kernel-fusion

Burn is a next generation tensor library and Deep Learning Framework that doesn't compromise on flexibility, efficiency and portability.

AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

An agent harness that compiles a model into one provably-correct, self-retargeting CUDA megakernel and self-tunes it past cuBLAS at batch-1 LLM decode, paper: https://arxiv.org/abs/2606.09682

An efficient concurrent graph processing system

GoPTX: Fine-grained GPU Kernel Fusion by PTX-level Instruction Flow Weaving

Fused Triton kernels for TurboQuant KV cache compression — 2-4 bit quantization with RHT rotation. Drop-in HuggingFace & vLLM integration. Up to 4.9x KV cache compression for Llama, Qwen, Mistral, and more.

A complete GPT built entirely on NVIDIA cuTile: declarative GPU kernels in ~20 lines instead of ~150 hand-tuned CUDA, matching PyTorch speed in a ~10MB footprint

Noeris — autonomous kernel fusion discovery + Triton autotuning for LLM kernels and Gemma layer deeper fusion (A100/H100 wins).

LAMB go brrr

面向 Transformer 推理的可验证 Triton 融合算子与 torch.library 集成

MLX + Metal implementation of mHC: Manifold-Constrained Hyper-Connections by DeepSeek-AI.

A CUDA framework for separating graph-recoverable launch overhead from byte-elimination gains in LLM decode-chain fusion

Browser-native quantum chemistry in WebGPU and TypeScript. HF, UHF, DFT, MP2, CCSD, unrestricted CCSD and CCSD(T), with WGSL kernels for the CCSD(T) triples and the Fock build. EE/IP/EA-EOM-CCSD, Cholesky density fitting, statevector and MPS/DMRG. Cross-checked against PySCF, ITensor and brute-force Hamiltonians.

Assigment 3 for the "Parallel & Distributed Systems" course (ECE, AUTh) - Fall 2024

Fused and block-float ML kernels for Tenstorrent Tensix cores in TT-Lang and TT-Metalium C++, measured on Tenstorrent's simulators, with CI on simulated chips, containers, a Kubernetes JobSet sweep and Ansible provisioning.

A torch.compile backend that pays attention: custom FX fusion passes, a Triton LayerNorm kernel, and a persistent compile cache. Apache-2.0 free core of the g2n platform.

A lightweight ML compiler that generates PTX

2x faster Breeze TTS 2 inference with lossless CUDA kernels. Streaming text-to-speech at 4.3x real time, 32-38 ms to first audio, on one RTX 4080 SUPER. Same weights, no quantization, same HTTP API as the official --fast-all server.

Write the math. Get the kernel. Fused CUDA kernel generation from mathematical specifications.

GPU fusion code and algorithm