大规模并行系统中的动态缓冲管理:随机性的力量
Minh Pham1, Yongke Yuan2, Hao Li1
1University of South Florida, USA.
ACM transactions on parallel computing
|February 24, 2025
概括
本研究介绍了使用随机搜索的图形处理单元 (GPU) 的新型动态内存分配方法,显著减少延迟并提高并行计算任务的性能.
科学领域:
- 计算机科学 计算机科学
- 高性能计算 高性能计算
- 系统软件 系统软件
背景情况:
- 像图形处理单元 (GPU) 这样的大规模并行系统对于数据密集型计算至关重要.
- 为众多并行线程开发高效的系统软件带来了独特的挑战.
- 传统的动态内存分配器在并行环境中与全球数据结构瓶作斗争.
研究的目的:
- 为大规模并行系统提出一种新的动态内存分配方法,避免集中式数据结构.
- 为了提高内存分配效率和降低GPU计算中的延迟.
- 通过先进的内存管理来提高GPU算法的性能.
主要方法:
- 开发了一个动态内存分配策略,利用线程的随机搜索程序来定位空白的内存页面.
- 设计了先进的技术来解决曲线分歧,并在有限的自由内存条件下优化性能.
- 集成和评估在GPU算法中提出的内存管理技术,如哈希连接和按组分组.
主要成果:
- 在大多数场景中,基本的随机搜索设计与现有解决方案相比,显示了较低的延迟.
- 先进的技术实现了基本设计的数量级改进.
- 在性能增长方面,最先进的技术总是以高达两倍的数量表现出色.
- 案例研究显示,GPU散列连接和分组算法显著提高了性能.
结论:
- 提出的基于随机搜索的动态内存分配有效地消除了大规模并行系统中集中数据结构的瓶.
- 先进的技术提供了显著的性能提升,显著超过当前最先进的内存管理解决方案.
- 实际集成到GPU算法中验证了该方法的有效性和在高性能计算中的广泛适用性.
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