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-Accelerated Bulk Execution of Multiple-Length Multiplication with Warp-Synchronous Programming Technique
TL;DR: A GPU implementation of bulk multiple-length multiplications to adopt a warp-synchronous programming technique and attains a speed-up factor of 52 for 1024-bit multiplelength multiplication over the sequential CPU implementation.
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Performance Analysis and Optimization for MTTKRP of Sparse Tensor on CPU and GPU
Rong Hu,Yang Wangdong,Xu Zhou,Kenli Li,Keqin Li +4 more
- 01 Dec 2020
TL;DR: Wang et al. as discussed by the authors improved the computational efficiency of MTTKRP based on the GPU parallel technology by using a tensor compressed format that eliminates redundant computational steps and enhances parallelism for the computing characteristics.
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Algorithm Flattening: Complete branch elimination for GPU requires a paradigm shift from CPU thinking
Lucas Vespa,Alexander Bauman,Jenny Wells +2 more
- 12 Nov 2015
TL;DR: Algorithm Flattening (AF) is presented, a de-optimization for CPU which completely removes all branches, and results in a significant optimization for GPU accelerated applications, which will have a significant impact on high performance computing.
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Dynamic memory optimization and parallelism management for OpenCL
Chao-Hung Hsu,I-Wei Wu,Jean Jyh-Jiun Shann +2 more
- 26 Apr 2014
TL;DR: A compilation pass to automatically perform optimizations for OpenCL kernels is proposed, which will transform an input naïve kernel function with optimizations, including kernel function analysis, work-group rearrangement, memory coalescing, and work-item merge.
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Optimization of Ray-Tracing Algorithm for Simulation of PMD Sensors
Sangita Lade,Purva S Kulkarni,Prasad Saraf,Purva Nartam,Aniket Patil +4 more
- 01 Jan 2021
TL;DR: In this article, various optimization approaches are proposed which are generic across all CUDA GPUs along with the merits and demerits for ray-tracing algorithm for simulation of photonic mixer devices (PMD) sensors.
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