GPU Profiler for AI agents
An agentic system that auto-optimizes LLM workloads on AMD GPUs.
[DEPRECATED] Moved to ROCm/rocm-systems repo
AgentKernelArena is a platform for evaluating and training AI agents for GPU kernel optimization, combining realistic tasks, correctness validation, and performance-based rewards for reinforcement learning.
Production-Grade Autoresearch. Ideal for agent harness engineering, prompt engineering, ML model development, GPU kernels, and other optimizable code.
Open source skill library for AI coding agents to write, optimize, and debug high performance compute kernels across CUDA, Triton, and quantized workloads.
Online CUDA Occupancy Calculator
Windows port of GreenBoost - GPU VRAM extension with system RAM & NVMe for larger LLMs. Original by Ferran Duarri (GPL v2).
Agents, and RL environment, for optimizing GPU kernels on AMD ROCm using LLM agents. Benchmarks LLM serving workloads end-to-end, profiles bottleneck kernels, optimizes them via Claude Code or Codex, and scores on compilation, correctness, and speedup.
(Spring 2017) Assignment 2: GPU Executor
Source code and documentation for Flash-Aurora porject.
Minimal FlashAttention in CUDA C++/CuTe: readable WMMA/CuTe kernels, no NxN workspace, up to 4.5x faster than naive PyTorch
Runs a single CUDA/OpenCL kernel, taking its source from a file and arguments from the command-line
GPU Drano Static Analysis for GPU programs.
Run a coding model at 300+ tok/s on your Mac. Native Rust + Metal inference engine for LiquidAI LFM2.5-24B-A2B (MLX 4-bit) on Apple Silicon.
Apple Silicon (Metal) backend for Triton: write standard @triton.jit kernels on your Mac GPU. The same source runs bit-identical on NVIDIA and AMD, so you develop kernel logic locally and rent a datacenter GPU only for the perf pass.
[COLM-LLA 2026] The official implementation for paper "AdaExplore: Failure-Driven Adaptation and Diversity-Preserving Search for Efficient Kernel Generation"
🔥 The living map of LLM-driven kernel generation: 280+ agents, models, benchmarks, and industry systems across NVIDIA, AMD, Ascend, TPU, and more. Continuously updated.
Prototype for a SPIR-V assembler and dissasembler. It provides a composable Java interface for generating SPIR-V code at runtime.
native blackwell training backward for gated linear recurrences, and a verifier that catches broken llm-generated gpu kernels
To associate your repository with the gpu-kernels topic, visit your repo's landing page and select "manage topics."