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1. Introduction — CUDA C++ Programming Guide

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The Graphics Processing Unit (GPU)1 provides much higher instruction throughput and memory bandwidth than the CPU within a similar price and power envelope. Many applications leverage these higher capabilities to run faster on the GPU than on the CPU (see GPU Applications). Other computing devices, like FPGAs, are also very energy efficient, but offer much less programming flexibility than GPUs. This difference in capabilities between the GPU and the CPU exists because they are designed with different goals in mind. While the CPU is designed to excel at executing a sequence of operations, called a thread, as fast as possible and can execute a few tens of these threads in parallel, the GPU is designed to excel at executing thousands of them in parallel (amortizing the slower single-thread performance to achieve greater throughput). The GPU is specialized for highly parallel computations and therefore designed such that more transistors are devoted to data processing rather than data cac

CUDA C++ Programming Guide (Legacy) — CUDA C++ Programming Guide » CUDA C++ Programming Guide (Legacy) v13.3 | PDF | Archive CUDA C++ Programming Guide (Legacy) Warning This document has been replaced by a new CUDA Programming Guide . The information in this document should be considered legacy, and this document is no longer being updated as of CUDA 13.0. Please refer to the CUDA Programming Guide for up-to-date information on CUDA. 1. Overview  CUDA is a parallel computing platform and programming model developed by NVIDIA that enables dramatic increases in computing performance by harnessi

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