# Show HN: Auto GPU Kernel – Autonomous GPU-kernel discovery and optimizer

> Source: <https://github.com/Dogacel/auto-gpu-kernel>
> Published: 2026-05-26 04:23:12+00:00

Autonomous GPU-kernel discovery & optimizer.

Ranked #1 on [MLSys 2026 - FlashInfer AI Kernel Generation Contest](https://mlsys26.flashinfer.ai/) for the *DeepSeek Sparse Attention (DSA)* track with an average speedup of 34.93x. Submissions can be found at:

| Kernel | Runtime (ms) |
|---|---|
|

[dsa_topk_indexer_fp8_h64_d128_topk2048_ps64](/Dogacel/auto-gpu-kernel/blob/main/dsa_topk_indexer_fp8_h64_d128_topk2048_ps64)— DSA TopK IndexerCopy the `template`

directory into a separate folder / git repository to make sure your agents work in an isolated environment.

The kernel agent is compatible with [FlashInfer](https://github.com/flashinfer-ai/flashinfer) format and can run without a local GPU on cloud using [Modal](https://modal.com/). Requires [Claude Code CLI](https://use-claude.com/index).

```
# Python env
conda create -n fi-bench python=3.12
conda activate fi-bench
pip install flashinfer-bench modal

# One-time environment setup
modal setup
modal volume create flashinfer-trace
modal volume put flashinfer-trace /path/to/flashinfer-trace/
```

To get started clone the [MLSys-2026 Contest Dataset](https://huggingface.co/datasets/flashinfer-ai/mlsys26-contest). To change the kernel you are implementing, please refer to the [FlashInfer-Trace - Bring Your Own Kernel](https://bench.flashinfer.ai/docs/tutorials/bring-your-own-kernel) guide.

Important

Make sure you update `CLAUDE.md`

to describe the kernel you are optimizing. The example in template is customized for sparse attention. Also `optimize.md`

and `benchmark.md`

has some parameters tuned for sparse attention such as number of test cases to run to get a sanity check. You can ask an agent to help you adjsut them.

To run one iteration,

```
claude --dangerously-skip-permissions -p "/optimize"
```

Or you can launch interactive mode by running `claude --dangerously-skip-permissions`

, selecting the right model, thinking mode and enter `/loop Run /optimize every 15 minutes`

.

That's it. The loop runs indefinitely, each iteration picks one optimization, benchmarks it, logs an experiment folder, and continues. Stop with `Ctrl+C`

when you want to step in. As agent struggles to find new optimizations, it will start to change its schedule to be less frequent.

For more details on the agentic loop, please refer to the technical report.

Agents:

- Profiler
- Research
- Workload inspector

| Command | Purpose |
|---|---|
`/optimize` |
Main loop |
`/benchmark <quick|stride N|full>` |
One-shot Modal run |
`/log-experiment` |
Snapshot + write `result.md` + update index |

See `CLAUDE.md`

for rules and `.claude/commands/`

for full command specs.

`solution/triton/sparse_fused.py`

— the kernel being optimized (overwritten each iteration)`experiments/exp_N/`

— snapshot + results for iteration N`experiments/summary.md`

— master index, one row per iteration`experiments/LESSONS.md`

— durable cross-experiment findings