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Benchmarks

The benchmark suite under bench/ compares GLASS variants against block-level CUDA library baselines and against each other, so you can see which path wins for a given shape on your hardware.

What's in bench/

File Comparison
bench_reduce.cu glass::*::reduce/dot/nrm2 (plain, low_memory, high_speed, compile-time) vs CUB BlockReduce vs glass::nvidia::reduce
bench_gemv.cu glass::gemv (runtime + compile-time) vs raw cuBLASDx vs glass::nvidia::gemv (default + caller-pinned BlockDim<256>)
bench_gemm.cu glass::gemm (plain, tiled, compile-time) vs raw cuBLASDx vs glass::nvidia::gemm (default + caller-pinned)
bench_blockdim.cu glass::nvidia::gemm cuBLASDx-chosen block_dim vs caller-pinned BlockDim<128> vs BlockDim<352>
bench_gemm_batched.cu glass::nvidia::gemm_batched<...,BATCH> vs a naive for(b) loop, for BATCH ∈ {4, 8, 16, 32}
bench_gemm_batched_1d.cu 1D-launch gemm_batched_1d (SIMT vs cuBLASDx) — feeds the autotune table
bench_lapack.cu (needs cuSOLVERDx) pure-SIMT glass::potrf / trsm vs glass::nvidia::potrf / trsm / posv (fused)

CUB ships with CUDA 11+. cuBLASDx and cuSOLVERDx ship together in NVIDIA MathDx — see :doc:`../getting_started/installation`.

Running the suite

Set MATHDX_ROOT to your MathDx install, then:

cd /path/to/GLASS
python3 bench/run_bench.py

# Custom iteration count (default: 10000)
python3 bench/run_bench.py --iters 50000

# Skip cuBLASDx (only bench_reduce will run)
python3 bench/run_bench.py --no-cublasdx

The driver auto-detects dependencies and prints what it found at startup:

=== GLASS Benchmark Suite ===
GPU arch: sm_120 (SM1200)
cuBLASDx: enabled (/opt/nvidia/mathdx/25.12)
cuSOLVERDx: enabled
Iterations: 10000

bench_lapack is skipped automatically if cusolverdx.hpp is not present under $MATHDX_ROOT/include/. When cuSOLVERDx is enabled, the driver adds the -rdc=true -dlto -lcusolverdx ... device-link flags for you.

Results are printed as a Markdown table and saved to bench/results/bench_<hostname>.json. Timing uses the GRiD pattern — the iteration loop runs inside the kernel to amortize launch overhead.

Note

Anti-optimization safeguards are baked into every bench loop: per-iteration writes to a volatile sink defeat dead-store elimination; destructive inputs (Cholesky, LU, QR overwrite their input) are reloaded from a master copy each iteration; nvcc -Xptxas -O1 is enforced. Numbers below ~0.1 µs/op for a non-trivial kernel almost always mean the bench was elided — recheck the safeguards.

Autotuning from the bench

The same harness backs bench/autotune.py, which measures SIMT vs cuBLASDx per shape and writes a per-host override table so the glass::nvidia::* auto-dispatch picks the measured winner instead of the static heuristic. See :doc:`../concepts/tuning` for that workflow.