← GPU latency results

gpu/fft

Legacy report: ingestion provenance is unrecorded; no raw artifact is inferred.

Original report

gpu/fft: CUDA public API benchmark

Raw run: data/results/nvidia-gpu/gpu/fft/20261013_133616/.

Values are median (IQR) µs per operation. Samples time batches of declared operations plus device synchronization; retained output memory is limited to 256 MiB. Batch counts and raw durations are preserved in JSONL.

Setup, input transfers, library/plan initialization, JIT compilation, validation downloads and output destruction are outside timing. Intrinsic output allocation is inside timing; _into rows instead reuse preallocated destinations across samples. Direct and eager each use one entered session across samples; direct FFT uses cached FftExecutor. JAX eager and compiled JIT are separate arms.

All measured output elements are checked against NumPy/SciPy double-precision reference results before and after sampling. Unsupported rows include unavailable APIs and APIs whose internal setup cannot be excluded; these are not performance measurements. Short batches limited by output memory remain identifiable in the sampling table.

Sampling

Run metadata

schema_version: 1
target_profile: nvidia-gpu
suite_id: gpu/fft
suite_file: benchmarks/gpu/fft.yaml
timestamp: '2026-10-13T13:36:17Z'
tenferro_rs:
  path: extern/tenferro-rs
  commit: dfbdf6b66d40162f4731da116ec42ed034da0dce
  dirty: false
  features:
  - cuda
  resolved_path: /workspaces/tenferro-benchmark/extern/tenferro-rs
harness:
  path: /workspaces/tenferro-benchmark
  commit: 2858dcda46699788bc1df84d923a95ed93ef6469
  dirty: false
environment:
  hostname: 737f43342de3
  os: Linux-6.8.0-146-generic-x86_64-with-glibc2.39
  arch: x86_64
  cpu: x86_64
  env:
    OMP_NUM_THREADS: '1'
    RAYON_NUM_THREADS: '1'
    BENCHMARK_TARGET_PROFILE: nvidia-gpu
    OPENBLAS_ROOT: /opt/openblas
    OPENBLAS_NUM_THREADS: '1'
    CUDA_HOME: /usr/local/cuda
    CUDA_PATH: /usr/local/cuda
    TENFERRO_CUTENSOR_PATH: /usr/lib/x86_64-linux-gnu/libcutensor/12/libcutensor.so.2
    BENCH_RUNS: '15'
    BENCH_WARMUPS: '3'
    USE_CUDA: '1'
timing_policy:
  version: 2
  scope: operation execution and completion synchronization
  setup: fixtures, H2D, runtime/session/handles, plans and compilation outside every
    timer
  cleanup: returned outputs retained until after timer stops
  minimum_untimed_priming_runs: 1
  short_operation_sampling: adaptive batches target 2 ms, at most 4096 operations
    and 256 MiB retained output; output-reuse destinations prepared before timing
  trace_execution: 'tenferro trace excluded: run_prepared cannot borrow the entered
    session; JAX JIT compilation outside timing'
  setup_diagnostics: false
blas:
  implementation: none
python_backends:
  pytorch:
    available: true
    version: 2.12.0+cu130
    file: /workspaces/tenferro-benchmark/.venv/lib/python3.12/site-packages/torch/__init__.py
    provider: mkl
    blas_info: mkl
    lapack_info: mkl
    config: "PyTorch built with:\n  - GCC 13.3\n  - C++ Version: 202002\n  - Intel(R)\
      \ oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R)\
      \ 64 architecture applications\n  - Intel(R) MKL-DNN v3.11.2 (Git Hash 03c022d3ffdcee958cfacbe720048e725fdf644c)\n\
      \  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n  - LAPACK is enabled (usually provided\
      \ by MKL)\n  - NNPACK is enabled\n  - CPU capability usage: AVX2\n  - CUDA Runtime\
      \ 13.0\n  - NVCC architecture flags: -gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90;-gencode;arch=compute_100,code=sm_100;-gencode;arch=compute_120,code=sm_120\n\
      \  - CuDNN 92.7  (built against CUDA 12.9)\n    - Built with CuDNN 92.0\n  -\
      \ Magma 2.6.1\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=7661cd9c6b841b62b7f411aa52ec51f05457263b,\
      \ CUDA_FLAGS= -DLIBCUDACXX_ENABLE_SIMPLIFIED_COMPLEX_OPERATIONS -Xfatbin -compress-all\
      \ -DONNX_NAMESPACE=onnx_torch -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80\
      \ -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90 -gencode\
      \ arch=compute_100,code=sm_100 -gencode arch=compute_120,code=sm_120 -Xcudafe\
      \ --diag_suppress=cc_clobber_ignored,--diag_suppress=field_without_dll_interface,--diag_suppress=base_class_has_different_dll_interface,--diag_suppress=dll_interface_conflict_none_assumed,--diag_suppress=dll_interface_conflict_dllexport_assumed,--diag_suppress=bad_friend_decl\
      \ --expt-relaxed-constexpr --expt-extended-lambda -Xfatbin -compress-all --threads\
      \ 2 -compress-mode=size -Wno-deprecated-gpu-targets --expt-extended-lambda -DCUB_WRAPPED_NAMESPACE=at_cuda_detail\
      \ -DDISABLE_CUSPARSE_DEPRECATED -DCUDA_HAS_FP16=1 -D__CUDA_NO_HALF_OPERATORS__\
      \ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__\
      \ -DC10_NODEPRECATED, CUDA_VERSION=13.0, CUDNN_VERSION=9.20.0, CXX_COMPILER=/opt/rh/gcc-toolset-13/root/usr/bin/c++,\
      \ CXX_FLAGS= -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO\
      \ -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_MSLK -DUSE_PYTORCH_QNNPACK\
      \ -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -DC10_NODEPRECATED\
      \ -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=range-loop-construct\
      \ -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-unknown-pragmas\
      \ -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow\
      \ -Wsuggest-override -Wno-psabi -Wno-error=old-style-cast -faligned-new -Wno-maybe-uninitialized\
      \ -fno-math-errno -fno-trapping-math -Werror=format -Wno-dangling-reference\
      \ -Wno-error=dangling-reference -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1,\
      \ PERF_WITH_AVX2=1, TORCH_VERSION=2.12.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1,\
      \ USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF,\
      \ USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF,\
      \ USE_XCCL=OFF, USE_XPU=OFF, \n"
    library: /workspaces/tenferro-benchmark/.venv/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so
    linked_libraries:
    - /workspaces/tenferro-benchmark/.venv/lib/python3.12/site-packages/torch/lib/libgomp.so.1
  jax:
    available: true
    version: 0.10.1
    jaxlib_version: 0.10.1
    backend: gpu
    file: /workspaces/tenferro-benchmark/.venv/lib/python3.12/site-packages/jaxlib/__init__.py
    provider: xla_gpu
    dot_backend: xla_gpu
    lapack_provider: none_detected
    library: /workspaces/tenferro-benchmark/.venv/lib/python3.12/site-packages/jaxlib/_ifrt_proxy.so
    linked_libraries: []
julia:
  available: false
  reason: julia not found on PATH
cuda:
  device_ordinal: 0
  device_name: NVIDIA GeForce RTX 3060
  device_uuid: GPU-a78d5217-eba3-72c2-3d5b-8ae496ebbc2e
  device_memory_bytes: 12884901888
  driver_version: 580.178.04
  runtime_version: '12.9'
  cuda_version: '13.0'
  cudnn_version: '92700'
gpu_assignment:
  role: benchmark
  physical_device_index: 0
  device_uuid: GPU-a78d5217-eba3-72c2-3d5b-8ae496ebbc2e
  cuda_visible_devices: GPU-a78d5217-eba3-72c2-3d5b-8ae496ebbc2e
  process_device_ordinal: 0
  verification_only: false
collection:
  command: BENCHMARK_TARGET_PROFILE=nvidia-gpu GPU_BENCH_DEVICE=0 BENCH_RUNS=15 BENCH_WARMUPS=3
    ./scripts/run_gpu_public_api.sh fft
  devcontainer_config: .devcontainer/cuda/devcontainer.json
  expected_cases: 16
  selected_cases: 16
  sha256:
    .devcontainer/cuda/devcontainer.json: 76569d9e898967d094988483a7adf6ab96eb426793db7248545d50e155f19cf1
    .devcontainer/cuda/Dockerfile: 1cadf2f890f01187553bab0f136dc817f8964eff6a35b1806b1448a7383d21de
    src/bin/benchmark_gpu_public_api.rs: 249184323d09433d1fbf401284e39fa797c0252dceb20351bbcc151551d1b32c
    src/bin/benchmark_gpu_public_api/runtime_ops.rs: d7b64dccce85c804b8f67543fefca89836f045702bdcf15b5441c7bb8543eb21
    src/bin/benchmark_gpu_public_api/structural.rs: 382fb5ffb6d86ac1a4a66723376808e9c95c8a8731570a5617f2ffed2a6e40a7
    src/bin/benchmark_gpu_public_api/linalg.rs: 84162923fa0d0772c88ec9c4eab169ad3a75e5d16ff76961d1541670dd8565c1
    src/bin/benchmark_gpu_public_api/reuse.rs: ad72b565373904275128d7beb8673bada08d922e8f310be6cedb0d2d8187898f
    /workspaces/tenferro-benchmark/scripts/benchmark_gpu_public_api.py: 4d05b9facc0b7e5a3ff31146fc42f98d680010264c6606a3154e9da2dfa6a0cc
    /workspaces/tenferro-benchmark/scripts/gpu_public_api_ops.py: 71cb26510a35647e7aadc46827ca00d06a06e283eacfcba8ebe93d2e8e8add86
    scripts/run_gpu_public_api.sh: 3516e06358fc3c2e36a066067cdffcab85673c7e78bf9a244dde318d109dd772
    data/results/nvidia-gpu/gpu/fft/20261013_133616/fixtures/manifest.json: 6f14a232def70d9c1acea54911211620ba8165c34541f631b0f76c95792ea041
    target/release/benchmark_gpu_public_api: b505a2bdb3d0c5a7bffd1390af659a154753ea998ec68becd4779e137c9c7150
  reference_numpy_version: 2.4.2