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在FPGA中优化Sparse矩阵-向量乘法内核的性能,以屋顶线模型为指导
Federico Favaro1, Ernesto Dufrechou2, Juan P Oliver1
1Instituto de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de la República, Montevideo 11300, Uruguay.
Micromachines
|November 25, 2023
概括
本研究介绍了一种增强的屋顶线模型 (RLM),用于优化 Field-Programmable Gate Arrays (FPGA) 上的数值线性代数 (NLA) 内核. 新工具有助于提高稀疏矩阵计算的运行时间和能源效率.
科学领域:
- 高性能计算 高性能计算
- 计算机架构 计算机架构
- 数字分析 数字分析
背景情况:
- 现场可编程门阵列 (FPGA) 越来越多地用于高性能计算,作为多核处理器的节能替代品.
- 在FPGA上有效地实现数值线性代数 (NLA) 内核对于性能和能耗至关重要.
- 需要分析工具来理解和优化FPGAs上的NLA内核性能.
研究的目的:
- 提出一个扩展的屋顶线模型 (RLM) 来优化FPGA上的NLA内核.
- 为了提高NLA内核的运行时间和能源效率,使用稀疏的封锁存储格式.
- 为计算任务指导开发高效的FPGA实现.
主要方法:
- 扩展屋顶线模型 (RLM) 来纳入稀疏的封闭式存储格式.
- 在FPGA上实施优化的稀疏矩阵向量乘法 (SpMV) 内核.
- 利用区块分散存储格式来改善数据访问和计算效率.
主要成果:
- 扩展的RLM提供了一个分析和优化FPGA上的NLA内核的框架.
- 拟议的方法证明了SpMV内核的运行时间和能源消耗的改善.
- 区块分散式存储格式有助于更有效地访问数据,从而提高性能.
结论:
- 扩展的RLM是优化FPGA上的NLA内核的宝贵工具,平衡速度和能源效率.
- 通过优化存储格式和分析建模,NLA内核的FPGA实现可以实现显著的性能增长.
- 这项工作有助于在FPGA上推进节能高性能计算.
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