Merchandiser: Data Placement on Heterogeneous Memory for Task-Parallel HPC Applications with Load-Balance Awareness

Zhen Xie, Jie Liu, Jiajia Li, Dong Li

Abstract: 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.