Optimization Principles and Application Performance Evaluation of a Multithreaded GPU Using CUDA

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Optimization Principles and Application Performance Evaluation of a Multithreaded GPU Using CUDA (2008)

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by Shane Ryoo , Christopher I. Rodrigues , Sara S. Baghsorkhi , Sam S. Stone, et al.

Venue:	PPOPP'08
Citations:	215 - 11 self

BibTeX

@MISC{Ryoo08optimizationprinciples,
    author = {Shane Ryoo and Christopher I. Rodrigues and Sara S. Baghsorkhi and Sam S. Stone and et al.},
    title = {Optimization Principles and Application Performance Evaluation of a Multithreaded GPU Using CUDA },
    year = {2008}
}

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Abstract

GPUs have recently attracted the attention of many application developers as commodity data-parallel coprocessors. The newest generations of GPU architecture provide easier programmability and increased generality while maintaining the tremendous memory bandwidth and computational power of traditional GPUs. This opportunity should redirect efforts in GPGPU research from ad hoc porting of applications to establishing principles and strategies that allow efficient mapping of computation to graphics hardware. In this work we discuss the GeForce 8800 GTX processor’s organization, features, and generalized optimization strategies. Key to performance on this platform is using massive multithreading to utilize the large number of cores and hide global memory latency. To achieve this, developers face the challenge of striking the right balance between each thread’s resource usage and the number of simultaneously active threads. The resources to manage include the number of registers and the amount of on-chip memory used per thread, number of threads per multiprocessor, and global memory bandwidth. We also obtain increased performance by reordering accesses to off-chip memory to combine requests to the same or contiguous memory locations and apply classical optimizations to reduce the number of executed operations. We apply these strategies across a variety of applications and domains and achieve between a 10.5X to 457X speedup in kernel codes and between 1.16X to 431X total application speedup.

Keyphrases

multithreaded gpu using cuda abstract optimization principle application performance evaluation active thread traditional gpus large number off-chip memory contiguous memory location resource usage generalized optimization strategy executed operation right balance gpu architecture global memory bandwidth gtx processor organization global memory latency classical optimization graphic hardware efficient mapping tremendous memory bandwidth massive multithreading gpgpu research increased performance on-chip memory computational power commodity data-parallel coprocessors ad hoc porting kernel code many application developer total application speedup

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