Average Pooling 3D
4 eligible runs
pooling
KernelBench level1 problem 46: Average Pooling 3D. The computation is the reference PyTorch module's forward pass; the output shape follows the module (mirrored as each implementation's source).
Source baseline · unbeaten
8.69ms±0.05 · mean of 100
PyTorch eagerPyTorch · MIT · python
Reported evidence · last observed 2026-03-05. The source's designated baseline implementation. Reported by source; not independently reproduced.
Current records
HardwareBest knownImplementationRuns
NVIDIA H100 · env 18.69 msPyTorch eager2NVIDIA H100 · env 211.3 msPyTorch eager2Not measured on B200 for this workload. Challenges →
Source-native comparison · GPU NVIDIA H100 · Workload depth = 128 · width = 256 · height = 128 · channels = 32 · batch_size = 16 · fp32 · Protocol KernelBench timing scripts · mean · 2 results · last observed 2026-03-05Record history →
Estimated floor 2.56 ms · record 3.39× above itestimate, not evidence ›
DRAM 2.56 ms · bandwidth-bound on H100 SXM
every declared tensor crosses HBM exactly once (3,350 GB/s, H100 SXM datasheet)
no arithmetic formula for this family: bandwidth floor only
headroom-v1: a lower bound from declared tensors and datasheet peaks. A kernel can sit well above it for good reasons.
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Implementation
Latency
vs #1
Trust
Observed
18.69ms±0.051.00×Reported · MIT · source2026-03-05stale
Measured exactly what you asked. The source's designated baseline implementation. Reported by source; not independently reproduced.
source mirroredMITno install recipeView source →Run detail →
2torch.compile (inductor)PyTorch8.70ms±0.031.00×Reported · MIT · source2026-03-05stale
1.00× slower than the baseline. Measured exactly what you asked. Reported by source; not independently reproduced.
source mirroredMITno install recipeView source →Run detail →
Implementations
Implementation
Runtime
Best latency
Evidence
Availability
Semantics
Inputs and outputs
xfp32 [batch_size, channels, depth, height, width]
outfloat [out]
Axes and behavior
outvariable
depthvariable
widthvariable
heightvariable
channelsvariable
batch_sizevariable
determinismunspecified
constraintsNo mutation or aliasing
Identity
sha256ab0f5f50de96…
Sources: KernelBench baseline timings (2026-03-05) · MITlast observed 2026-03-05How records are decidedJSON