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HardTanh

4 eligible runs
activation

KernelBench level1 problem 32: HardTanh. The computation is the reference PyTorch module's forward pass; the output shape follows the module (mirrored as each implementation's source).

Fastest reported
4.29ms±0.03 · mean of 100 · 1.01× faster than baseline
torch.compile (inductor)PyTorch · MIT · python

Reported evidence · last observed 2026-03-05. Reported by source; not independently reproduced.

No package · vendor the source →View source →Run detail →

Current records

HardwareBest knownImplementationRuns
NVIDIA H100 · env 14.29 mstorch.compile (inductor)2NVIDIA H100 · env 26.95 msPyTorch eager2

Not measured on B200 for this workload. Challenges →

Source-native comparison · GPU NVIDIA H100 · Workload dim = 393216 · batch_size = 4096 · fp32 · Protocol KernelBench timing scripts · mean · 2 results · last observed 2026-03-05Record history →
Estimated floor 1.92 ms · record 2.23× above itestimate, not evidence ›
DRAM 1.92 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.
#
Implementation
Latency
vs #1
Trust
Observed
1
torch.compile (inductor)PyTorch
4.29ms±0.03
1.00×
Reported · MIT · source
2026-03-05stale

1.01× faster than the baseline. Measured exactly what you asked. Reported by source; not independently reproduced.

source mirroredMITno install recipeView source →Run detail →
2
PyTorch eagerbaselinePyTorch
4.34ms±0.04
1.01×
Reported · MIT · source
2026-03-05stale

Measured exactly what you asked. The source's designated baseline implementation. Reported by source; not independently reproduced.

source mirroredMITno install recipeView source →Run detail →

Implementations

Implementation
Runtime
Best latency
Evidence
Availability
PyTorch eagerPyTorch
python · torch_eager
4.34ms
1.01×
Reported
MIT · source
torch.compile (inductor)PyTorch
python · torch_compile_inductor
4.29ms
1.00×
Reported
MIT · source

Semantics

Inputs and outputs
xfp32 [batch_size, dim]
outfloat [out]
Axes and behavior
dimvariable
outvariable
batch_sizevariable
determinismunspecified
constraintsNo mutation or aliasing
Identity
sha256e21ee1c47114…
Sources: KernelBench baseline timings (2026-03-05) · MITlast observed 2026-03-05How records are decidedJSON