Software
Collection
Software (GitHub) datasets I've created
•
4 items
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Updated
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|---|---|---|---|---|---|
goose
|
https://github.com/block/goose
|
agent
|
an open source, extensible AI agent that goes beyond code suggestions - install, execute, edit, and test with any LLM
|
https://block.github.io/goose
|
ai-agents
|
ray
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https://github.com/ray-project/ray
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ai compute engine
|
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
|
https://ray.io
|
machine-learning
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flashinfer-bench
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https://github.com/flashinfer-ai/flashinfer-bench
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benchmark
|
Building the Virtuous Cycle for AI-driven LLM Systems
|
https://bench.flashinfer.ai
|
benchmark
|
KernelBench
|
https://github.com/ScalingIntelligence/KernelBench
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benchmark
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KernelBench: Can LLMs Write GPU Kernels? - Benchmark with Torch -> CUDA problems
|
https://scalingintelligence.stanford.edu/blogs/kernelbench
|
benchmark
|
SWE-bench
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https://github.com/SWE-bench/SWE-bench
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benchmark
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SWE-bench: Can Language Models Resolve Real-world Github Issues?
|
https://swebench.com
|
benchmark
|
terminal-bench
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https://github.com/laude-institute/terminal-bench
|
benchmark
|
A benchmark for LLMs on complicated tasks in the terminal
|
https://tbench.ai
|
benchmark
|
TritonBench
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https://github.com/thunlp/TritonBench
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benchmark
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TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators
|
https://arxiv.org/abs/2502.14752
|
benchmark
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BitBLAS
|
https://github.com/microsoft/BitBLAS
|
Basic Linear Algebra Subprograms (BLAS)
|
BitBLAS is a library to support mixed-precision matrix multiplications, especially for quantized LLM deployment.
| null |
matrix-multiplication
|
hipBLAS
|
https://github.com/ROCm/hipBLAS
|
Basic Linear Algebra Subprograms (BLAS)
|
[DEPRECATED] Moved to ROCm/rocm-libraries repo
| null |
matrix-multiplication
|
hipBLASLt
|
https://github.com/AMD-AGI/hipBLASLt
|
Basic Linear Algebra Subprograms (BLAS)
|
hipBLASLt is a library that provides general matrix-matrix operations with a flexible API and extends functionalities beyond a traditional BLAS library
|
https://rocm.docs.amd.com/projects/hipBLASLt
|
matrix-multiplication
|
AdaptiveCpp
|
https://github.com/AdaptiveCpp/AdaptiveCpp
|
compiler
|
Compiler for multiple programming models (SYCL, C++ standard parallelism, HIP/CUDA) for CPUs and GPUs from all vendors: The independent, community-driven compiler for C++-based heterogeneous programming models. Lets applications adapt themselves to all the hardware in the system - even at runtime!
|
https://adaptivecpp.github.io
|
compiler
|
llvm-project
|
https://github.com/llvm/llvm-project
|
compiler
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The LLVM Project is a collection of modular and reusable compiler and toolchain technologies.
|
http://llvm.org
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compiler
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numba
|
https://github.com/numba/numba
|
compiler
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NumPy aware dynamic Python compiler using LLVM
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https://numba.pydata.org
|
compiler
|
nvcc4jupyter
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https://github.com/andreinechaev/nvcc4jupyter
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compiler
|
A plugin for Jupyter Notebook to run CUDA C/C++ code
| null | null |
CU2CL
|
https://github.com/vtsynergy/CU2CL
|
CUDA / OpenCL
|
A prototype CUDA-to-OpenCL source-to-source translator, built on the Clang compiler framework
|
http://chrec.cs.vt.edu/cu2cl
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opencl
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cuda-python
|
https://github.com/NVIDIA/cuda-python
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CUDA / OpenCL
|
CUDA Python: Performance meets Productivity
|
https://nvidia.github.io/cuda-python
| null |
OpenCL-SDK
|
https://github.com/KhronosGroup/OpenCL-SDK
|
CUDA / OpenCL
|
OpenCL SDK
| null |
opencl
|
pocl
|
https://github.com/pocl/pocl
|
CUDA / OpenCL
|
pocl - Portable Computing Language
|
https://portablecl.org
|
opencl
|
SYCL-Docs
|
https://github.com/KhronosGroup/SYCL-Docs
|
CUDA / OpenCL
|
SYCL Open Source Specification
| null |
opencl
|
triSYCL
|
https://github.com/triSYCL/triSYCL
|
CUDA / OpenCL
|
Generic system-wide modern C++ for heterogeneous platforms with SYCL from Khronos Group
| null |
opencl
|
ZLUDA
|
https://github.com/vosen/ZLUDA
|
CUDA / OpenCL
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CUDA on non-NVIDIA GPUs
|
https://vosen.github.io/ZLUDA
|
cuda
|
llama.cpp
|
https://github.com/ggml-org/llama.cpp
|
inference engine
|
LLM inference in C/C++
|
https://ggml.ai
|
inference
|
mistral-inference
|
https://github.com/mistralai/mistral-inference
|
inference engine
|
Official inference library for Mistral models
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https://mistral.ai
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llm-inference
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ollama
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https://github.com/ollama/ollama
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inference engine
|
Get up and running with OpenAI gpt-oss, DeepSeek-R1, Gemma 3 and other models.
|
https://ollama.com
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inference
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sglang
|
https://github.com/sgl-project/sglang
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inference engine
|
SGLang is a fast serving framework for large language models and vision language models.
|
https://docs.sglang.ai
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inference
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TensorRT
|
https://github.com/NVIDIA/TensorRT
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inference engine
|
NVIDIA TensorRT is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
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https://developer.nvidia.com/tensorrt
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inference
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vllm
|
https://github.com/vllm-project/vllm
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inference engine
|
A high-throughput and memory-efficient inference and serving engine for LLMs
|
https://docs.vllm.ai
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inference
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kernels
|
https://github.com/huggingface/kernels
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gpu kernels
|
Load compute kernels from the Hub
| null | null |
kernels-community
|
https://github.com/huggingface/kernels-community
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gpu kernels
|
Kernel sources for https://huggingface.co/kernels-community
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https://huggingface.co/kernels-community
| null |
Liger-Kernel
|
https://github.com/linkedin/Liger-Kernel
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kernel examples
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Efficient Triton Kernels for LLM Training
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https://openreview.net/pdf?id=36SjAIT42G
|
triton
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quack
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https://github.com/Dao-AILab/quack
|
kernel examples
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A Quirky Assortment of CuTe Kernels
| null | null |
reference-kernels
|
https://github.com/gpu-mode/reference-kernels
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kernel examples
|
Official Problem Sets / Reference Kernels for the GPU MODE Leaderboard!
|
https://gpumode.com
|
gpu
|
pytorch
|
https://github.com/pytorch/pytorch
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machine learning framework
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Tensors and Dynamic neural networks in Python with strong GPU acceleration
|
https://pytorch.org
|
machine-learning
|
tensorflow
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https://github.com/tensorflow/tensorflow
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machine learning framework
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An Open Source Machine Learning Framework for Everyone
|
https://tensorflow.org
|
machine-learning
|
torchdendrite
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https://github.com/sandialabs/torchdendrite
|
machine learning framework
|
Dendrites for PyTorch and SNNTorch neural networks
| null | null |
onnx
|
https://github.com/onnx/onnx
|
machine learning interoperability
|
Open standard for machine learning interoperability
|
https://onnx.ai
|
onnx
|
executorch
|
https://github.com/pytorch/executorch
|
model compiler
|
On-device AI across mobile, embedded and edge for PyTorch
|
https://executorch.ai
|
compiler
|
cutlass
|
https://github.com/NVIDIA/cutlass
|
parallel computing
|
CUDA Templates and Python DSLs for High-Performance Linear Algebra
|
https://docs.nvidia.com/cutlass/index.html
|
parallel-programming
|
ThunderKittens
|
https://github.com/HazyResearch/ThunderKittens
|
parallel computing
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Tile primitives for speedy kernels
|
https://hazyresearch.stanford.edu/blog/2024-10-29-tk2
|
parallel-programming
|
helion
|
https://github.com/pytorch/helion
|
parallel computing dsl
|
A Python-embedded DSL that makes it easy to write fast, scalable ML kernels with minimal boilerplate.
|
https://helionlang.com
|
parallel-programming
|
TileIR
|
https://github.com/microsoft/TileIR
|
parallel computing dsl
|
TileIR (tile-ir) is a concise domain-specific IR designed to streamline the development of high-performance GPU/CPU kernels (e.g., GEMM, Dequant GEMM, FlashAttention, LinearAttention). By employing a Pythonic syntax with an underlying compiler infrastructure on top of TVM, TileIR allows developers to focus on productivity without sacrificing the low-level optimizations necessary for state-of-the-art performance.
| null |
parallel-programming
|
tilelang
|
https://github.com/tile-ai/tilelang
|
parallel computing dsl
|
Domain-specific language designed to streamline the development of high-performance GPU/CPU/Accelerators kernels
|
https://tilelang.com
|
parallel-programming
|
triton
|
https://github.com/triton-lang/triton
|
parallel computing dsl
|
Development repository for the Triton language and compiler
|
https://triton-lang.org
|
parallel-programming
|
cupti
|
https://github.com/cwpearson/cupti
|
performance testing
|
Profile how CUDA applications create and modify data in memory.
| null |
profiling
|
hatchet
|
https://github.com/LLNL/hatchet
|
performance testing
|
Graph-indexed Pandas DataFrames for analyzing hierarchical performance data
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https://llnl-hatchet.readthedocs.io
|
profiling
|
intelliperf
|
https://github.com/AMDResearch/intelliperf
|
performance testing
|
Automated bottleneck detection and solution orchestration
|
https://arxiv.org/html/2508.20258v1
|
profiling
|
omnitrace
|
https://github.com/ROCm/omnitrace
|
performance testing
|
Omnitrace: Application Profiling, Tracing, and Analysis
|
https://rocm.docs.amd.com/projects/omnitrace
|
profiling
|
jax
|
https://github.com/jax-ml/jax
|
scientific computing
|
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
|
https://docs.jax.dev
|
scientific-computing
|
numpy
|
https://github.com/numpy/numpy
|
scientific computing
|
The fundamental package for scientific computing with Python.
|
https://numpy.org
|
scientific-computing
|
scipy
|
https://github.com/scipy/scipy
|
scientific computing
|
SciPy library main repository
|
https://scipy.org
|
scientific-computing
|
elasticsearch
|
https://github.com/elastic/elasticsearch
|
search engine
|
Free and Open Source, Distributed, RESTful Search Engine
|
https://elastic.co/products/elasticsearch
|
search-engine
|
jupyterlab
|
https://github.com/jupyterlab/jupyterlab
|
user interface
|
JupyterLab computational environment.
|
https://jupyterlab.readthedocs.io
|
jupyter
|
milvus
|
https://github.com/milvus-io/milvus
|
vector database
|
Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search
|
https://milvus.io
|
vector-search
|
accelerate
|
https://github.com/huggingface/accelerate
|
training framework
|
A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support.
|
https://huggingface.co/docs/accelerate
|
gpu-acceleration
|
aiter
|
https://github.com/ROCm/aiter
|
ai tensor engine
|
AI Tensor Engine for ROCm
|
https://rocm.blogs.amd.com/software-tools-optimization/aiter-ai-tensor-engine/README.html
|
gpu-acceleration
|
ao
|
https://github.com/pytorch/ao
| null |
PyTorch native quantization and sparsity for training and inference
|
https://pytorch.org/ao
|
quantization
|
burn
|
https://github.com/tracel-ai/burn
| null |
Burn is a next generation tensor library and Deep Learning Framework that doesn't compromise on flexibility, efficiency and portability.
|
https://burn.dev
|
machine-learning
|
ccache
|
https://github.com/ccache/ccache
| null |
ccache - a fast compiler cache
|
https://ccache.dev
| null |
ComfyUI
|
https://github.com/comfyanonymous/ComfyUI
|
user interface
|
The most powerful and modular diffusion model GUI, api and backend with a graph/nodes interface.
|
https://comfy.org
|
stable-diffusion
|
composable_kernel
|
https://github.com/ROCm/composable_kernel
|
gpu kernels
|
Composable Kernel: Performance Portable Programming Model for Machine Learning Tensor Operators
|
https://rocm.docs.amd.com/projects/composable_kernel
| null |
cudnn-frontend
|
https://github.com/NVIDIA/cudnn-frontend
| null |
cudnn_frontend provides a c++ wrapper for the cudnn backend API and samples on how to use it
| null | null |
cuJSON
|
https://github.com/AutomataLab/cuJSON
|
library leveraging parallel compute
|
cuJSON: A Highly Parallel JSON Parser for GPUs
| null |
json-parser
|
DeepSpeed
|
https://github.com/deepspeedai/DeepSpeed
|
training framework
|
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
|
https://deepspeed.ai
|
gpu-acceleration
|
dstack
|
https://github.com/dstackai/dstack
|
gpu provisioning and orchestration
|
dstack is an open-source control plane for running development, training, and inference jobs on GPUs-across hyperscalers, neoclouds, or on-prem.
|
https://dstack.ai
|
orchestration
|
flashinfer
|
https://github.com/flashinfer-ai/flashinfer
|
gpu kernels
|
FlashInfer: Kernel Library for LLM Serving
|
https://flashinfer.ai
|
attention
|
FTorch
|
https://github.com/Cambridge-ICCS/FTorch
| null |
A library for directly calling PyTorch ML models from Fortran.
|
https://cambridge-iccs.github.io/FTorch
|
machine-learning
|
GEAK-agent
|
https://github.com/AMD-AGI/GEAK-agent
|
agent
|
It is an LLM-based AI agent, which can write correct and efficient gpu kernels automatically.
| null |
ai-agents
|
hhvm
|
https://github.com/facebook/hhvm
| null |
A virtual machine for executing programs written in Hack.
|
https://hhvm.com
|
hack
|
hip
|
https://github.com/ROCm/hip
| null |
HIP: C++ Heterogeneous-Compute Interface for Portability
|
https://rocmdocs.amd.com/projects/HIP
|
hip
|
hipCUB
|
https://github.com/ROCm/hipCUB
| null |
[DEPRECATED] Moved to ROCm/rocm-libraries repo
|
https://github.com/ROCm/rocm-libraries
| null |
IMO2025
|
https://github.com/harmonic-ai/IMO2025
|
formal mathematical reasoning
|
Harmonic's model Aristotle achieved gold medal performance, solving 5 problems. This repository contains the lean statement files and proofs for Problems 1-5.
|
https://harmonic.fun
|
lean
|
kubernetes
|
https://github.com/kubernetes/kubernetes
|
container orchestration
|
Production-Grade Container Scheduling and Management
|
https://kubernetes.io
|
kubernetes
|
lapack
|
https://github.com/Reference-LAPACK/lapack
| null |
LAPACK development repository
| null |
linear-algebra
|
lean4
|
https://github.com/leanprover/lean4
|
theorem prover
|
Lean 4 programming language and theorem prover
|
https://lean-lang.org
|
lean
|
letta
|
https://github.com/letta-ai/letta
|
agent
|
Letta is the platform for building stateful agents: open AI with advanced memory that can learn and self-improve over time.
|
https://docs.letta.com
|
ai-agents
|
lightning-thunder
|
https://github.com/Lightning-AI/lightning-thunder
| null |
PyTorch compiler that accelerates training and inference. Get built-in optimizations for performance, memory, parallelism, and easily write your own.
| null | null |
LMCache
|
https://github.com/LMCache/LMCache
| null |
Supercharge Your LLM with the Fastest KV Cache Layer
|
https://lmcache.ai
|
inference
|
mcp-agent
|
https://github.com/lastmile-ai/mcp-agent
|
mcp
|
Build effective agents using Model Context Protocol and simple workflow patterns
| null |
mcp
|
metaflow
|
https://github.com/Netflix/metaflow
| null |
Build, Manage and Deploy AI/ML Systems
|
https://metaflow.org
|
machine-learning
|
MIOpen
|
https://github.com/ROCm/MIOpen
| null |
[DEPRECATED] Moved to ROCm/rocm-libraries repo
|
https://github.com/ROCm/rocm-libraries
| null |
modelcontextprotocol
|
https://github.com/modelcontextprotocol/modelcontextprotocol
| null |
Specification and documentation for the Model Context Protocol
|
https://modelcontextprotocol.io
| null |
modular
|
https://github.com/modular/modular
| null |
The Modular Platform (includes MAX & Mojo)
|
https://docs.modular.com
|
mojo
|
monarch
|
https://github.com/meta-pytorch/monarch
| null |
PyTorch Single Controller
|
https://meta-pytorch.org/monarch
| null |
Mooncake
|
https://github.com/kvcache-ai/Mooncake
| null |
Mooncake is the serving platform for Kimi, a leading LLM service provided by Moonshot AI.
|
https://kvcache-ai.github.io/Mooncake
|
inference
|
nccl
|
https://github.com/NVIDIA/nccl
| null |
Optimized primitives for collective multi-GPU communication
|
https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/index.html
| null |
neuronx-distributed-inference
|
https://github.com/aws-neuron/neuronx-distributed-inference
| null | null | null | null |
nixl
|
https://github.com/ai-dynamo/nixl
| null |
NVIDIA Inference Xfer Library (NIXL)
| null | null |
ome
|
https://github.com/sgl-project/ome
| null |
OME is a Kubernetes operator for enterprise-grade management and serving of Large Language Models (LLMs)
|
http://docs.sglang.ai/ome
|
k8s
|
ondemand
|
https://github.com/OSC/ondemand
| null |
Supercomputing. Seamlessly. Open, Interactive HPC Via the Web
|
https://openondemand.org
|
hpc
|
oneDPL
|
https://github.com/uxlfoundation/oneDPL
| null |
oneAPI DPC++ Library (oneDPL)
|
https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/dpc-library.html
| null |
openevolve
|
https://github.com/codelion/openevolve
| null |
Open-source implementation of AlphaEvolve
| null |
genetic-algorithm
|
ort
|
https://github.com/pytorch/ort
| null |
Accelerate PyTorch models with ONNX Runtime
| null | null |
peft
|
https://github.com/huggingface/peft
| null |
PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
|
https://huggingface.co/docs/peft
|
lora
|
Primus-Turbo
|
https://github.com/AMD-AGI/Primus-Turbo
| null | null | null | null |
pybind11
|
https://github.com/pybind/pybind11
| null |
Seamless operability between C++11 and Python
|
https://pybind11.readthedocs.io
|
bindings
|
RaBitQ
|
https://github.com/gaoj0017/RaBitQ
| null |
[SIGMOD 2024] RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search
|
https://github.com/VectorDB-NTU/RaBitQ-Library
|
nearest-neighbor-search
|
rdma-core
|
https://github.com/linux-rdma/rdma-core
| null |
RDMA core userspace libraries and daemons
| null |
linux-kernel
|
rocFFT
|
https://github.com/ROCm/rocFFT
| null |
[DEPRECATED] Moved to ROCm/rocm-libraries repo
|
https://github.com/ROCm/rocm-libraries
|
hip
|
ROCm
|
https://github.com/ROCm/ROCm
| null |
AMD ROCm Software - GitHub Home
|
https://rocm.docs.amd.com
|
documentation
|
rocm-systems
|
https://github.com/ROCm/rocm-systems
| null |
super repo for rocm systems projects
| null | null |
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