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
OpenCL™ FFT Optimizations for Intel® Processor Graphics
Dan Petre,Adam T. Lake,Allen Hux +2 more
- 19 Apr 2016
TL;DR: A 1D, multi-kernel, mixed-radix Cooley-Tukey power of two algorithms that improves upon clFFT for many cases under consideration, and achieves high-thread occupancy and high memory bandwidth for a wide range of global and local sizes.
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A compiler approach to map algebra: automatic parallelization, locality optimization, and GPU acceleration of raster spatial analysis
TL;DR: This work designs and implements a framework that uses compiler techniques to automatically speed up raster spatial analysis and reorders the raster operations to maximize data reuse and minimize memory movements, which, as it is shown, is key to the performance of rasters spatial analysis.
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Auto-Tuning GEMV on Many-Core GPU
Weizhi Xu,Zhiyong Liu,Jun Wu,Xiaochun Ye,Shuai Jiao,Da Wang,Fenglong Song,Dongrui Fan +7 more
- 17 Dec 2012
TL;DR: A novel register blocking method that uses a warp to compute an element of vector y and a performance-tuning framework on how to choose an optimal algorithm of GEMV for an arbitrary input matrix on GPU.
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RegTT: Accelerating Tree Traversals on GPUs by Exploiting Regularities
Feng Zhang,Peng Di,Hao Zhou,Xiangke Liao,Jingling Xue +4 more
- 01 Aug 2016
TL;DR: A new parallelization approach, RegTT, is introduced to orchestrate an efficient execution of a tree traversal algorithm on GPUs by starting with BFT (Breadth-First Traversal), then reordering the queries being processed (based on their truncation histories), and finally, switching to DFT (Depth-FirstTraversal).
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Optimized Password Recovery for Encrypted RAR on GPUs
Xiaojing An,Haipeng Jia,Yunquan Zhang +2 more
- 24 Aug 2015
TL;DR: This paper adopts coarse granularity parallel for SHA-1 hashing, which means that one GPU thread is responsible for the validation of one password, and mainly uses four optimization methods to optimize this parallel version: asynchronous parallel between CPU and GPU, redundant calculations and conditional statements reduction, data locality by using LDS, and the usage of registers optimization.