Optimization Techniques for GPU Programming
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TL;DR: In this article , a survey discusses various optimization techniques found in 450 articles published in the last 14 years and analyzes the optimizations from different perspectives which shows that the various optimizations are highly interrelated, explaining the need for techniques such as auto-tuning.
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Abstract: In the past decade, Graphics Processing Units have played an important role in the field of high-performance computing and they still advance new fields such as IoT, autonomous vehicles, and exascale computing. It is therefore important to understand how to extract performance from these processors, something that is not trivial. This survey discusses various optimization techniques found in 450 articles published in the last 14 years. We analyze the optimizations from different perspectives which shows that the various optimizations are highly interrelated, explaining the need for techniques such as auto-tuning.
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References
Gunrock: a high-performance graph processing library on the GPU
Yangzihao Wang,Andrew Davidson,Yuechao Pan,Yuduo Wu,Andy Riffel,John D. Owens +5 more
- 24 Jan 2015
TL;DR: This work evaluates Gunrock on five graph primitives and shows that Gunrock has at least an order of magnitude speedup over Boost and PowerGraph, comparable performance to the fastest GPU hardwired primitives, and better performance than any other GPU high-level graph library.
Dense linear algebra solvers for multicore with GPU accelerators
Stanimire Tomov,Rajib Nath,Hatem Ltaief,Jack Dongarra +3 more
- 19 Apr 2010
TL;DR: This work describes how to code/develop solvers to effectively use the high computing power available in these new and emerging hybrid architectures of dense linear algebra (DLA) for multicore with GPU accelerators, and develops newly developed DLA solvers.
CUDASW++: optimizing Smith-Waterman sequence database searches for CUDA-enabled graphics processing units
TL;DR: The CUDASW++ implementation provides a significant performance improvement for Smith-Waterman-based protein sequence database searches by fully exploiting the compute capability of commonly used CUDA-enabled low-cost GPUs.
Automatically tuning sparse matrix-vector multiplication for GPU architectures
Alexander Monakov,Anton Lokhmotov,Arutyun Avetisyan +2 more
- 25 Jan 2010
TL;DR: In this paper, a new storage format for sparse matrices is presented, which employs locality, has low memory footprint and enables automatic specialization for various matrices and future devices via parameter tuning.
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A Survey on optimized implementation of deep learning models on the NVIDIA Jetson platform
TL;DR: A survey of works that evaluate and optimize neural network applications on Jetson platform, which seeks to provide a glimpse of the recent progress towards that goal and shows the real-life applications where these algorithms have been applied.
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