Optimization Techniques for GPU Programming
54
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.
read more
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.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Unleashing the potential: AI empowered advanced metasurface research
Yunlai Fu,Xuxi Zhou,Yiwan Yu,Jiawang Chen,Shuming Wang,Shining Zhu,Zhenlin Wang +6 more
TL;DR: AI-powered advanced metasurface research explores the intersection of AI and metasurfaces, leveraging AI's computational power to design, analyze, and optimize metasurfaces for various applications.
GPU acceleration of Levenshtein distance computation between long strings
TL;DR: In this paper , a GPU implementation of the WFA algorithm and a new optimization that can halve the elements to be computed, providing additional performance gains, are presented, which is the best ever reported.
7
Progress and Opportunities of Foundation Models in Bioinformatics
Qing Li,Zhihang Hu,Yixuan Wang,Lei Li,Yimin Fan,Irwin King,Le Song,Yu Li +7 more
TL;DR: A systematic investigation and summary of FMs in bioinformatics, tracing their evolution, current research status, and the methodologies employed, aiming to guide the research community in choosing appropriate FMs for their research needs.
6
Sustainable Optimizing Performance and Energy Efficiency in Proof of Work Blockchain: A Multilinear Regression Approach
Meennapa Rukhiran,Songwut Boonsong,Paniti Netinant +2 more
TL;DR: The results reveal that strategically adjusting GPU hardware, software, and configuration can preserve substantial energy while preserving computational efficiency, and offer practical recommendations for optimizing the feature configurations of GPUs to reduce energy consumption, mitigate the environmental impacts of blockchain operations, and contribute to the current research on performance in PoW blockchain applications.
6
References
Faster and Cheaper: Parallelizing Large-Scale Matrix Factorization on GPUs
Wei Tan,Liangliang Cao,Liana Fong +2 more
- 31 May 2016
TL;DR: The CUDA-based matrix factorization library cuMF as mentioned in this paper optimizes alternate least square (ALS) method to solve very large-scale MF problems on single and multiple GPUs.
Data-Driven Versus Topology-driven Irregular Computations on GPUs
Rupesh Nasre,Martin Burtscher,Keshav Pingali +2 more
- 20 May 2013
TL;DR: Data-driven and topology-driven implementations of six important graph algorithms on GPUs are studied to understand the tradeoffs between these implementations and how to optimize them and devise hybrid approaches that combine the two techniques and outperform both of them.
CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment.
Svetlin A Manavski,Giorgio Valle +1 more
TL;DR: The results show that graphic cards are now sufficiently advanced to be used as efficient hardware accelerators for sequence alignment, and their performance is better than any alternative available on commodity hardware platforms.
In-Place Matrix Transposition on GPUs
Juan Gómez-Luna,I-Jui Sung,Li-Wen Chang,José María González-Linares,Nicolás Guil,Wen-mei W. Hwu +5 more
TL;DR: This paper proposes low-level optimizations for the elementary transpositions, and finds the best performing configurations for them, and compares the method to another recent implementation of in-place matrix transposition for GPUs.
A review of CUDA optimization techniques and tools for structured grid computing
TL;DR: The basic architectural optimizations and implementations in research and industry compilers are presented and a set of tools with the main optimization functionalities for an integrated library are proposed to ease the process of defining complex SGC data structure and optimizing solver code using intelligent high-level interface and domain specific annotations.