gpuemu vs the alternatives
The first question gpuemu gets is "isn't this just assert_close?" Here's how it
compares to the tools people reach for — and the gap each one leaves.
gpuemu vs torch.allclose / torch.testing.assert_close
torch.allclose is the field-standard kernel correctness check — one shape, one dtype, one seed. In a measured 26-op corpus it caught 0/9 LLM-style bugs. gpuemu's operator-aware regime caught 9/9.
Read the comparison →gpuemu vs NVIDIA Compute Sanitizer
Compute Sanitizer (memcheck, racecheck, synccheck) finds memory and synchronization bugs. It is blind to silent numerical wrong-output — the kernel that runs cleanly and returns the wrong answer. gpuemu covers exactly that gap.
Read the comparison →gpuemu vs KernelBench, TritonBench, GEAK, and the LLM-kernel benchmarks
KernelBench, TritonBench, GEAK, KernelBand, and STARK are leaderboards for LLM-generated kernels — and they use the same one-shape allclose oracle inside. gpuemu is the user-facing correctness tool that oracle should have been.
Read the comparison →gpuemu vs framework fuzzers (FreeFuzz, DocTer, NablaFuzz, FuzzGPT)
API-level deep-learning fuzzers test the framework's Python API surface. gpuemu tests the kernel. The distinction matters: an ACL TOSEM 2025 study measured these fuzzers at a 6.5% real-world bug catch rate.
Read the comparison →Stop shipping silently-wrong kernels
Open source, dual-licensed MIT / Apache-2.0. Validate your first kernel in five minutes — or talk to us about an enterprise pilot.