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
GPU-based Graph Traversal on Compressed Graphs
Mo Sha,Yuchen Li,Kian-Lee Tan +2 more
- 25 Jun 2019
TL;DR: This paper introduces GPU-based graph traversal on compressed graphs, designed towards GPU's SIMT architecture, and proposes two novel parallel scheduling strategies Two-Phase Traversal and Task-Stealing to handle thread divergence and workload imbalance issues when decoding the compressed graph.
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GPU optimized computation of stencil based algorithms
Lucian Mihai Itu,Constantin Suciu,Florin Moldoveanu,Adrian Postelnicu +3 more
- 23 Jun 2011
TL;DR: The approach described in the paper does not only represent a step forward for the steady state heat conduction problem but also for any other algorithm which performs the numerical solution of partial differential equations or which is stencil based.
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An Empirically Optimized Radix Sort for GPU
Bonan Huang,Jinlan Gao,Xiaoming Li +2 more
- 18 Aug 2009
TL;DR: An empirical optimization technique is proposed for one of the most important sorting routines on GPU, the radix sort, that generates highly efficient code for a number of representative NVIDIA GPUs with a wide variety of architectural specifications.
GPUSGD: A GPU-accelerated stochastic gradient descent algorithm for matrix factorization
TL;DR: This is the first work that develops a parallel SGD method to improve the matrix factorization on GPU and the experimental results show that GPUSGD performs much better in accelerating the matrix factorsization compared with the existing state‐of‐the‐art parallel methods.
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