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Parallel execution models that dispatch groups of compute invocations coordinated via memory barriers.
Distinct from High-Performance and Parallel Computing: Specifically targets GPU workgroup dispatch and synchronization, distinct from MPI-based distributed processes or general CPU task executors.
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This project provides a comprehensive toolset for WebGPU, serving as a graphics API wrapper, compute shader framework, resource manager, and shader toolchain. It enables browser-based GPU acceleration by offloading memory-intensive tasks and data processing from the CPU to the GPU. The framework manages the full lifecycle of GPU operations, from requesting physical hardware adapters and initializing logical devices to configuring programmable render and compute pipelines. It specifically supports the coordination of parallel workgroups and collective subgroup operations for general-purpose co
Dispatches groups of shader invocations to perform general-purpose computations and coordinate via memory barriers.
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
Distributes computations across multi-dimensional grids using workgroup-based parallel execution models.