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
Memory-Efficient Parallel Simulation of Electron Beam Dynamics Using GPUs
Kamesh Arumugam,Desh Ranjan,Mohammad Zubair,Balsa Terzic,Alexander Godunov +4 more
- 01 Dec 2016
TL;DR: A novel cache-aware algorithm that uses a locality heuristic to maximize data reuse by improving data locality and minimizes threads divergence and enables reuse of partial results of previous iterations and thereby reducing the overall operation count is presented.
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Padding Free Bank Conflict Resolution for CUDA-Based Matrix Transpose Algorithm
Ayaz ul Hassan Khan,Mayez Al-Mouhamed,Allam Fatayer,Anas Almousa,Abdulrahman Ahmed Bobakr Baqais,Mohammed O. Assayony +5 more
TL;DR: Two matrix transpose algorithms are proposed to alleviate the issues of ensuring coalesced access and conflict free bank access and they eliminate bank conflicts while allocating shared memory exactly equal to the tile size of the problem space.
Optimization Techniques for Dimensionally Truncated Sparse Grids on Heterogeneous Systems
Andrei Deftu,Alin Murarasu +1 more
- 27 Feb 2013
TL;DR: This work describes the experience with implementing fastsg's time critical routines for Intel CPUs and Nvidia Fermi GPUs, and shows the differences and especially the similarities between the optimization strategies for the two architectures.
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Optimizing 3d convolutions for wavelet transforms on CPUs with SSE units and GPUs
Brice Videau,Vania Marangozova-Martin,Luigi Genovese,Thierry Deutsch +3 more
- 26 Aug 2013
TL;DR: This paper presents their optimizations for 3D convolutions in the BigDFT nanosimulation software, exploiting the relation between algorithmic specifics and hardware architecture to obtain performance gains of around x2 on CPU and up to x20 on GPU.
An Efficient Transaction-Based GPU Implementation of Minimum Spanning Forest Algorithm
Shayan Manoochehri,Bahareh Goodarzi,Dhrubajyoti Goswami +2 more
- 01 Jul 2017
TL;DR: This paper shows that a transaction- based design and implementation of the Boruvka's algorithm on GPU can handle some of the challenges arising due to irregularity and employs several optimization techniques in different phases of the algorithm to achieve load balance and enhanced GPU resource utilization.
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