PyTorch eager
ConvTranspose3d MaxPool Softmax Subtract Swish Max · depth = 16 · width = 32 · height = 32 · batch_size = 128 · in_channels = 3 · fp32 · run 01a03f0e-f6d8…
●passedReported evidence
Not re-observed recently.
Primary measurement
5.68ms±0.01 · mean of 100
Rank 2 in its comparison group · source-native comparison · observed 2026-03-05
max ms 5.75 · min ms 5.65 · std ms 0.0259 · mean ms 5.68
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloaddepth = 16 · width = 32 · height = 32 · batch_size = 128 · in_channels = 3 · fp32
comparison keysha256:6360822a803ac3e2…
sourceKernelBench baseline timings
external idH100_Modal/torch/level2/89_ConvTranspose3d_MaxPool_Softmax_Subtract_Swish_Max.py
sha256:61f052c2ed9ac627cdc1049…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
depth16
width32
height32
batch_size128
in_channels3
input_1fp32 [128, 3, 16, 32, 32]
definition comparatornot_asserted
Measurements
latency · max5.75 ms · n=100
latency · mean5.68 ms · n=100
latency · min5.65 ms · n=100
latency · std25.9 µs
Protocol
harnessKernelBench timing scripts
timercuda_events
primaryStatisticmean
comparabilityFamilykernelbench_baseline_timing
Environment
gpuNVIDIA H100 (sm_90)
Artifacts
No artifacts published with this run.
Replications and notes
No attestations yet.
Community attestations. They never change the evidence level; only a KernelIndex-controlled rerun does.
Add a reproduction or note
Canonical manifest
Show manifestHide manifest
{
"run": {
"kind": "BenchmarkRun",
"spec": {
"status": "passed",
"timing": {
"samples": 100,
"latencyNs": {
"mean": 5680000,
"maximum": 5750000,
"minimum": 5650000,
"confidence95": [
5674924,
5685076
]
},
"primaryStatistic": "mean"
},
"observedAt": "2026-03-05T08:38:15.000Z",
"measurements": [
{
"unit": "ns",
"value": 25900,
"metric": "latency",
"statistic": "std"
}
],
"sourceNative": {
"source": "kernelbench",
"metrics": {
"max_ms": 5.75,
"min_ms": 5.65,
"std_ms": 0.0259,
"mean_ms": 5.68
},
"benchmark": "H100_Modal/baseline_time_torch.json",
"externalId": "H100_Modal/torch/level2/89_ConvTranspose3d_MaxPool_Softmax_Subtract_Swish_Max.py"
},
"protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
"workloadDigest": "sha256:10d7d39194b79ed59cea3b53ddfe7878b4f352fe6bd43cce47fc56825fe7d44b",
"environmentDigest": "sha256:2750ae381d6de582f1eb6b9008e0987463d19ede8af127d97d76c5ae09121393",
"implementationDigest": "sha256:02da96bb167c5134f4c72f0ec53f639d090e09c87c2f2d1f7eef6ca4f3afaa12"
},
"metadata": {
"name": "kernelbench-h100-modal-torch-l2-89-convtranspose3d-maxpool-softmax-subtract-swish-max",
"title": "ConvTranspose3d MaxPool Softmax Subtract Swish Max · PyTorch eager · Modal"
},
"apiVersion": "kernelindex.dev/v1alpha1"
},
"protocol": {
"kind": "BenchmarkProtocol",
"spec": {
"harness": {
"name": "KernelBench timing scripts",
"repository": "https://github.com/ScalingIntelligence/KernelBench"
},
"measurement": {
"timer": "cuda_events",
"primaryStatistic": "mean"
},
"comparability": {
"notes": "Mean of 100 timed forward passes (CUDA events, warm-up excluded) of the reference module on fixed inputs; the 95% interval is the mean's, from the reported standard deviation. The upstream JSON records no torch or CUDA version. Comparable only within one problem and one timing host.",
"family": "kernelbench_baseline_timing"
}
},
"metadata": {
"name": "kernelbench-timing-v1",
"title": "KernelBench baseline timing"
},
"apiVersion": "kernelindex.dev/v1alpha1"
},
"environment": {
"kind": "ExecutionEnvironment",
"spec": {
"hardware": {
"vendor": "nvidia",
"product": "NVIDIA H100",
"formFactor": "80GB HBM3",
"architecture": "sm_90"
},
"software": {}
},
"metadata": {
"name": "kernelbench-h100-modal",
"title": "KernelBench host · H100 80GB HBM3 (Modal)"
},
"apiVersion": "kernelindex.dev/v1alpha1"
}
}Cite this record (permalink, digest, access date)
Report an issue with this run
Published 2026-08-26 · KernelBench baseline timings · MITAll results for ConvTranspose3d MaxPool Softmax Subtract Swish Max →JSON