submission 782512
ooousay · python · License unknown
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No package. Vendor the mirrored source: 36 lines, June 9 Researcher Reciprocity License v1.0.
submission.py
curl "https://kernelindex.com/api/v1/implementations/kernelbot-conv2d-v2-782512?include=source"interfacepython
Compatibility
measured onNVIDIA A100
declared hardwareNVIDIA A100
architecturessm_80
dtypesfp32
Benchmark evidence
1 measurement across 1 GPU, fastest first.
Operation / workload
Hardware
Latency
Rank
Observed
Reported · How evidence levels are derived →
Source and license
sourceavailable
revision digestsha256:02cb3f02f7d4351e6926b06d2bb11fa54c2334e017be87dde0fe89869e589654
license declaredunknown
license concludedunknown
authorsooousay
imported2026-08-15
Kernel source
submission.py36 lines
"""Current best submission.
cuDNN baseline + `cudnn.benchmark = True`.
Root cause of the original 2.59 s outlier on S256 K32 C128 B1: with
`cudnn.deterministic = True` (set by the reference's DeterministicContext) and
`benchmark = False` (PyTorch default), cuDNN picks an FFT-conv engine that
degenerates to a memory-bound mat-vec at B=1.
Flipping `benchmark = True` lets cuDNN run a timed heuristic on the first call
and select a much faster implicit-GEMM path for K=32. The eval harness does
one warmup call before timing, so algo selection happens off-clock.
Tradeoff: `benchmark = True` can pick worse algos for some mid-sized shapes
when L2 is cleared between calls (the heuristic times candidates with hot L2).
On this benchmark suite, that costs ~30 ms across all shapes vs cuDNN-default
but saves ~2470 ms on the K=32 shape — net ~11x speedup on total wall time.
TF32 stays off: reference is strict FP32, default-TF32 cuDNN diverges >1e-3
on K=16 and K=32 shapes.
"""
from task import input_t, output_t
import torch
import torch.nn.functional as F
torch.backends.cudnn.allow_tf32 = False
torch.backends.cuda.matmul.allow_tf32 = False
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = False
def custom_kernel(data: input_t) -> output_t:
input_tensor, kernel, output = data
output[...] = F.conv2d(input_tensor, kernel, stride=1, padding=0)
return output
scrolls · 36 lines total
Source code from GPU Mode and the KernelBot dataset · June 9 Researcher Reciprocity License v1.0
Changes from previous submission
Against this author's previous submission submission 782511.
- """Current best submission. Starts as the cuDNN baseline.+ """Current best submission.- TF32 must stay off: the reference runs in DeterministicContext (strict FP32),- and default-TF32 cuDNN diverges by >1e-3 on the K=32 shape.+ cuDNN baseline + `cudnn.benchmark = True`.++ Root cause of the original 2.59 s outlier on S256 K32 C128 B1: with+ `cudnn.deterministic = True` (set by the reference's DeterministicContext) and+ `benchmark = False` (PyTorch default), cuDNN picks an FFT-conv engine that+ degenerates to a memory-bound mat-vec at B=1.++ Flipping `benchmark = True` lets cuDNN run a timed heuristic on the first call+ and select a much faster implicit-GEMM path for K=32. The eval harness does+ one warmup call before timing, so algo selection happens off-clock.++ Tradeoff: `benchmark = True` can pick worse algos for some mid-sized shapes+ when L2 is cleared between calls (the heuristic times candidates with hot L2).+ On this benchmark suite, that costs ~30 ms across all shapes vs cuDNN-default+ but saves ~2470 ms on the K=32 shape — net ~11x speedup on total wall time.++ TF32 stays off: reference is strict FP32, default-TF32 cuDNN diverges >1e-3+ on K=16 and K=32 shapes."""from task import input_t, output_timport torchimport torch.nn.functional as Ftorch.backends.cudnn.allow_tf32 = False+ torch.backends.cuda.matmul.allow_tf32 = False+ torch.backends.cudnn.benchmark = True+ torch.backends.cudnn.deterministic = Falsedef custom_kernel(data: input_t) -> output_t:
scrolls · 35 diff lines total
Best evidence level for this revision: reported
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