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 parallel multi-objective particle swarm optimization for large swarms and high dimensional problems
Md. Maruf Hussain,Noriyuki Fujimoto +1 more
- 01 Apr 2020
TL;DR: The proposed parallel implementation of MOPSO using a master-slave model provides up to 157 times speedup compared to the corresponding CPU implementation, which can be widely used in real world optimization problems.
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Single Kernel Soft Synchronization Technique for Task Arrays on CUDA-enabled GPUs, with Applications
Shunji Funasaka,Koji Nakano,Yasuaki Ito +2 more
- 01 Nov 2017
TL;DR: The main contribution of this paper is to introduce task arrays and to present Single Kernel Soft Synchronization (SKSS) technique that significantly reduces such overheads for task arrays.
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Reconstructing permutation table to improve the Tabu Search for the PFSP on GPU
TL;DR: An efficient multiple-loop struct to generate most part of the permutation on the fly, which can decrease the size of permutation table and significantly reduce the amount of global memory access is proposed.
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Cache-Aware GPU Optimization for Out-of-Core Cone Beam CT Reconstruction of High-Resolution Volumes
TL;DR: This paper proposes a cache-aware optimization method to accelerate out-of-core cone beam computed tomography reconstruction on a graphics processing unit (GPU) device by increasing the cache hit rate so as to speed up the reconstruction of high-resolution volumes that exceed the capacity of device memory.
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Autotuning CUDA compiler parameters for heterogeneous applications using the OpenTuner framework
TL;DR: A Graphics Processing Unit (GPU) is a parallel computing coprocessor specialized in accelerating vector operations that solves the Algorithm Selection Problem using search and optimization techniques.
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