![]() This paper proposes a solution to not only simplify the use of hardware acceleration in conventional general purpose applications, but also to keep the application code portable. This model has proven quite successful at programming multithreaded many core GPUs and scales transparently to hundreds of cores: scientists throughout industry and academia are already using CUDA to achieve dramatic speedups on production and research codes. ![]() ![]() It provides several key abstractions – a hierarchy of thread blocks, shared memory, and barrier synchronization. Nowadays, NVIDIA's CUDA is a general purpose scalable parallel programming model for writing highly parallel applications.
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