Compute architecture and scheduling
Abstract
This chapter introduces key concepts in the compute architectures of modern GPUs that are important to CUDA C programmers. It first gives an overview of the GPU execution resources, such as streaming multiprocessors (SMs). It then discusses how the blocks are assigned to SMs and divided into warps for scheduling purposes. It then gives more details about single-instruction, multiple-data execution hardware, warp scheduling, latency tolerance, control divergence, and effects of resource limitations. The chapter concludes with an introduction to the concept of resource queries.
Keywords
Thread scheduling; warp scheduling; linear layout of threads; control divergence; dynamic resource partitioning; ...
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