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Reference implementations for maximizing GPU throughput and reducing execution latency.
Distinct from GPU & Performance: Provides concrete code samples for optimization rather than being a tool or a manual.
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This 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
Includes demonstrations for measuring memory bandwidth and using execution graphs to optimize performance.