3 repositorios
Awesome GitHub RepositoriesSIMT Execution Models
Hardware-software mapping where a single instruction is executed across many threads organized in grids and blocks.
Distinct from Multi-threaded Execution: Focuses on the SIMT (Single Instruction, Multiple Threads) hardware mapping, not general CPU multi-threading.
Explore 3 awesome GitHub repositories matching operating systems & systems programming · SIMT Execution Models. Refine with filters or upvote what's useful.
Awesome SIMT Execution Models GitHub Repositories
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- infrasys-ai/aisystem
Infrasys-AI/AISystem
17,017Ver en GitHubAISystem 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
Maps software threads to hardware using the Single Instruction, Multiple Threads (SIMT) execution model.
Jupyter Notebookaiaiinfraaisys - nvidia/cuda-samples
NVIDIA/cuda-samples
9,319Ver en GitHubThis repository is a collection of reference implementations and programming examples for the CUDA Toolkit. It serves as a GPGPU implementation guide and a parallel computing reference, providing code for using graphics hardware to perform general-purpose calculations and high-performance parallel processing. The project provides specific samples for GPU kernel development and resource management. These include demonstrations of multi-GPU communication, peer-to-peer memory access, and system hardware inspection to coordinate distributed GPU resources. The codebase covers a wide range of capa
Implements the SIMT model to run the same instruction across multiple threads for parallel processing of large datasets.
C++cudacuda-driver-apicuda-kernels - packtpublishing/learn-cuda-programming
PacktPublishing/Learn-CUDA-Programming
1,258Ver en GitHubEste proyecto sirve como un recurso educativo completo para aprender programación paralela y computación de alto rendimiento utilizando unidades de procesamiento gráfico (GPU). Proporciona guía técnica sobre los paradigmas fundamentales requeridos para delegar tareas computacionalmente intensivas desde un sistema host a aceleradores de hardware especializados. Los materiales cubren las metodologías centrales para gestionar operaciones de datos paralelos, incluyendo la orquestación de memoria entre espacios de host y dispositivo y la organización de hilos en rejillas y bloques estructurados. Detalla los modelos de ejecución necesarios para distribuir cargas de trabajo a través de múltiples núcleos de procesamiento, permitiendo a los desarrolladores escalar aplicaciones pesadas en datos de manera efectiva. Más allá de la implementación básica, el recurso incluye prácticas de diagnóstico para analizar métricas de ejecución e identificar cuellos de botella de rendimiento. Ofrece estrategias para optimizar la ejecución de kernels y depurar errores lógicos dentro de bases de código concurrentes para asegurar el máximo rendimiento y eficiencia en entornos de computación acelerada.
Executes identical instructions across multiple threads simultaneously to process large data arrays in parallel.
Cuda
