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
An optimized approach to histogram computation on GPU
Juan Gómez-Luna,José María González-Linares,J.I. Benavides,Nicolás Guil +3 more
- 01 Jul 2013
TL;DR: This paper proposes a highly optimized approach to histogram calculation that uses histogram replication for eliminating position conflicts, padding to reduce bank conflicts, and an improved access to input data called interleaved read access.
A Scalable Work-Efficient and Depth-Optimal Parallel Scan for the GPGPU Environment
Sang-Won Ha,Tack-Don Han +1 more
TL;DR: This study presents a parallel scan method that is derived from the Han-Carlson parallel prefix graph and is both a work-efficient and a depth-optimal process and employs a novel cascaded thread-block execution method to exploit the single-program-multiple- data (SPMD) nature of the compute unified device architecture (CUDA) environment developed by NVIDIA.
Exploiting Bank Conflict-based Side-channel Timing Leakage of GPUs
TL;DR: This article identifies a second finer-grained microarchitectural timing channel, related to the banking structure of the GPU’s Shared Memory, and develops a differential timing attack that can compromise table-based cryptographic algorithms.
Optimizing sparse tensor times matrix on GPUs
TL;DR: This work optimizes tensor-times-dense matrix multiply (Ttm) for general sparse and semi-sparse tensors on CPU and NVIDIA GPU platforms, and designs an in-place sequential SpTtm to avoid explicit data reorganizing between a tensor and a matrix in its conventional approach.
SNU-NPB 2019: Parallelizing and Optimizing NPB in OpenCL and CUDA for Modern GPUs
Youngdong Do,Hyungmo Kim,Pyeongseok Oh,Daeyoung Park,Jaejin Lee +4 more
- 01 Nov 2019
TL;DR: This work proposes a benchmark suite, called SNU-NPB 2019, which is based on NPB 3.3.1 and written in both OpenCL and CUDA, and introduces code parallelization/optimization techniques for modern GPUs that are applied to the benchmark programs and their performance characteristics.