Awesome GitHub RepositoriesParallelizing Compilers

Genesis is an embodied AI simulation platform and parallelized robotics simulator designed for training general-purpose robotic agents. It integrates a physics engine for robotics that calculates collisions and movements for rigid bodies, soft tissues, and fluids, alongside a photorealistic 3D rendering engine. The platform features a domain randomization framework to vary environment parameters across parallel simulations, aiding in sim-to-real transfer. It supports the integration of real-world captured light fields and Gaussian splatting to provide photorealistic backgrounds within simulat

Bend is a high-level parallel programming language and compiler designed to execute code across multi-core CPUs and GPUs automatically. By translating functional source code into a graph-based intermediate representation, it enables massive parallel execution without requiring manual management of threads, locks, or atomic operations. The runtime operates as an interaction net engine, where computations are represented as networks of nodes that reduce through local rewriting rules. This model utilizes a work-stealing scheduler to distribute tasks across thousands of hardware threads, ensuring

Numba este un compilator just-in-time care traduce funcțiile Python de nivel înalt în cod mașină optimizat la runtime. Prin utilizarea infrastructurii de compilare LLVM, acesta oferă un framework pentru accelerarea procesării datelor numerice și a calculelor matematice, permițând niveluri de performanță comparabile cu limbajele compilate static. Proiectul se distinge prin capacitatea sa de a efectua specializarea bazată pe inferența de tipuri, care generează instrucțiuni mașină adaptate tipurilor de date specifice utilizate în timpul execuției. Acesta folosește un pipeline de compilare leneșă (lazy) care amână traducerea până în momentul invocării, minimizând timpul de pornire și menținând o performanță consistentă pe diverse arhitecturi de procesoare și sisteme de operare. Dincolo de compilarea de bază, toolkit-ul oferă suport extins pentru accelerarea hardware prin distribuirea operațiunilor iterative și a expresiilor de tip array pe mai multe nuclee CPU și unități de procesare grafică. Utilizează strategii de vectorizare și paralelizare pentru a maximiza debitul pentru seturi de date numerice la scară largă, permițând dezvoltatorilor să vizeze hardware specializat direct din codul standard.

IREE is an MLIR-based compiler toolchain and runtime designed to translate machine learning models from various frameworks into optimized binaries for execution across diverse hardware targets. It provides a unified pipeline to ingest models from PyTorch, TensorFlow, JAX, and ONNX, lowering them into a common intermediate representation for deployment on CPUs, GPUs, and bare-metal embedded systems. The project distinguishes itself through a bytecode virtual machine and a hardware abstraction layer that decouple high-level model logic from specific hardware instruction sets. It supports sophis

Tiramisu is a polyhedral C++ compiler framework designed to express and optimize data-parallel algorithms for diverse hardware accelerators. It provides a programming interface that allows developers to define mathematical expressions, manage loop iteration spaces, and organize functions targeting heterogeneous architectures. The system features an advanced compilation infrastructure that abstracts computations into a hardware-agnostic intermediate representation before lowering them into native machine code or hardware configuration bitstreams. It calculates exact data flow dependencies thro