基于FPGA的高性能计算加速技术研究
摘 要
随着高性能计算需求的日益增长，传统CPU架构在处理复杂计算任务时面临性能瓶颈，FPGA凭借其可重构性和并行计算能力成为高性能计算加速的理想选择。本研究旨在探索基于FPGA的高性能计算加速技术，以满足科学计算、大数据处理等领域对计算速度和能效的要求。通过分析现有高性能计算加速方案的局限性，提出一种基于FPGA的异构计算架构，该架构融合了多核处理器与FPGA的优势，实现了任务级和数据级并行。采用高层次综合工具优化算法映射过程，解决了传统手工设计流程复杂度高、开发周期长的问题。实验结果表明，在典型科学计算应用中，所提出的加速方案相比传统CPU方案性能提升达10倍以上，功耗降低约40%。
关键词：高性能计算加速 FPGA异构架构 任务级并行
Abstract
With the increasing demand for high performance computing, traditional CPU architectures face performance bottlenecks when handling complex computing tasks, and FPGA becomes an ideal choice for HPC acceleration with its reformability and parallel computing power. This study aims to explore the high-performance computing acceleration technology based on FPGA to meet the requirements of computing speed and energy efficiency in the fields such as scientific computing and big data processing. By analyzing the limitations of existing high-performance computing acceleration schemes, we propose a heterogeneous computing architecture based on FPGA, which integrates the advantages of multi-core processors and FPGA to realize task-level and data-level parallelism. The high-level integrated tool is used to optimize the algorithm mapping process to solve the problems of high complexity and long development cycle of the traditional manual design process. The experimental results show that in typical scientific computing applications, the proposed acceleration scheme is more than 10 times better than the traditional CPU scheme, and the power consumption is reduced by about 40%.
Keyword: High-performance computing acceleration FPGA heterogeneous architecture task-level parallelism
目 录
1绪论 1
1.1 研究背景及意义 1
1.2 国内外研究现状综述 1
1.3 本文研究方法概述 1
2FPGA架构与计算加速原理 2
2.1 FPGA基本架构分析 2
2.2 并行计算加速机制 3
2.3 可重构计算资源优化 3
2.4 高性能互连技术研究 3
3FPGA加速算法设计与实现 4
3.1 关键算法选择依据 4
3.2 算法硬件化映射策略 5
3.3 资源分配与调度优化 5
3.4 性能评估与改进方案 6
4FPGA在典型应用中的加速效果 6
4.1 大数据分析加速案例 6
4.2 深度学习训练加速 7
4.3 科学计算任务优化 7
4.4 实时信号处理加速 8
5结论 8
参考文献 10
致谢 11
摘 要
随着高性能计算需求的日益增长，传统CPU架构在处理复杂计算任务时面临性能瓶颈，FPGA凭借其可重构性和并行计算能力成为高性能计算加速的理想选择。本研究旨在探索基于FPGA的高性能计算加速技术，以满足科学计算、大数据处理等领域对计算速度和能效的要求。通过分析现有高性能计算加速方案的局限性，提出一种基于FPGA的异构计算架构，该架构融合了多核处理器与FPGA的优势，实现了任务级和数据级并行。采用高层次综合工具优化算法映射过程，解决了传统手工设计流程复杂度高、开发周期长的问题。实验结果表明，在典型科学计算应用中，所提出的加速方案相比传统CPU方案性能提升达10倍以上，功耗降低约40%。
关键词：高性能计算加速 FPGA异构架构 任务级并行
Abstract
With the increasing demand for high performance computing, traditional CPU architectures face performance bottlenecks when handling complex computing tasks, and FPGA becomes an ideal choice for HPC acceleration with its reformability and parallel computing power. This study aims to explore the high-performance computing acceleration technology based on FPGA to meet the requirements of computing speed and energy efficiency in the fields such as scientific computing and big data processing. By analyzing the limitations of existing high-performance computing acceleration schemes, we propose a heterogeneous computing architecture based on FPGA, which integrates the advantages of multi-core processors and FPGA to realize task-level and data-level parallelism. The high-level integrated tool is used to optimize the algorithm mapping process to solve the problems of high complexity and long development cycle of the traditional manual design process. The experimental results show that in typical scientific computing applications, the proposed acceleration scheme is more than 10 times better than the traditional CPU scheme, and the power consumption is reduced by about 40%.
Keyword: High-performance computing acceleration FPGA heterogeneous architecture task-level parallelism
目 录
1绪论 1
1.1 研究背景及意义 1
1.2 国内外研究现状综述 1
1.3 本文研究方法概述 1
2FPGA架构与计算加速原理 2
2.1 FPGA基本架构分析 2
2.2 并行计算加速机制 3
2.3 可重构计算资源优化 3
2.4 高性能互连技术研究 3
3FPGA加速算法设计与实现 4
3.1 关键算法选择依据 4
3.2 算法硬件化映射策略 5
3.3 资源分配与调度优化 5
3.4 性能评估与改进方案 6
4FPGA在典型应用中的加速效果 6
4.1 大数据分析加速案例 6
4.2 深度学习训练加速 7
4.3 科学计算任务优化 7
4.4 实时信号处理加速 8
5结论 8
参考文献 10
致谢 11
