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
Histogram optimization with CUDA
Keh Kok Yong,Sheera Shaheera Othman Talib +1 more
- 01 Nov 2016
TL;DR: The result shows the GPU optimized streaming histogram is able to gain 7x more speedup against the multi-threaded OpenMP implementation.
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Accelerating Support Count for Association Rule Mining on GPUs
Vasileios Zois,Anand Panangadan,Viktor K. Prasanna +2 more
- 23 May 2016
TL;DR: A compressed data layout scheme that enables high off-chip memory bandwidth utilization and results in low overhead parallel coordination while reducing the memory requirements of support count is developed.
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Exploring the parallel capabilities of GPU: Berlekamp-Massey algorithm case study
TL;DR: This research exploits optimization techniques for CUDA enabled GPU architecture in order to achieve the best possible performance for Berlekamp-Massey Algorithm as a case study.
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Performance optimization for CPU-GPU heterogeneous parallel system
Yanhua Wang,Jianzhong Qiao,Shukuan Lin,Tinglei Zhao +3 more
- 01 Aug 2016
TL;DR: The proposed task allocation model can achieve up to 23.43% of performance improvement compared to some states of the art allocation strategies averagely and is evaluated by implemented on a real heterogeneous system and several benchmarks.
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Performance Optimization of Light-Field Applications on GPU
TL;DR: This work proposes an optimization technique which redesigns the memory access pattern of the applications to alleviate the memory bottleneck of rendering application and to increase the data reusability for depth extraction application.
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