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submission 555875

idanbeck · python · License unknown
Kernel source · 18 lines ↓

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Vendorable · source mirrored · license unknownView source →

No package. Vendor the mirrored source: 18 lines, June 9 Researcher Reciprocity License v1.0.

v8_module_config_direct.py
curl "https://kernelindex.com/api/v1/implementations/kernelbot-conv2d-v2-555875?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
2D convolutionsuite of 5 cases
NVIDIA A100
18.8ms
#8 of 40
2026-03-15
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Reported · How evidence levels are derived →

Source and license

sourceavailable
revision digestsha256:4989d7a33085b8949faf8932d9666eb97a8af41a49be99cdf922a8e639def0c9
license declaredunknown
license concludedunknown
authorsidanbeck
imported2026-08-15

Kernel source

v8_module_config_direct.py18 lines
#!POPCORN leaderboard conv2d_v2
#!POPCORN gpu A100
from task import input_t, output_t
import torch
import torch.nn.functional as F

torch.use_deterministic_algorithms(True)
torch.set_float32_matmul_precision("highest")
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = True
torch.backends.cuda.matmul.allow_tf32 = False
torch.backends.cudnn.allow_tf32 = False

@torch.inference_mode()
def custom_kernel(data: input_t) -> output_t:
    x,w,out=data
    return F.conv2d(x,w,stride=1,padding=0)

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 555595.

  ⋯ 3 unchanged lines
  import torch
  import torch.nn.functional as F
   
+ torch.use_deterministic_algorithms(True)
+ torch.set_float32_matmul_precision("highest")
+ torch.backends.cudnn.benchmark = True
+ torch.backends.cudnn.deterministic = True
+ torch.backends.cuda.matmul.allow_tf32 = False
+ torch.backends.cudnn.allow_tf32 = False
+  
  @torch.inference_mode()
  def custom_kernel(data: input_t) -> output_t:
-     input_tensor, kernel, output = data
-     torch.use_deterministic_algorithms(True)
-     torch.set_float32_matmul_precision("highest")
-     torch.backends.cudnn.benchmark = True
-     torch.backends.cudnn.deterministic = True
-     torch.backends.cuda.matmul.allow_tf32 = False
-     torch.backends.cudnn.allow_tf32 = False
-     output[...] = F.conv2d(input_tensor, kernel, stride=1, padding=0)
-     return output
+     x,w,out=data
+     return F.conv2d(x,w,stride=1,padding=0)
scrolls · 24 diff lines total

Best evidence level for this revision: reported

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