Merchandiser: Data Placement on Heterogeneous Memory for Task-Parallel HPC Applications with Load-Balance Awareness
Merchandiser: Data Placement on Heterogeneous Memory for Task-Parallel HPC Applications with Load-Balance Awareness
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DOI：
10.1145/3572848.3577497
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发表时间：
2023-02
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影响因子：
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通讯作者：
Zhen Xie;Jie Liu-;Jiajia Li;Dong Li
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作者：
Zhen Xie;Jie Liu-;Jiajia Li;Dong Li
异质记忆（HM）的出现为记忆耗尽的HPC应用程序提供了一种经济高效且高性能的解决方案。确定数据对象在HM上的放置对于高性能至关重要。我们揭示了与HM上数据放置有关的性能问题。该问题表现为任务并行HPC应用程序中任务之间的负载不平衡。该问题的根源来自不知道并行任务语义，而错误的假设是将经常访问的页面带到快速内存总是会带来更好的性能。为了解决这个问题，我们引入了一个名为Merchandiser的负载平衡感知页面管理系统。销售商在内存分析期间介绍了任务语义，而不是应用程序语义。使用有限的任务语义，商品可以有效地在使用HM的任务之间进行协调，以快速完成所有任务，而不仅仅是考虑任何个人任务。销售商具有高度自动化，以实现高可用性。通过记忆力消耗的HPC应用程序，我们表明，与基于硬件的解决方案和一种行业品质相比基于软件的解决方案。
The emergence of heterogeneous memory (HM) provides a cost-effective and high-performance solution to memory-consuming HPC applications. Deciding the placement of data objects on HM is critical for high performance. We reveal a performance problem related to data placement on HM. The problem is manifested as load imbalance among tasks in task-parallel HPC applications. The root of the problem comes from being unaware of parallel-task semantics and an incorrect assumption that bringing frequently accessed pages to fast memory always leads to better performance. To address this problem, we introduce a load balance-aware page management system, named Merchandiser. Merchandiser introduces task semantics during memory profiling, rather than being application-agnostic. Using the limited task semantics, Merchandiser effectively sets up coordination among tasks on the usage of HM to finish all tasks fast instead of only considering any individual task. Merchandiser is highly automated to enable high usability. Evaluating with memory-consuming HPC applications, we show that Merchandiser reduces load imbalance and leads to an average of 17.1% and 15.4% (up to 26.0% and 23.2%) performance improvement, compared with a hardware-based solution and an industry-quality software-based solution.