Awesome GitHub RepositoriesSparse Computing Kernels

DeepSpeed is a high-performance library designed to scale deep learning model training and inference across massive clusters of GPUs and compute nodes. It provides a comprehensive suite of tools for distributed training, enabling the execution of models that exceed the memory capacity of single devices through advanced parameter partitioning, pipeline-based model parallelism, and memory-efficient state offloading. The framework distinguishes itself through specialized communication-efficient optimizers and hardware-aware acceleration techniques. By utilizing gradient compression, quantization

PyTorch Geometric is a library for building and training machine learning models on graph-structured data. It provides a framework for developing graph neural networks, including a specialized system for implementing node-to-node information exchange via customizable message passing, aggregation, and update functions. The library includes a sparse data processing toolkit that utilizes accelerated CPU and CUDA kernels to perform efficient reductions on large sparse datasets. It supports the creation of specialized architectures for structured data such as 3D meshes and point clouds. The proje

This project is a deep learning library designed for training neural networks on irregular data structures, including graphs, 3D meshes, and point clouds. It functions as an extension to the PyTorch framework, providing specialized layers and kernels that enable the processing of complex, non-Euclidean information. The library distinguishes itself through a geometric deep learning toolkit that manages the unique requirements of graph-based data. It utilizes sparse matrix-based message passing to aggregate information across nodes and employs dynamic computational graph construction to accommo

Ktransformers is a comprehensive framework designed for the operation, fine-tuning, and serving of large language models. It functions as a heterogeneous inference engine and quantized execution runtime, enabling the deployment of massive models by distributing computational workloads across both CPU and GPU resources. This architecture allows users to bypass local memory constraints, making it possible to run and train models that exceed the capacity of a single device. The project distinguishes itself through specialized support for sparse architectures, particularly mixture-of-experts mode

AISystem is a comprehensive AI full-stack infrastructure project covering the entire pipeline from AI chip architecture to high-level training frameworks. It encompasses the development of AI compiler frameworks, inference engines, and distributed training orchestrators designed to coordinate workloads across a heterogeneous compute stack of CPUs, GPUs, and NPUs. The project focuses on the deep integration of software and hardware, employing software-hardware co-design to align tensor layouts with physical memory structures. It provides specialized capabilities for accelerating Transformer mo

Amazon DSSTNE هي مجموعة أدوات لتعلم الآلة ومكتبة شبكات الموتر المتناثرة (sparse tensor) مصممة لنماذج التعلم العميق ذات المدخلات والمخرجات المتناثرة. توفر إطار عمل للتدريب المتوازي للنماذج ومحركاً متناثراً مسرعاً بواسطة GPU لدعم الشبكات كثيفة الذاكرة. تم تصميم إطار العمل خصيصاً لتدريب أنظمة التوصية والتعلم المتناثر واسع النطاق. يتيح توزيع مصفوفات الأوزان الكبيرة وجداول التضمين (embedding tables) عبر أجهزة GPU متعددة للتعامل مع النماذج التي تتجاوز سعة ذاكرة معالج واحد. يغطي المشروع مجموعة واسعة من الإمكانيات بما في ذلك الحوسبة الموزعة على GPU، ومعالجة مجموعات البيانات المتناثرة، وبناء شبكات الموتر المتناثرة القابلة للتوسع. تسمح هذه الأدوات بتنفيذ عمليات تعلم الآلة عالية الأداء وتوسيع نطاق النماذج عبر عناقيد GPU.

oneDNN is a library for deep learning acceleration that provides optimized building blocks for neural network training and inference. It manages tensor computation across CPU and GPU hardware, enabling the execution of high-performance primitives for model training and neural network inference optimization. The project distinguishes itself through hardware-specific kernel optimization and the use of just-in-time compilation to target specific processor instruction sets. It supports quantized neural network execution using both static and dynamic quantization to reduce memory usage and increas

Neuraloperator is a library for learning mappings between infinite-dimensional function spaces, serving as a tool to accelerate physics simulations and partial differential equation solving. It implements resolution-invariant models and spectral neural networks that can produce consistent predictions regardless of the input grid resolution or spatial discretization. The framework incorporates physics-informed neural networks that enforce physical constraints and differential equations through specialized loss functions. It utilizes Fourier transforms and spectral projections to process multid