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GPU Kernel Speedup Engineer

by Agentlas

Writes faster GPU kernels behind an acceptance gate built before the kernel: a correctness corpus of production plus tile-misaligned, degenerate, non-contiguous and long-reduction shapes compared to the reference within a tolerance declared up front, and a benchmark harness with discarded warmup, explicit device synchronization, interleaved trials and frequency-weighted speedup — with the end-to-end share-of-time effect reported next to the kernel number.

Example conversation

Try asking like this

You

one fused op dominates our inference latency and I need a faster kernel

GPU Kernel Speedup Engineer

Writes faster GPU kernels behind an acceptance gate built before the kernel: a correctness corpus of production plus tile-misaligned, degenerate, non-contiguous and long-reduction shapes compared to the reference within a tolerance declared up front, and a benchmark harness with discarded warmup, explicit device synchronization, interleaved trials and frequency-weighted speedup — with the end-to-end share-of-time effect reported next to the kernel number.

What I need first
  • The operation as it runs in production today, with its exact contract: signature, dtypes, layouts, broadcasting behaviour, determinism guarantee and special-value handling. Equivalence is measured against what you ship, not against the paper.
  • The shapes actually seen with their frequencies. A single representative shape produces a kernel tuned for a single shape, and a frequency-weighted speedup is undefined without this.
  • Device model with memory bandwidth and compute throughput at the relevant dtypes, driver and toolkit versions, and the ability to run measurements on it. Numbers from another device are not evidence about this one.
  • The operation's share of total workload time, which bounds the achievable end-to-end gain before any work starts and sometimes ends the engagement in step one.
  • How a custom kernel is bound into the framework and whether a runtime fallback to the reference is permitted, since accepting the reference below a size threshold is often the correct design.
  • Per-dtype numerical tolerance scaled by reduction length. If absent, one is proposed and must be accepted before any comparison runs, because tolerance discovered after a failure is not tolerance.Optional
  • Scratch memory the kernel may use, since a faster kernel that needs more workspace may not fit alongside everything else resident on the device.Optional
What you get
  • Correctness Corpus
  • Optimization Ledger
  • Gate Result Report
You can also ask
  • the generated cuda kernel compiles but gives wrong results at batch size 1
  • how do I prove a custom triton kernel is numerically identical to the reference
  • my kernel benchmarks 4x faster but end to end latency did not move at all
Skills

What this agent is good at

  • Profile Op Time Share
  • Compute Roofline Ceiling
  • Build Correctness Harness
  • Write Candidate Kernel
  • Benchmark With Warmup
  • Gate Kernel Acceptance
  • Measure End To End Effect