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submission 545144

rajesh0042 · python · License unknown
Kernel source · 19 lines ↓

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Vendorable · source mirrored · license unknownView source →

No package. Vendor the mirrored source: 19 lines, June 9 Researcher Reciprocity License v1.0.

conv2d_v2.py
curl "https://kernelindex.com/api/v1/implementations/kernelbot-conv2d-v2-545144?include=source"
interfacepython
Compatibility
measured onNVIDIA H100
declared hardwareNVIDIA H100
architecturessm_90
dtypesfp32

Benchmark evidence

1 measurement across 1 GPU, fastest first.

Operation / workload
Hardware
Latency
Rank
Observed
2D convolutionsuite of 5 cases
NVIDIA H100
47.7ms
#6 of 35
2026-03-13
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Reported · How evidence levels are derived →

Source and license

sourceavailable
revision digestsha256:323dd45894e08d9bab4f4c804e4a142dd73768033589bbb5eabaf976e5b54d1b
license declaredunknown
license concludedunknown
authorsrajesh0042
imported2026-08-15

Kernel source

conv2d_v2.py19 lines
import os
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"

import torch
import torch.nn.functional as F
from task import input_t, output_t

# Deterministic for correctness, but with benchmark to find fastest algorithm
torch.backends.cudnn.allow_tf32 = False
torch.backends.cuda.matmul.allow_tf32 = False
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True  # Find fastest deterministic algo

def custom_kernel(data: input_t) -> output_t:
    input_tensor, kernel, output = data
    result = F.conv2d(input_tensor, kernel, stride=1, padding=0)
    output[...] = result
    return output

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 545078.

  ⋯ 4 unchanged lines
  import torch.nn.functional as F
  from task import input_t, output_t
   
- # Force deterministic behavior to match reference exactly
+ # Deterministic for correctness, but with benchmark to find fastest algorithm
  torch.backends.cudnn.allow_tf32 = False
  torch.backends.cuda.matmul.allow_tf32 = False
  torch.backends.cudnn.deterministic = True
- torch.backends.cudnn.benchmark = False
+ torch.backends.cudnn.benchmark = True  # Find fastest deterministic algo
   
  def custom_kernel(data: input_t) -> output_t:
      input_tensor, kernel, output = data

Best evidence level for this revision: reported

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