* dtype = argc > 3 ? argv[3] : "f32"; + g_rounds = argc > 4 ? std::atoi(argv[4]) : 9; + g_seed = argc > 5 ? std::strtoull(argv[5], nullptr, 10) : 1; + if (g_nprob <= 0 || slots <= 0 || g_rounds < 3) { + std::fprintf(stderr, "nprob/slots must be positive and rounds >= 3\n"); + return 2; + } + std::printf("# fresh-input solver ladder | NPROB=%d requested_slots=%d " + "dtype=%s rounds=%d seed=%llu | median ns/problem\n", + g_nprob, slots, dtype, g_rounds, g_seed); + std::printf("# paired randomized rounds; every launch consumes a distinct valid batch; " + "initialization is outside timing; raw round samples follow each row\n"); + tc_warm_gpu(); + if (std::strcmp(dtype, "f64") == 0) run_type(slots); + else run_type(slots); + return 0; +} diff --git a/bench/export_sweep_figures.py b/bench/export_sweep_figures.py index bc05f7c..eea66b6 100644 --- a/bench/export_sweep_figures.py +++ b/bench/export_sweep_figures.py @@ -1,7 +1,8 @@ #!/usr/bin/env python3 """Render the native/NVIDIA execution-scope sweep into static docs assets. -Reads a ``bench/mega_sweep_*.txt`` run (the same data behind +Reads a ``bench/mega_sweep_*.txt`` run plus its symmetric fresh-input +``bench/solver_ladder_*.txt`` companion (the same combined data behind ``glass-defaults.cuh``'s ``recommend<>()``) and writes, into ``docs/source/_static/``: @@ -16,7 +17,8 @@ build time. Regenerate after a fresh sweep (``bench/tune.py`` runs this as its figures leg, or run it standalone):: - python bench/export_sweep_figures.py bench/mega_sweep_*.txt + python bench/export_sweep_figures.py bench/mega_sweep_*.txt \ + --solver bench/solver_ladder_*.txt This is the script form of ``bench/explore_sweep.ipynb`` (kept as the interactive explorer). Needs numpy + matplotlib. @@ -79,6 +81,24 @@ def parse(text, regimes=REGIMES): return data +def overlay_solver(data, text): + """Replace destructive main-ladder rows with fresh-input measurements.""" + for nprob in regimes_present(data): + rows = tp.parse_solver_ladder(text, nprob) + expected = {(dt, op, N) for (np, dt, op, N) in data + if np == nprob and op in {"potrf", "trsv", "posv"}} + missing = expected - set(rows) + if missing: + preview = ", ".join(f"{dt}/{op}/N={N}" + for dt, op, N in sorted(missing)[:6]) + raise ValueError(f"solver capture missing NPROB={nprob}: {preview}") + for dt, op, N in expected: + data[(nprob, dt, op, N)] = { + impl: metadata["ns"] for impl, metadata in rows[(dt, op, N)].items() + } + return data + + def regimes_present(data): return sorted({np for (np, _d, _o, _N) in data}) @@ -150,6 +170,7 @@ def main(): ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) ap.add_argument("sweep", nargs="?", help="path to a mega_sweep_*.txt (default: latest in bench/)") + ap.add_argument("--solver", help="matching solver_ladder_*.txt (default: latest in bench/)") ap.add_argument("--out", default=default_static, help="output dir (default: docs/source/_static)") args = ap.parse_args() @@ -161,7 +182,16 @@ def main(): sweep = cands[-1] print("sweep file:", os.path.relpath(sweep, repo)) - data = parse(open(sweep).read()) + solver = args.solver + if not solver: + cands = sorted(glob.glob(os.path.join(here, "solver_ladder_*.txt"))) + if not cands: + sys.exit("no solver_ladder_*.txt found — destructive solver rows " + "cannot be plotted from the main ladder") + solver = cands[-1] + print("solver file:", os.path.relpath(solver, repo)) + + data = overlay_solver(parse(open(sweep).read()), open(solver).read()) present = regimes_present(data) print("parsed", len(data), "cells across NPROB", present) os.makedirs(args.out, exist_ok=True) diff --git a/bench/run_bench.py b/bench/run_bench.py index bd60baa..488e25e 100644 --- a/bench/run_bench.py +++ b/bench/run_bench.py @@ -13,6 +13,8 @@ python3 bench/run_bench.py [--iters N] [--no-cublasdx] """ +from __future__ import annotations + import argparse import hashlib import json diff --git a/bench/run_jetson.sh b/bench/run_jetson.sh index fe6cbff..1027c45 100755 --- a/bench/run_jetson.sh +++ b/bench/run_jetson.sh @@ -198,7 +198,7 @@ run_timed_legs() { # freshest of each into DEST (mv, so the next mode can't pick up stale files; # the sentinel guards against packaging captures committed in the repo). local pat latest - for pat in mega_sweep_ nvt_valid_ paper_hostblas_ paper_fusion_; do + for pat in mega_sweep_ solver_ladder_ paper_hostblas_ paper_fusion_; do latest=$(ls -t ${pat}*.txt 2>/dev/null | head -1) if [[ -n "$latest" && "$latest" -nt "$DEST/.legs_start" ]]; then mv "$latest" "$DEST/" diff --git a/bench/run_quiet_audit.sh b/bench/run_quiet_audit.sh index e41c015..82aad0a 100755 --- a/bench/run_quiet_audit.sh +++ b/bench/run_quiet_audit.sh @@ -72,7 +72,7 @@ one_new_capture() { } ladder="$(one_new_capture 'mega_sweep_*.txt')" -nvt_valid="$(one_new_capture 'nvt_valid_*.txt')" +solver_ladder="$(one_new_capture 'solver_ladder_*.txt')" body="$(one_new_capture 'body_dispatch_sweep_*.txt')" reduced="$(one_new_capture 'reduced_sweep_*.txt')" blas2="$(one_new_capture 'blas2_sweep_*.txt')" @@ -86,7 +86,7 @@ echo "── offline regeneration from immutable captures ─────── python3 bench/tune.py --sm "$SMS" \ --legs ladder,body,reduced,blas2,rect,solvers,figures \ --from-ladder "$ladder" \ - --from-nvt-valid "$nvt_valid" \ + --from-solver-ladder "$solver_ladder" \ --from-body "$body" \ --from-reduced "$reduced" \ --from-blas2 "$blas2" \ diff --git a/bench/timing_common.cuh b/bench/timing_common.cuh index ca8dc6d..680b037 100644 --- a/bench/timing_common.cuh +++ b/bench/timing_common.cuh @@ -14,8 +14,8 @@ // ops (potrf/ldlt/…) re-process their own output from rep 2 on. That is // a steady-throughput characterization ONLY for branch-free ops whose // control flow is data-independent. It is not sufficient evidence for an -// in-place solver default: NVIDIA-thread ladder winners also pass the -// independent-valid-batch veto in bench_nvt_valid.cu. Do NOT time a +// in-place solver default: POTRF/TRSV/POSV defaults come instead from the +// symmetric fresh-input sweep in bench_solver_ladder.cu. Do NOT time a // CHECK-gated or pivoted op through this loop — its data-dependent branches // would time garbage. #pragma once diff --git a/bench/tune.py b/bench/tune.py index 27493ab..241918b 100755 --- a/bench/tune.py +++ b/bench/tune.py @@ -11,7 +11,7 @@ block), so no table bakes sub-noise jitter and a pure-noise re-run reproduces the same tables. The legs: - ladder bench_mega_sweep.cu + bench_nvt_valid.cu + ladder bench_mega_sweep.cu + bench_solver_ladder.cu → native/NVIDIA thread/warp/block ladder in glass-defaults.cuh (native thread — one problem/thread — is a dependency-free contender alongside warp/block: @@ -19,8 +19,9 @@ with ties inside the ±2% SIMT band resolving to the simpler tier — so a fresh sweep emits `backend::thread` wherever the low-DOF packing - actually wins; every NVIDIA-thread pick must - also pass the independent-valid-batch veto) + actually wins; POTRF/TRSV/POSV are replaced by + a symmetric fresh-valid-input sweep of every + native and NVIDIA contender) (paired per-arch MathDx/native-only tables + the SM dispatch switch; a first-time arch — e.g. sm_87 on a Jetson Orin — gets a new table + case, @@ -68,6 +69,7 @@ import re import subprocess import sys +import tempfile import time import tune_pick as tp @@ -154,10 +156,17 @@ def run_isolated(argv, force=False): # them so the mid-run check catches only NEW processes (previously the # very PIDs --force accepted re-tripped it, making the flag self-defeating). baseline = compute_pids() if force else frozenset() - proc = subprocess.Popen([str(x) for x in argv], cwd=BENCH_DIR, text=True, - stdout=subprocess.PIPE, stderr=subprocess.STDOUT) - _, foreign = watch_process(proc, baseline) - stdout, _ = proc.communicate() + # watch_process() polls the child before communicate() is called. A PIPE + # therefore deadlocks once a verbose harness (notably the raw-sample solver + # ladder) fills the kernel pipe buffer. A seekable temporary file retains + # the same all-or-nothing capture semantics without requiring a second + # reader thread or buffering output in the child. + with tempfile.TemporaryFile(mode="w+", encoding="utf-8") as capture: + proc = subprocess.Popen([str(x) for x in argv], cwd=BENCH_DIR, text=True, + stdout=capture, stderr=subprocess.STDOUT) + _, foreign = watch_process(proc, baseline) + capture.seek(0) + stdout = capture.read() if foreign: sys.exit(f"ERROR: timing invalidated; foreign compute PIDs appeared: " f"{sorted(foreign)}") @@ -255,24 +264,24 @@ def build_mega_sweep(sms, mdx, allow_no_mathdx=False): return binp -def _fatbin_build_nvt_valid(sms, mdx): - """Build the small valid-input confirmation harness on non-x86 hosts.""" +def _fatbin_build_solver_ladder(sms, mdx): + """Build the fresh-input solver ladder on non-x86 MathDx hosts.""" common = ["-std=c++17", f"-arch=sm_{sms // 10}", "-O3", "--expt-relaxed-constexpr", "-Xptxas", "-O1", "-I..", "-I../src", f"-I{mdx/'include'}", f"-I{mdx/'external'/'cutlass'/'include'}", "-DGLASS_BENCH_CUSOLVERDX", f"-DGLASS_TARGET_SM={sms}", "-DCUSOLVERDX_IGNORE_NVBUG_5288270_ASSERT"] - src = (BENCH_DIR / "bench_nvt_valid.cu").read_bytes() + src = (BENCH_DIR / "bench_solver_ladder.cu").read_bytes() key = hashlib.sha256(src + lib_digest().encode() + " ".join(common + ["fatbin"]).encode()).hexdigest()[:12] - binp = cache_dir(sms) / f"nvt_valid_fatbin_{key}" + binp = cache_dir(sms) / f"solver_ladder_fatbin_{key}" if binp.exists(): return binp, "cached" obj, dlk = str(binp) + ".o", str(binp) + "_dlink.o" steps = [ ["nvcc"] + common + ["-rdc=true", "-dlto", "-dc", - "bench_nvt_valid.cu", "-o", obj], + "bench_solver_ladder.cu", "-o", obj], ["nvcc", f"-arch=sm_{sms // 10}", "-dlto", "-dlink", obj, str(mdx / "lib" / "libcusolverdx.fatbin"), "-o", dlk], ["nvcc", f"-arch=sm_{sms // 10}", obj, dlk, @@ -284,11 +293,16 @@ def _fatbin_build_nvt_valid(sms, mdx): return binp, "built" -def build_nvt_valid(sms, mdx): +def build_solver_ladder(sms, mdx): if mdx is None: - sys.exit("ERROR: NVIDIA-thread confirmation needs MATHDX_ROOT.") - if platform.machine() != "x86_64": - binp, status = _fatbin_build_nvt_valid(sms, mdx) + print(" bench_solver_ladder: MathDx absent -> native contenders only") + flags = ["nvcc", "-std=c++17", f"-arch=sm_{sms // 10}", "-O3", + "--expt-relaxed-constexpr", "-Xptxas", "-O1", "-I..", "-I../src", + f"-DGLASS_TARGET_SM={sms}", "bench_solver_ladder.cu"] + binp, status = cached_build("solver_ladder_simt", + "bench_solver_ladder.cu", flags, sms) + elif platform.machine() != "x86_64": + binp, status = _fatbin_build_solver_ladder(sms, mdx) else: flags = ["nvcc", "-std=c++17", f"-arch=sm_{sms // 10}", "-O3", "--expt-relaxed-constexpr", "-Xptxas", "-O1", "-I..", "-I../src", @@ -297,26 +311,43 @@ def build_nvt_valid(sms, mdx): f"-DGLASS_TARGET_SM={sms}", "-DCUSOLVERDX_IGNORE_NVBUG_5288270_ASSERT", "-rdc=true", "-dlto", f"-L{mdx/'lib'}", "-lcusolverdx", "-lcublas", "-lcusolver", "-lcudart", - "bench_nvt_valid.cu"] - binp, status = cached_build("nvt_valid", "bench_nvt_valid.cu", flags, sms) + "bench_solver_ladder.cu"] + binp, status = cached_build("solver_ladder", "bench_solver_ladder.cu", + flags, sms) if status == "fail": - sys.exit("ERROR: bench_nvt_valid compile failed.") - print(f" bench_nvt_valid: {status} ({binp.name})") + sys.exit("ERROR: bench_solver_ladder compile failed.") + print(f" bench_solver_ladder: {status} ({binp.name})") return binp -def run_nvt_valid(binp, sms, margin, force=False): - """Capture both precisions at the ladder's NPROB=8192 policy point.""" - path = BENCH_DIR / f"nvt_valid_{time.strftime('%Y%m%d_%H%M%S')}.txt" - lines = provenance("nvt_valid", sms, margin) - lines += [f"# nvt_valid {time.strftime('%c')} (bench/tune.py)", ""] +def run_solver_ladder(binp, quick, sms, margin, sched=None, force=False, + rounds=9, seed=1): + """Capture every solver contender on fresh valid inputs. + + Input generation/restoration is outside the timed region. Implementations + and launch shapes are randomized within each paired round; all raw samples + are retained so table generation and paper analysis need not reuse a + min-of-three summary. + """ + path = BENCH_DIR / f"solver_ladder_{time.strftime('%Y%m%d_%H%M%S')}.txt" + lines = provenance("solver_ladder", sms, margin) + lines += [f"# solver_ladder {time.strftime('%c')} (bench/tune.py)", + f"# paired_rounds={rounds} seed={seed} inputs=fresh_valid", ""] path.write_text("\n".join(lines) + "\n") - for dtype in ("f32", "f64"): - print(f" -> nvt_valid NPROB=8192 requested_slots=64 {dtype}") - stdout = run_isolated([str(binp), "8192", "64", dtype], force) - with open(path, "a") as stream: - stream.write(stdout) - stream.write("\n") + if sched is None: + sched = _QUICK_SCHED if quick else _FULL_SCHED + slots = {64: 2048, 1024: 256, 8192: 64} + for nprob_text, _ in sched: + nprob = int(nprob_text) + requested_slots = slots.get(nprob, max(8, min(2048, 131072 // nprob))) + for dtype in ("f32", "f64"): + print(f" -> solver_ladder NPROB={nprob} slots={requested_slots} " + f"rounds={rounds} {dtype}") + stdout = run_isolated( + [str(binp), nprob, requested_slots, dtype, rounds, seed], force) + with open(path, "a") as stream: + stream.write(stdout) + stream.write("\n") print(f"==> wrote {path.relative_to(GLASS_DIR)}") return path @@ -411,7 +442,7 @@ def warn_jittery_rows(text, margin, label): return len(jittery) -def winners_from_sweep(text, margin, native_only=False): +def winners_from_sweep(text, margin, native_only=False, solver_text=None): """(dtype, op) -> {N: backend} under the shared dependency margin. ``native_only`` removes both vendor measurements before applying the same @@ -419,6 +450,17 @@ def winners_from_sweep(text, margin, native_only=False): of approximating vendor cells with a size heuristic. """ cells = tp.parse_mega_sweep(text, nprob=8192) + if solver_text is not None: + solver_cells = tp.parse_solver_ladder(solver_text, nprob=8192) + expected = {key for key in cells if key[1] in {"potrf", "trsv", "posv"}} + missing = sorted(expected - set(solver_cells)) + if missing: + summary = ", ".join(f"{dt}/{op}/N={N}" for dt, op, N in missing[:6]) + sys.exit("ERROR: fresh-input solver capture is incomplete at " + f"NPROB=8192 ({summary}{' ...' if len(missing) > 6 else ''}).") + for key in expected: + cells[key] = {impl: row["ns"] + for impl, row in solver_cells[key].items()} winners = {} for (dt, op, N), times in cells.items(): if native_only: @@ -430,47 +472,26 @@ def winners_from_sweep(text, margin, native_only=False): return winners -def apply_nvt_valid_veto(winners, confirmation_text, margin): - """Veto NVIDIA-thread picks that do not reproduce on independent inputs. - - The confirmation leg is deliberately asymmetric: it may replace a main- - ladder NVIDIA-thread winner with the valid-input native winner, but it does - not promote NVIDIA-thread into any cell. Every selected NVIDIA-thread cell - must have an exact confirmation row; incomplete evidence is a hard error. - Returns ``(updated_winners, veto_count)``. - """ - confirmed = tp.parse_nvt_valid(confirmation_text, nprob=8192) - spreads = tp.parse_nvt_valid_spreads(confirmation_text, nprob=8192) - updated = {key: dict(value) for key, value in winners.items()} - vetoes = 0 - for (dtype, op), cells in updated.items(): - for N, winner in list(cells.items()): - if winner != "nvidia_thread": +def require_stable_solver_picks(solver_text, winners, native_winners, margin): + """Fail closed when an emitted solver plan is noisy at decision scale.""" + cells = tp.parse_solver_ladder(solver_text, nprob=8192) + unstable = [] + for policy_name, policy in (("full", winners), ("native", native_winners)): + for (dtype, op), sizes in policy.items(): + if op not in {"potrf", "trsv", "posv"}: continue - key = (dtype, op, N) - if key not in confirmed: - sys.exit("ERROR: NVIDIA-thread ladder winner lacks valid-input " - f"confirmation: {dtype} {op} N={N}.") - times = confirmed[key] - native_names = ("thread", "warp", "block") - raw_native = min(native_names, key=lambda name: times[name]) - native_lo = times[raw_native] - native_hi = native_lo * (1.0 + spreads[key][raw_native] / 100.0) - nvt_lo = times["nvidia_thread"] - nvt_hi = nvt_lo * (1.0 + spreads[key]["nvidia_thread"] / 100.0) - guaranteed_win = nvt_hi < native_lo * (1.0 - margin) - guaranteed_loss = nvt_lo >= native_hi * (1.0 - margin) - if not guaranteed_win and not guaranteed_loss: - sys.exit("ERROR: valid-input confirmation cannot resolve the " - f"±{margin*100:.0f}% margin for {dtype} {op} N={N}: " - f"native {raw_native} interval=[{native_lo:.4f}," - f"{native_hi:.4f}], NVIDIA thread interval=" - f"[{nvt_lo:.4f},{nvt_hi:.4f}].") - if guaranteed_loss: - native_times = {name: times[name] for name in native_names} - cells[N] = tp.pick(native_times, margin) - vetoes += 1 - return updated, vetoes + for N, impl in sizes.items(): + row = cells[(dtype, op, N)][impl] + if row["spread"] > margin * 100.0: + unstable.append((policy_name, dtype, op, N, impl, + row["cfg"], row["spread"])) + if unstable: + detail = "; ".join( + f"{policy} {dtype}/{op}/N={N} {impl}/{cfg} spread={spread:.2f}%" + for policy, dtype, op, N, impl, cfg, spread in unstable[:8]) + sys.exit("ERROR: selected fresh-input solver plan has round spread " + f"above the ±{margin*100:.0f}% decision margin: {detail}. " + "Recapture on a quieter GPU.") def emit_ideal_body(winners, fname, ops=tp.LADDER_OPS): @@ -494,33 +515,29 @@ def emit_ideal_body(winners, fname, ops=tp.LADDER_OPS): def regen_ladder(sweep_text, margin, src_name, sms, - nvt_valid_text=None, nvt_valid_name=None): + solver_text=None, solver_name=None): """Regenerate this arch's table block in glass-defaults.cuh (replace if the arch was swept before, insert after the last table block if it's new) and rebuild the SM dispatch case-list from every table block present.""" arch = sms // 10 warn_jittery_rows(sweep_text, margin, "ladder") - winners = winners_from_sweep(sweep_text, margin) - native_winners = winners_from_sweep(sweep_text, margin, native_only=True) + if solver_text is None: + sys.exit("ERROR: ladder regeneration requires a symmetric fresh-input " + "solver capture; pass --from-solver-ladder or run online.") + winners = winners_from_sweep(sweep_text, margin, solver_text=solver_text) + native_winners = winners_from_sweep( + sweep_text, margin, native_only=True, solver_text=solver_text) if not winners: sys.exit("ERROR: no NPROB=8192 verdicts parsed from the sweep.") - has_nvt = any(backend == "nvidia_thread" - for cells in winners.values() for backend in cells.values()) - if has_nvt and nvt_valid_text is None: - sys.exit("ERROR: this ladder selects NVIDIA-thread but no independent-" - "valid-input capture was supplied; pass --from-nvt-valid or " - "run the ladder online.") - vetoes = 0 - if has_nvt: - winners, vetoes = apply_nvt_valid_veto(winners, nvt_valid_text, margin) + require_stable_solver_picks(solver_text, winners, native_winners, margin) + solver_rows = tp.parse_solver_configs(solver_text, nprob=8192) begin, end = _LAD_BEGIN.format(a=arch), _LAD_END.format(a=arch) region = "\n".join([ begin, f"// Source sweep: {src_name} tie margin: ±{margin*100:.0f}% " "(NVIDIA block/thread must clear it; SIMT ties ±2% prefer thread>warp>block)", - *([f"// NVIDIA-thread valid-input veto: {nvt_valid_name} " - f"({vetoes} ladder pick{'s' if vetoes != 1 else ''} vetoed)"] - if nvt_valid_name else []), + f"// Fresh-input solver sweep: {solver_name} " + f"({len(solver_rows)} measured execution plans; symmetric selection)", "// Paired tables preserve the measured native runner-up for callers that", "// do not opt into MathDx; both use the same capture and SIMT tie rule.", emit_ideal_body(winners, f"ideal_sm{arch}"), @@ -549,7 +566,7 @@ def regen_ladder(sweep_text, margin, src_name, sms, pre, _, rest = text.partition(_DIS_BEGIN) _, _, post = rest.partition(_DIS_END) text = pre + _DIS_BEGIN + "\n" + cases + " " + _DIS_END + post - return text, len(winners), vetoes + return text, len(winners), len(solver_rows) # ─── blas2 + rect header tables (glass-defaults.cuh; warp-vs-block only) ───── @@ -1232,6 +1249,11 @@ def main(): "an 8192 section, the regenerated tables read it") p.add_argument("--quick", action="store_true", help="ladder: throughput point only (NPROB=8192), fewer reps") + p.add_argument("--solver-rounds", type=int, default=9, + help="paired rounds per fresh-input solver plan (default 9)") + p.add_argument("--solver-seed", type=int, default=1, + help="candidate-order seed for the solver ladder; use a " + "different seed for held-out Capture B") p.add_argument("--prebuild", action="store_true", help="compile every binary the selected legs need into the " "build cache and exit — no timing. Run this ANYTIME (even " @@ -1253,10 +1275,12 @@ def main(): help="allow timed runs despite a busy-GPU preflight; foreign " "compute PIDs appearing during a leg still invalidate it") p.add_argument("--from-ladder", metavar="TXT", - help="skip ladder build/run; regenerate from this mega_sweep .txt") - p.add_argument("--from-nvt-valid", metavar="TXT", - help="independent-valid-input companion capture required when " - "--from-ladder selects NVIDIA-thread") + help="reuse this mega_sweep .txt; with --from-solver-ladder, " + "regenerate fully offline, otherwise capture only the " + "missing fresh-input solver companion") + p.add_argument("--from-solver-ladder", metavar="TXT", + help="symmetric fresh-valid-input POTRF/TRSV/POSV companion " + "capture required with --from-ladder") p.add_argument("--from-body", metavar="TXT", help="skip body build/run; regenerate dispatch_body() from " "this body_dispatch_sweep .txt") @@ -1269,6 +1293,10 @@ def main(): p.add_argument("--from-solvers", metavar="TXT", help="skip solvers build/run; report from this bench_solvers sweep .txt") args = p.parse_args() + if args.solver_rounds < 3: + p.error("--solver-rounds must be at least 3") + if args.from_solver_ladder and not args.from_ladder: + p.error("--from-solver-ladder is a companion to --from-ladder") user_sched = None if args.sched: try: @@ -1282,7 +1310,7 @@ def main(): bad = [l for l in legs if l not in ALL_LEGS] if bad: sys.exit(f"unknown leg(s) {bad}; choose from {ALL_LEGS}") - offline = bool(args.from_ladder or args.from_nvt_valid or args.from_body or args.from_reduced + offline = bool(args.from_ladder or args.from_solver_ladder or args.from_body or args.from_reduced or args.from_blas2 or args.from_rect or args.from_solvers) sms = None if (args.sm == "auto" and offline) else ( detect_sm() if args.sm == "auto" else int(args.sm)) @@ -1303,8 +1331,7 @@ def main(): if "ladder" in legs: print("── prebuild: ladder ──────────────────────────────────────") build_mega_sweep(sms, mdx, args.allow_no_mathdx) - if mdx is not None: - build_nvt_valid(sms, mdx) + build_solver_ladder(sms, mdx) if "body" in legs: print("── prebuild: body ────────────────────────────────────────") build_body(sms) @@ -1348,26 +1375,32 @@ def main(): "regenerated on the desktop).") sweep_path = pathlib.Path(args.from_ladder) sweep_text = sweep_path.read_text() - nvt_valid_path = (pathlib.Path(args.from_nvt_valid) - if args.from_nvt_valid else None) + if args.from_solver_ladder: + solver_path = pathlib.Path(args.from_solver_ladder) + else: + solver_bin = build_solver_ladder(sms, mdx) + solver_path = run_solver_ladder( + solver_bin, args.quick, sms, args.margin, sched=user_sched, + force=args.force, rounds=args.solver_rounds, + seed=args.solver_seed) else: binp = build_mega_sweep(sms, mdx, args.allow_no_mathdx) sweep_path = run_mega_sweep( binp, args.quick, sms, args.margin, sched=user_sched, force=args.force) sweep_text = sweep_path.read_text() - nvt_valid_path = None - if mdx is not None: - nvt_bin = build_nvt_valid(sms, mdx) - nvt_valid_path = run_nvt_valid(nvt_bin, sms, args.margin, - force=args.force) - nvt_valid_text = (nvt_valid_path.read_text() - if nvt_valid_path is not None else None) - new_defaults, n, vetoes = regen_ladder( - sweep_text, args.margin, sweep_path.name, sms, nvt_valid_text, - nvt_valid_path.name if nvt_valid_path is not None else None) + solver_bin = build_solver_ladder(sms, mdx) + solver_path = run_solver_ladder( + solver_bin, args.quick, sms, args.margin, sched=user_sched, + force=args.force, rounds=args.solver_rounds, + seed=args.solver_seed) + solver_text = (solver_path.read_text() + if solver_path is not None else None) + new_defaults, n, solver_plans = regen_ladder( + sweep_text, args.margin, sweep_path.name, sms, solver_text, + solver_path.name if solver_path is not None else None) print(f" regenerated ideal_sm{sms // 10} from {n} (dtype,op) groups; " - f"valid-input vetoes={vetoes}") + f"fresh-input solver plans={solver_plans}") if args.dry_run: changed["ladder"] = show_diff(DEFAULTS, new_defaults, "glass-defaults.cuh") else: @@ -1535,12 +1568,22 @@ def main(): if not sweep_for_fig: cands = sorted(glob.glob(str(BENCH_DIR / "mega_sweep_*.txt"))) sweep_for_fig = cands[-1] if cands else None + solver_for_fig = (str(pathlib.Path(args.from_solver_ladder).resolve()) + if args.from_solver_ladder else None) + if sweep_for_fig and not solver_for_fig: + cands = sorted(glob.glob(str(BENCH_DIR / "solver_ladder_*.txt"))) + solver_for_fig = cands[-1] if cands else None if not sweep_for_fig: print(" [skip] no mega_sweep_*.txt to plot.") + elif not solver_for_fig: + print(" [skip] no solver_ladder_*.txt companion to plot destructive rows.") elif args.dry_run: - print(f" [dry-run] would render figures from {pathlib.Path(sweep_for_fig).name}") + print(f" [dry-run] would render figures from " + f"{pathlib.Path(sweep_for_fig).name} + " + f"{pathlib.Path(solver_for_fig).name}") else: - r = run([sys.executable, "export_sweep_figures.py", sweep_for_fig], cwd=BENCH_DIR) + r = run([sys.executable, "export_sweep_figures.py", sweep_for_fig, + "--solver", solver_for_fig], cwd=BENCH_DIR) if r.returncode != 0: print(" ⚠️ figures leg failed; inspect the renderer error above " "(matplotlib is one possible missing dependency). Tables are unaffected.") diff --git a/bench/tune_pick.py b/bench/tune_pick.py index ea6271a..1516606 100755 --- a/bench/tune_pick.py +++ b/bench/tune_pick.py @@ -173,49 +173,60 @@ def parse_mega_sweep(text, nprob=8192): return data -_NVT_VALID_RE = re.compile( - r"^NVT_VALID\s+op=(potrf|trsv|posv)\s+N=(\d+)\s+" +_SOLVER_RESULT_RE = re.compile( + r"^SOLVER_RESULT\s+op=(potrf|trsv|posv)\s+N=(\d+)\s+" r"dtype=(f32|f64)\s+nprob=(\d+)\s+slots=(\d+)\s+" - r"block=([\d.]+)\s+block_shape=(\d+)\s+block_spread=([\d.]+)\s+" - r"warp=([\d.]+)\s+warp_shape=(\d+)\s+warp_spread=([\d.]+)\s+" - r"thread=([\d.]+)\s+thread_shape=(\d+)\s+thread_spread=([\d.]+)\s+" - r"nvidia_thread=([\d.]+)\s+nvt_shape=(\d+)\s+nvt_spread=([\d.]+)$") + r"impl=(block|warp|thread|nvidia|nvidia_thread)\s+" + r"cfg=([a-z]+\d+)\s+ns=([\d.]+)\s+spread=([\d.]+)\s+" + r"samples=([\d.,]+)$") -def parse_nvt_valid(text, nprob=8192): - """Parse the independent-valid-batch NVIDIA-thread confirmation leg. +def parse_solver_configs(text, nprob=8192): + """Parse the unified fresh-input solver capture at ``nprob``. - Returns ``(dtype, op, N) -> {block, warp, thread, nvidia_thread}``. - Launch-shape and trial-spread metadata remain in the capture for audit; - table generation deliberately consumes only the measured times. + Returns ``(dtype, op, N, impl, cfg) -> metadata`` where metadata contains + the median ``ns``/problem, percent ``spread``, raw paired-round ``samples``, + and realized ring-buffer ``slots``. Keeping every launch configuration + (rather than just a tier summary) is what lets downstream analyses transfer + an exact execution plan across architectures without silently retuning its + shape on the target. """ data = {} for line in text.splitlines(): - m = _NVT_VALID_RE.match(line.strip()) + m = _SOLVER_RESULT_RE.match(line.strip()) if not m or int(m.group(4)) != nprob: continue - data[(m.group(3), m.group(1), int(m.group(2)))] = { - "block": float(m.group(6)), - "warp": float(m.group(9)), - "thread": float(m.group(12)), - "nvidia_thread": float(m.group(15)), + samples = tuple(float(value) for value in m.group(10).split(",")) + if len(samples) < 3: + raise ValueError("SOLVER_RESULT needs at least three raw rounds: " + + line.strip()) + key = (m.group(3), m.group(1), int(m.group(2)), + m.group(6), m.group(7)) + if key in data: + raise ValueError(f"duplicate SOLVER_RESULT plan: {key}") + data[key] = { + "ns": float(m.group(8)), + "spread": float(m.group(9)), + "samples": samples, + "slots": int(m.group(5)), } return data -def parse_nvt_valid_spreads(text, nprob=8192): - """Return per-contender trial spreads for valid-input confirmation rows.""" +def parse_solver_ladder(text, nprob=8192): + """Return the best measured launch shape for each solver implementation. + + Result keys are ``(dtype, op, N)``. Each value maps an implementation to + ``{"ns", "cfg", "spread", "samples", "slots"}``. The implementation + winner is selected later by :func:`pick`, under the same dependency and + SIMT tie margins as the non-destructive ladder. + """ + rows = parse_solver_configs(text, nprob) data = {} - for line in text.splitlines(): - m = _NVT_VALID_RE.match(line.strip()) - if not m or int(m.group(4)) != nprob: - continue - data[(m.group(3), m.group(1), int(m.group(2)))] = { - "block": float(m.group(8)), - "warp": float(m.group(11)), - "thread": float(m.group(14)), - "nvidia_thread": float(m.group(17)), - } + for (dtype, op, N, impl, cfg), metadata in rows.items(): + cell = data.setdefault((dtype, op, N), {}) + if impl not in cell or metadata["ns"] < cell[impl]["ns"]: + cell[impl] = {**metadata, "cfg": cfg} return data diff --git a/docs/source/user_guide/concepts/tuning.rst b/docs/source/user_guide/concepts/tuning.rst index 79a897e..a7cd495 100644 --- a/docs/source/user_guide/concepts/tuning.rst +++ b/docs/source/user_guide/concepts/tuning.rst @@ -122,17 +122,18 @@ the current sm_120 and sm_87 tables select NVIDIA thread for some small the remaining bands. See :doc:`../tutorials/sweep_results` and ``bench/RESULTS.md`` for the dated per-op × per-precision results. -In-place solver timing has one additional gate. The main ladder measures -back-to-back throughput and restores inputs once per trial; after its first -launch, an in-place solver therefore consumes its own output. Whenever that -ladder selects NVIDIA thread, ``bench_nvt_valid.cu`` remeasures native -thread/warp/block and NVIDIA thread with a ring of independent valid systems, -one per timed launch. NVIDIA thread must clear the same 5% margin there or the -table falls back to the valid-input native winner. This companion leg is a -veto only: it cannot promote a vendor path the main ladder did not select, and -missing confirmation evidence makes regeneration fail closed. The gate also -uses each contender's observed three-trial interval: if those intervals cannot -resolve the 5% boundary, regeneration stops for a quieter recapture. +In-place solver timing uses a separate authoritative ladder. The general +ladder's back-to-back launches are suitable for non-destructive operations, +but an in-place solver would consume its own output after the first launch. +``bench_solver_ladder.cu`` therefore remeasures POTRF, TRSV, and POSV for every +supported native block/warp/thread and NVIDIA block/thread launch plan. Each +timed launch consumes a fresh valid system from a bounded ring, while input +initialization remains outside the timed region. Plans are randomized within +nine paired rounds and every raw sample is recorded. Any contender can win +under the same 5% dependency and ±2% SIMT tie rules; there is no special veto +or asymmetric treatment of NVIDIA thread. Missing solver cells make +regeneration fail closed. Numerical correctness remains a separate signed- +receipt requirement, not something inferred from timing agreement. The ``constexpr`` ``glass::recommend()`` query returns one ``execution_plan`` containing family, scope, and launch packing. @@ -176,8 +177,8 @@ retune?". (op, N, precision, batch) cells measured on both, **131 (33 %) crown a different tier**. The smaller Orin selects native thread more often (87 vs 66 cells), NVIDIA thread more often (52 vs 29), and -block less often (48 vs 82). After the independent-valid-input veto, the -NPROB=8192 regime that actually generates the tables differs in **39 of 132** +block less often (48 vs 82). Under the legacy 2026-08-30 confirmation method, +the NPROB=8192 regime that generated those archived tables differs in **39 of 132** cells. With far fewer SMs to fill, packing more problems per warp often beats spreading one problem across more lanes, but the movement is not one-directional. No library source differs between the two @@ -205,8 +206,10 @@ Two practical notes from the Orin bring-up: separate-compilation device link against the fatbin, so Jetson runs the full native/NVIDIA ladder. In the current capture, the NVIDIA block and thread tiers take 87 and 52 of 396 raw ladder cells respectively; the shipped - throughput table retains NVIDIA thread in 15 of 132 cells after its - independent-valid-input veto. + legacy throughput table retained NVIDIA thread in 15 of 132 cells. New + captures replace that asymmetric confirmation with the unified fresh-input + solver ladder described above; these historical counts are retained only to + document the earlier release. * The ``nvpmodel`` labels are ceilings, not draws. Sampling the board rails at 1 Hz with the GPU ≥98.6 % busy, the whole ladder pulls 9.2 W in the 15 W mode, 13.4 W in the 30 W mode and 16.0 W in the 50 W mode. Small diff --git a/docs/source/user_guide/tutorials/sweep_results.rst b/docs/source/user_guide/tutorials/sweep_results.rst index 9e605ec..63572f0 100644 --- a/docs/source/user_guide/tutorials/sweep_results.rst +++ b/docs/source/user_guide/tutorials/sweep_results.rst @@ -45,10 +45,11 @@ f64 curves may stop at their shared-memory feasibility limit. ``recommend<>()`` is keyed on the **NPROB=8192** throughput regime. Its policy first applies a ±2% tie band among dependency-free SIMT contenders, then requires an NVIDIA contender to clear the best native choice by more than -5%. NVIDIA-thread solver winners must additionally reproduce that margin when -every timed launch consumes an independent valid system. This confirmation is -a veto only; it cannot promote NVIDIA thread into a cell. These rules keep -small, noisy, or mutation-dependent gaps out of the generated table. +5%. For destructive POTRF/TRSV/POSV, every displayed and selected candidate is +remeasured by the unified solver ladder on fresh valid inputs. The plans are +randomized within paired rounds and every raw sample is retained; all native +and NVIDIA candidates use the same selection rule. These rules keep small, +noisy, or mutation-dependent gaps out of the generated table. float32 ~~~~~~~ @@ -83,10 +84,10 @@ float64 Raw ladder winner per (op, N), per regime ------------------------------------------ -The main-ladder policy winner at each ``(op, N)`` is listed for all three -NPROB regimes. The separate valid-input veto affects only final -NVIDIA-thread throughput defaults and is summarized below; it does not rewrite -these raw-capture figures. +The policy winner at each ``(op, N)`` is listed for all three NPROB regimes. +Non-destructive rows come from the general ladder; POTRF/TRSV/POSV rows come +from its fresh-input solver companion. Thus the plots, winner list, and shipped +solver defaults all consume the same authoritative measurements. The broad high-batch shape is mixed by design: native thread dominates many small packed problems, native warp/block remain important as work grows, and the NVIDIA block and thread implementations take measured factor/solve bands. @@ -233,6 +234,11 @@ for either GLASS's native thread code or cuSOLVERDx's block interface: - ``chol`` N=8,12; ``trsv`` N=16,24,32 - ``chol`` N=4,6,8; ``trsv`` N=4–32 +The table and replication paragraph below document the archived 2026-08-30 +release method. The next retune replaces its asymmetric confirmation with the +unified fresh-input solver ladder described above; regenerate these counts and +ranges from that accepted capture before treating them as current. + Those ranges enumerate the measured sizes ``4, 6, 8, 12, 16, 24, 32``; they do not imply testing every intervening integer. Against the fastest native tier on independent valid inputs, selected NVIDIA-thread cells range from diff --git a/test/conftest.py b/test/conftest.py index 8a5f2bd..65cc788 100644 --- a/test/conftest.py +++ b/test/conftest.py @@ -5,6 +5,8 @@ Each binary is compiled with nvcc against the local glass.cuh. """ +from __future__ import annotations + import hashlib import os import pathlib diff --git a/test/gpu-proof.json b/test/gpu-proof.json index ee2aa2b..ddff12f 100644 --- a/test/gpu-proof.json +++ b/test/gpu-proof.json @@ -20,7 +20,7 @@ }, "fingerprint": { "algorithm": "sha256-manifest-v2", - "digest": "087f0b4341f951f27bc61d099be54029159e4c3af6a824105c507f681f5a6b26", + "digest": "f6866cecea06fe968508591e4e8c6b417db921750652fafe219ff46a6354d89c", "excluded_paths": [ "gpu-proof.json" ], @@ -39,15 +39,15 @@ }, "mode": "local", "repo": { - "branch": "feature/nvidia-thread-backend", - "commit_sha": "6f01ee827dfabde60c5686d004b00dd57e9d9dff", + "branch": "eval/unified-solver-measurement", + "commit_sha": "470979b723c36427f4859f03dfabdb01762ab59d", "dirty": false, "github_username": "plancherb1", "remote_url": "git@github.com:A2R-Lab/GLASS.git" }, "schema_version": "3", "session": { - "ended_at": "2026-08-30T21:24:47Z", + "ended_at": "2026-09-01T14:52:01Z", "node_ids": [ "test/test_l1.py::test_axpy[cg-8-normal]", "test/test_l1.py::test_axpy[cg-64-mixed]", @@ -4335,15 +4335,20 @@ "test/test_api_hygiene.py::test_removed_advisors_do_not_reenter_public_headers", "test/test_api_hygiene.py::test_one_target_architecture_selector", "test/test_tuning_tools.py::test_ladder_preserves_measured_native_runner_up", - "test/test_tuning_tools.py::test_valid_input_confirmation_is_a_veto_not_a_promotion", - "test/test_tuning_tools.py::test_valid_input_confirmation_requires_every_selected_cell", - "test/test_tuning_tools.py::test_valid_input_confirmation_rejects_decision_scale_jitter", + "test/test_tuning_tools.py::test_fresh_input_solver_capture_is_symmetric_and_authoritative", + "test/test_tuning_tools.py::test_fresh_input_solver_capture_requires_every_solver_cell", + "test/test_tuning_tools.py::test_solver_parser_retains_launch_plan_and_raw_rounds", + "test/test_tuning_tools.py::test_solver_parser_rejects_too_few_rounds", + "test/test_tuning_tools.py::test_ladder_regeneration_names_symmetric_solver_evidence", + "test/test_tuning_tools.py::test_ladder_regeneration_rejects_noisy_selected_solver_plan", "test/test_tuning_tools.py::test_local_override_emits_both_dependency_policies", "test/test_tuning_tools.py::test_local_ladder_override_does_not_mask_other_tables", "test/test_bench_common.py::test_foreign_pids_subtracts_own_and_baseline", "test/test_bench_common.py::test_foreign_pids_all_tolerated", "test/test_bench_common.py::test_watch_process_tolerates_baseline_pids", "test/test_bench_common.py::test_watch_process_trips_on_new_pid", + "test/test_tune_isolation.py::test_run_isolated_uses_seekable_capture_not_pipe", + "test/test_tune_isolation.py::test_from_ladder_without_companion_resumes_solver_only", "test/test_banded.py::test_bdmv[1-2-3]", "test/test_banded.py::test_bdmv[1-6-4]", "test/test_banded.py::test_bdmv[7-2-3]", @@ -4862,6 +4867,7 @@ "test/test_api_hygiene.py", "test/test_tuning_tools.py", "test/test_bench_common.py", + "test/test_tune_isolation.py", "-q", "--gpu-proof-enable", "--gpu-proof-out", @@ -4871,7 +4877,7 @@ "--gpu-proof-shard", "tiers", "--gpu-proof-shard-fingerprint-paths", - "glass.cuh,glass-cgrps.cuh,glass-defaults.cuh,glass-dispatch.cuh,src/base/L1,src/base/barrier.cuh,src/base/dispatch.cuh,src/base/flags.cuh,src/base/L2,src/base/L3,src/cgrps,test/conftest.py,test/cuda/helpers.cuh,test/expected_skips.txt,src/base/est/svd3.cuh,bench/bench_common.py,bench/tune.py,bench/tune_pick.py,bench/autotune.py,test/test_thread.py,test/test_warp.py,test/test_defaults.py,test/test_dispatch.py,test/test_api_hygiene.py,test/test_tuning_tools.py,test/test_bench_common.py,test/cuda/test_thread.cu,test/cuda/test_warp.cu,test/cuda/test_defaults.cu,test/cuda/test_dispatch.cu", + "glass.cuh,glass-cgrps.cuh,glass-defaults.cuh,glass-dispatch.cuh,src/base/L1,src/base/barrier.cuh,src/base/dispatch.cuh,src/base/flags.cuh,src/base/L2,src/base/L3,src/cgrps,test/conftest.py,test/cuda/helpers.cuh,test/expected_skips.txt,src/base/est/svd3.cuh,bench/bench_common.py,bench/tune.py,bench/tune_pick.py,bench/autotune.py,test/test_thread.py,test/test_warp.py,test/test_defaults.py,test/test_dispatch.py,test/test_api_hygiene.py,test/test_tuning_tools.py,test/test_bench_common.py,test/test_tune_isolation.py,test/cuda/test_thread.cu,test/cuda/test_warp.cu,test/cuda/test_defaults.cu,test/cuda/test_dispatch.cu", "--gpu-proof-fingerprint-paths", "glass.cuh,glass-cgrps.cuh,glass-nvidia.cuh,glass-defaults.cuh,glass-dispatch.cuh,src,test/cuda,test/conftest.py" ], @@ -4945,68 +4951,68 @@ ], "shards": [ { - 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"duration_s": 0.2491, + "duration_s": 0.2551, "node_id": "test/test_trailing_sync.py::test_trailing_sync_surface[l3_simt_batched]", "outcome": "passed", "phase": "call" }, { "checks": [], - "duration_s": 0.2396, + "duration_s": 0.2438, "node_id": "test/test_trailing_sync.py::test_trailing_sync_surface[l3_simt_strided_batched]", "outcome": "passed", "phase": "call" }, { "checks": [], - "duration_s": 0.241, + "duration_s": 0.2436, "node_id": "test/test_trailing_sync.py::test_trailing_sync_surface[l3_factor_solve]", "outcome": "passed", "phase": "call" }, { "checks": [], - "duration_s": 0.2374, + "duration_s": 0.2459, "node_id": "test/test_trailing_sync.py::test_trailing_sync_cublasdx_gemm", "outcome": "passed", "phase": "call" diff --git a/test/run_gpu_proof.sh b/test/run_gpu_proof.sh index bdf919c..6e95bba 100755 --- a/test/run_gpu_proof.sh +++ b/test/run_gpu_proof.sh @@ -72,7 +72,8 @@ SHARD_DRIVERS[factor]="test/cuda/test_fused.cu test/cuda/test_factor_check.cu \ SHARD_PATHS[factor]="$ROOTS_BASE,$DENSE_BASE,$TEST_BASE" SHARD_FILES[tiers]="test/test_thread.py test/test_warp.py test/test_defaults.py test/test_dispatch.py \ - test/test_api_hygiene.py test/test_tuning_tools.py test/test_bench_common.py" + test/test_api_hygiene.py test/test_tuning_tools.py test/test_bench_common.py \ + test/test_tune_isolation.py" SHARD_DRIVERS[tiers]="test/cuda/test_thread.cu test/cuda/test_warp.cu \ test/cuda/test_defaults.cu test/cuda/test_dispatch.cu" # svd3.cuh: the dispatch face routes eig3 into its est/ body. diff --git a/test/test_tune_isolation.py b/test/test_tune_isolation.py new file mode 100644 index 0000000..c796f44 --- /dev/null +++ b/test/test_tune_isolation.py @@ -0,0 +1,64 @@ +"""Contract tests for the unified tuning process wrapper (no GPU required).""" + +import pathlib +import subprocess +import sys + +sys.path.insert(0, str(pathlib.Path(__file__).resolve().parents[1] / "bench")) +import tune # noqa: E402 + + +def test_run_isolated_uses_seekable_capture_not_pipe(monkeypatch): + """Raw solver samples must not fill an unread stdout pipe and deadlock.""" + monkeypatch.setattr(tune, "require_quiet_gpu", lambda force: None) + monkeypatch.setattr(tune, "compute_pids", lambda: set()) + + class FakeProc: + returncode = 0 + + def __init__(self, argv, **kwargs): + del argv + capture = kwargs["stdout"] + assert capture is not subprocess.PIPE + assert capture.seekable() + capture.write("raw-solver-output\n") + capture.flush() + + monkeypatch.setattr(tune.subprocess, "Popen", FakeProc) + monkeypatch.setattr(tune, "watch_process", + lambda proc, baseline: (0, set())) + + assert tune.run_isolated(["fake-harness"]) == "raw-solver-output\n" + + +def test_from_ladder_without_companion_resumes_solver_only( + monkeypatch, tmp_path): + mega = tmp_path / "mega.txt" + mega.write_text("mega rows\n") + solver = tmp_path / "solver.txt" + solver.write_text("solver rows\n") + calls = [] + + monkeypatch.setattr(tune, "mathdx_root", lambda: None) + monkeypatch.setattr(tune, "build_solver_ladder", + lambda sms, mdx: "solver-binary") + + def fake_run(binary, quick, sms, margin, **kwargs): + calls.append((binary, quick, sms, margin, kwargs)) + return solver + + monkeypatch.setattr(tune, "run_solver_ladder", fake_run) + monkeypatch.setattr(tune, "regen_ladder", + lambda *args: ("defaults\n", 1, 1)) + monkeypatch.setattr(tune, "show_diff", lambda *args: False) + monkeypatch.setattr( + sys, "argv", + ["tune.py", "--sm", "870", "--legs", "ladder", "--dry-run", + "--from-ladder", str(mega), "--sched", "64:200,8192:50"]) + + tune.main() + + assert len(calls) == 1 + assert calls[0][0:3] == ("solver-binary", False, 870) + assert calls[0][4]["sched"] == [("64", "200"), ("8192", "50")] + assert calls[0][4]["seed"] == 1 diff --git a/test/test_tuning_tools.py b/test/test_tuning_tools.py index 759ca56..54bbe27 100644 --- a/test/test_tuning_tools.py +++ b/test/test_tuning_tools.py @@ -18,60 +18,94 @@ potrf N=8 | BLOCK | WARP | THREAD || block tb32=10.0 warp w2=8.0 thread t32=7.0 nv=6.0 nvt t32=3.0 -> NVIDIA_THREAD """ -SYNTHETIC_NVT_VALID = """\ -NVT_VALID op=potrf N=8 dtype=f32 nprob=8192 slots=64 block=8.0000 block_shape=128 block_spread=0.50 warp=7.5000 warp_shape=8 warp_spread=0.60 thread=7.0000 thread_shape=64 thread_spread=0.40 nvidia_thread=7.2000 nvt_shape=64 nvt_spread=0.70 +SYNTHETIC_SOLVER = """\ +SOLVER_RESULT op=potrf N=8 dtype=f32 nprob=8192 slots=64 impl=block cfg=tb32 ns=8.0000 spread=0.50 samples=8.00,8.04,8.02 +SOLVER_RESULT op=potrf N=8 dtype=f32 nprob=8192 slots=64 impl=warp cfg=w2 ns=7.5000 spread=0.60 samples=7.50,7.52,7.54 +SOLVER_RESULT op=potrf N=8 dtype=f32 nprob=8192 slots=64 impl=thread cfg=t32 ns=7.0000 spread=0.40 samples=7.00,7.01,7.02 +SOLVER_RESULT op=potrf N=8 dtype=f32 nprob=8192 slots=64 impl=nvidia cfg=tb256 ns=6.0000 spread=0.70 samples=6.00,6.02,6.04 +SOLVER_RESULT op=potrf N=8 dtype=f32 nprob=8192 slots=64 impl=nvidia_thread cfg=t32 ns=7.2000 spread=0.70 samples=7.20,7.22,7.24 """ def test_ladder_preserves_measured_native_runner_up(): - full = tune.winners_from_sweep(SYNTHETIC_SWEEP, 0.05) - native = tune.winners_from_sweep(SYNTHETIC_SWEEP, 0.05, native_only=True) + full = tune.winners_from_sweep( + SYNTHETIC_SWEEP, 0.05, solver_text=SYNTHETIC_SOLVER) + native = tune.winners_from_sweep( + SYNTHETIC_SWEEP, 0.05, native_only=True, + solver_text=SYNTHETIC_SOLVER) - assert full[("f32", "potrf")][8] == "nvidia_thread" + assert full[("f32", "potrf")][8] == "nvidia" assert native[("f32", "potrf")][8] == "thread" -def test_valid_input_confirmation_is_a_veto_not_a_promotion(): - full = tune.winners_from_sweep(SYNTHETIC_SWEEP, 0.05) - gated, vetoes = tune.apply_nvt_valid_veto(full, SYNTHETIC_NVT_VALID, 0.05) - - assert gated[("f32", "potrf")][8] == "thread" - assert vetoes == 1 - - native_main = (SYNTHETIC_SWEEP - .replace("nv=6.0", "nv=7.5") - .replace("nvt t32=3.0", "nvt t32=7.5")) - already_native = tune.winners_from_sweep(native_main, 0.05) - gated, vetoes = tune.apply_nvt_valid_veto( - already_native, - SYNTHETIC_NVT_VALID.replace("nvidia_thread=7.2000", "nvidia_thread=2.0000"), - 0.05, +def test_fresh_input_solver_capture_is_symmetric_and_authoritative(): + promoted = SYNTHETIC_SOLVER.replace( + "impl=nvidia_thread cfg=t32 ns=7.2000 spread=0.70 samples=7.20,7.22,7.24", + "impl=nvidia_thread cfg=t32 ns=2.0000 spread=0.70 samples=2.00,2.01,2.02", ) - assert gated[("f32", "potrf")][8] == "thread" - assert vetoes == 0 + winners = tune.winners_from_sweep( + SYNTHETIC_SWEEP, 0.05, solver_text=promoted) + assert winners[("f32", "potrf")][8] == "nvidia_thread" + # The old destructive-input row says NVIDIA thread=3ns, but the unified + # solver row can select any other contender; it is not an asymmetric veto. + winners = tune.winners_from_sweep( + SYNTHETIC_SWEEP, 0.05, solver_text=SYNTHETIC_SOLVER) + assert winners[("f32", "potrf")][8] == "nvidia" -def test_valid_input_confirmation_requires_every_selected_cell(): - full = tune.winners_from_sweep(SYNTHETIC_SWEEP, 0.05) + +def test_fresh_input_solver_capture_requires_every_solver_cell(): try: - tune.apply_nvt_valid_veto(full, "", 0.05) + tune.winners_from_sweep(SYNTHETIC_SWEEP, 0.05, solver_text="") except SystemExit as error: - assert "lacks valid-input confirmation" in str(error) + assert "capture is incomplete" in str(error) else: - raise AssertionError("missing confirmation must fail closed") + raise AssertionError("missing solver measurement must fail closed") + + +def test_solver_parser_retains_launch_plan_and_raw_rounds(): + rows = tune.tp.parse_solver_configs(SYNTHETIC_SOLVER) + row = rows[("f32", "potrf", 8, "nvidia_thread", "t32")] + + assert row["ns"] == 7.2 + assert row["samples"] == (7.2, 7.22, 7.24) + assert row["slots"] == 64 + + +def test_solver_parser_rejects_too_few_rounds(): + malformed = SYNTHETIC_SOLVER.replace( + "samples=7.20,7.22,7.24", "samples=7.20,7.22") + try: + tune.tp.parse_solver_configs(malformed) + except ValueError as error: + assert "at least three" in str(error) + else: + raise AssertionError("undersampled solver row must fail closed") + + +def test_ladder_regeneration_names_symmetric_solver_evidence(): + generated, groups, plans = tune.regen_ladder( + SYNTHETIC_SWEEP, 0.05, "mega.txt", 1200, + SYNTHETIC_SOLVER, "solver.txt") + + assert groups == 1 + assert plans == 5 + assert "Fresh-input solver sweep: solver.txt" in generated + assert "veto" not in generated[generated.index("// === BEGIN tune.py ladder sm_120 ==="): + generated.index("// === END tune.py ladder sm_120 ===")] -def test_valid_input_confirmation_rejects_decision_scale_jitter(): - full = tune.winners_from_sweep(SYNTHETIC_SWEEP, 0.05) - noisy = (SYNTHETIC_NVT_VALID - .replace("nvidia_thread=7.2000", "nvidia_thread=6.5000") - .replace("nvt_spread=0.70", "nvt_spread=5.10")) +def test_ladder_regeneration_rejects_noisy_selected_solver_plan(): + noisy = SYNTHETIC_SOLVER.replace( + "impl=nvidia cfg=tb256 ns=6.0000 spread=0.70", + "impl=nvidia cfg=tb256 ns=6.0000 spread=5.10") try: - tune.apply_nvt_valid_veto(full, noisy, 0.05) + tune.regen_ladder(SYNTHETIC_SWEEP, 0.05, "mega.txt", 1200, + noisy, "solver.txt") except SystemExit as error: - assert "cannot resolve" in str(error) + assert "round spread" in str(error) else: - raise AssertionError("decision-scale confirmation jitter must fail closed") + raise AssertionError("decision-scale solver jitter must fail closed") def test_local_override_emits_both_dependency_policies(tmp_path): @@ -79,12 +113,14 @@ def test_local_override_emits_both_dependency_policies(tmp_path): output = tmp_path / "defaults.cuh" capture.write_text(SYNTHETIC_SWEEP) - autotune.emit_defaults_table(capture, output, 1200, margin=0.05) + solver = tmp_path / "solver.txt" + solver.write_text(SYNTHETIC_SOLVER) + autotune.emit_defaults_table(capture, solver, output, 1200, margin=0.05) generated = output.read_text() assert "bool allow_nvidia" in generated assert "if (allow_nvidia)" in generated - assert "backend::nvidia_thread" in generated + assert "backend::nvidia_block" in generated assert "if (!allow_nvidia)" in generated assert "backend::thread" in generated