基于异构计算的并行算法设计与优化
摘 要
随着信息技术的迅猛发展，数据量呈指数级增长，传统计算模式面临性能瓶颈，异构计算凭借其多核CPU与GPU等加速器协同工作的特性成为解决大规模数据处理问题的有效途径。基于此背景，本文聚焦于基于异构计算的并行算法设计与优化，旨在充分利用异构系统硬件资源，提高算法执行效率。研究中采用理论分析与实验验证相结合的方法，针对典型应用场景构建数学模型，深入剖析任务划分、负载均衡、数据传输等关键环节对算法性能的影响机制。创新性地提出一种动态自适应任务调度策略，依据任务特征及设备状态实时调整任务分配，减少空闲等待时间；同时设计高效的数据预取与缓存管理机制，降低数据传输延迟。
关键词：异构计算 并行算法优化 任务调度策略
Abstract
With the rapid development of information technology, the amount of data is growing exponentially, and the traditional computing mode is facing performance bottlenecks. Heterogeneous computing has become an effective way to solve the problem of large-scale data processing with its multi-core CPU and GPU accelerators. Based on this background, this paper focuses on the design and optimization of the parallel algorithm based on heterogeneous computing, aiming to make full use of the hardware resources of the heterogeneous system and improve the efficiency of the algorithm execution. In the study, the method of combining theoretical analysis and experimental verification is used to build a mathematical model for typical application scenarios, and the influence mechanism of key links such as task division, load balancing and data transmission on the algorithm performance is deeply analyzed. A dynamic adaptive task scheduling strategy to adjust task allocation according to task characteristics and equipment status to reduce idle waiting time and design efficient data prefetch and cache management mechanism to reduce data transmission delay.
Keyword:Heterogeneous Computing Parallel Algorithm Optimization Task Scheduling Strategies
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3本文研究方法 2
2异构计算平台特性分析 2
2.1异构计算架构概述 2
2.2主流异构平台比较 3
2.3平台对算法设计的影响 3
3并行算法设计原理 4
3.1并行化基本概念 4
3.2异构环境下的任务划分 4
3.3数据依赖性分析 5
4算法优化策略研究 6
4.1性能瓶颈识别 6
4.2资源分配优化 7
4.3通信开销最小化 7
结论 8
参考文献 9
致谢 10
摘 要
随着信息技术的迅猛发展，数据量呈指数级增长，传统计算模式面临性能瓶颈，异构计算凭借其多核CPU与GPU等加速器协同工作的特性成为解决大规模数据处理问题的有效途径。基于此背景，本文聚焦于基于异构计算的并行算法设计与优化，旨在充分利用异构系统硬件资源，提高算法执行效率。研究中采用理论分析与实验验证相结合的方法，针对典型应用场景构建数学模型，深入剖析任务划分、负载均衡、数据传输等关键环节对算法性能的影响机制。创新性地提出一种动态自适应任务调度策略，依据任务特征及设备状态实时调整任务分配，减少空闲等待时间；同时设计高效的数据预取与缓存管理机制，降低数据传输延迟。
关键词：异构计算 并行算法优化 任务调度策略
Abstract
With the rapid development of information technology, the amount of data is growing exponentially, and the traditional computing mode is facing performance bottlenecks. Heterogeneous computing has become an effective way to solve the problem of large-scale data processing with its multi-core CPU and GPU accelerators. Based on this background, this paper focuses on the design and optimization of the parallel algorithm based on heterogeneous computing, aiming to make full use of the hardware resources of the heterogeneous system and improve the efficiency of the algorithm execution. In the study, the method of combining theoretical analysis and experimental verification is used to build a mathematical model for typical application scenarios, and the influence mechanism of key links such as task division, load balancing and data transmission on the algorithm performance is deeply analyzed. A dynamic adaptive task scheduling strategy to adjust task allocation according to task characteristics and equipment status to reduce idle waiting time and design efficient data prefetch and cache management mechanism to reduce data transmission delay.
Keyword:Heterogeneous Computing Parallel Algorithm Optimization Task Scheduling Strategies
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3本文研究方法 2
2异构计算平台特性分析 2
2.1异构计算架构概述 2
2.2主流异构平台比较 3
2.3平台对算法设计的影响 3
3并行算法设计原理 4
3.1并行化基本概念 4
3.2异构环境下的任务划分 4
3.3数据依赖性分析 5
4算法优化策略研究 6
4.1性能瓶颈识别 6
4.2资源分配优化 7
4.3通信开销最小化 7
结论 8
参考文献 9
致谢 10
