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
Scalable SIMD-Efficient Graph Processing on GPUs
Farzad Khorasani,Rajiv Gupta,Laxmi N. Bhuyan +2 more
- 18 Oct 2015
TL;DR: Warp Segmentation is presented, a novel method that greatly enhances GPU device utilization by dynamically assigning appropriate number of SIMD threads to process a vertex with irregular-sized neighbors while employing compact CSR representation to maximize the graph size that can be kept inside the GPU global memory.
A survey of techniques for optimizing deep learning on GPUs
Sparsh Mittal,Shraiysh Vaishay +1 more
TL;DR: A survey of architecture and system-level techniques for optimizing DL applications on GPUs for both inference and training and for both single GPU and distributed system with multiple GPUs is presented.
156
yaSpMV: yet another SpMV framework on GPUs
Shengen Yan,Chao Li,Yunquan Zhang,Huiyang Zhou +3 more
- 06 Feb 2014
TL;DR: A new SpMV format is devised, called blocked compressed common coordinate (BCCOO), which uses bit flags to store the row indices in a blocked common coordinate format so as to alleviate the bandwidth problem and an auto-tuning framework is introduced to choose optimization parameters based on the characteristics of input sparse matrices and target hardware platforms.
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Optimizing Sparse Matrix—Matrix Multiplication for the GPU
TL;DR: The implementation is fully general and the optimization strategy adaptively processes the SpGEMM workload row-wise to substantially improve performance by decreasing the work complexity and utilizing the memory hierarchy more effectively.