{"id":20490,"date":"2023-02-01T04:01:34","date_gmt":"2023-02-01T04:01:34","guid":{"rendered":"https:\/\/www.booksofall.com\/pl\/?post_type=product&p=20490"},"modified":"2023-02-01T04:01:57","modified_gmt":"2023-02-01T04:01:57","slug":"cuda-c-programming-guide","status":"publish","type":"product","link":"https:\/\/www.booksofall.com\/pl\/cuda-c-programming-guide\/","title":{"rendered":"CUDA C++ Programming Guide"},"content":{"rendered":"

The Graphics Processing Unit (GPU)<\/a> provides much higher instruction throughput and memory bandwidth than the CPU<\/a> 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<\/a>, are also very energy efficient but offer much less programming flexibility than GPUs.<\/p>\n

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).<\/p>\n

The GPU is specialized for highly parallel computations<\/a> and therefore designed such that more transistors are devoted to data processing<\/a> rather than data caching and flow control.<\/p>\n

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