PyTorch eager
ConvTranspose3d AvgPool Clamp Softmax Multiply · depth = 32 · width = 64 · height = 64 · batch_size = 32 · in_channels = 32 · fp32 · run 01a03f0e-f6d4…
●passedReported evidence
Not re-observed recently.
Primary measurement
5.68ms±0.00 · mean of 100
Rank 2 in its comparison group · source-native comparison · observed 2026-03-05
max ms 5.93 · min ms 5.67 · std ms 0.025 · mean ms 5.68
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloaddepth = 32 · width = 64 · height = 64 · batch_size = 32 · in_channels = 32 · fp32
comparison keysha256:bb80e85ac9cc4cd5…
sourceKernelBench baseline timings
external idH100_Modal/torch/level2/38_ConvTranspose3d_AvgPool_Clamp_Softmax_Multiply.py
sha256:bd3ac5752bed45292cdaa6a…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
depth32
width64
height64
batch_size32
in_channels32
input_1fp32 [32, 32, 32, 64, 64]
definition comparatornot_asserted
Measurements
latency · max5.93 ms · n=100
latency · mean5.68 ms · n=100
latency · min5.67 ms · n=100
latency · std25.0 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": 5930000,
"minimum": 5670000,
"confidence95": [
5675100,
5684900
]
},
"primaryStatistic": "mean"
},
"observedAt": "2026-03-05T08:38:15.000Z",
"measurements": [
{
"unit": "ns",
"value": 25000,
"metric": "latency",
"statistic": "std"
}
],
"sourceNative": {
"source": "kernelbench",
"metrics": {
"max_ms": 5.93,
"min_ms": 5.67,
"std_ms": 0.025,
"mean_ms": 5.68
},
"benchmark": "H100_Modal/baseline_time_torch.json",
"externalId": "H100_Modal/torch/level2/38_ConvTranspose3d_AvgPool_Clamp_Softmax_Multiply.py"
},
"protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
"workloadDigest": "sha256:9215c7a8bd89c0d150b212e0e14cb7d06f1351f33b2da1c6f852f236c625f1be",
"environmentDigest": "sha256:2750ae381d6de582f1eb6b9008e0987463d19ede8af127d97d76c5ae09121393",
"implementationDigest": "sha256:ff6a42c75211278cbc2f1728c1c840f0fc3e40ef6be5de1cc022e9f51f54b2dc"
},
"metadata": {
"name": "kernelbench-h100-modal-torch-l2-38-convtranspose3d-avgpool-clamp-softmax-multiply",
"title": "ConvTranspose3d AvgPool Clamp Softmax Multiply · 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 AvgPool Clamp Softmax Multiply →JSON