Runtime Data Management on Non-Volatile Memory-based Heterogeneous Memory for Task-Parallel Programs

Non-volatile memory (NVM) provides a scalable solution to replace DRAM as main memory. Because of relatively high latency and low bandwidth of NVM (comparing with DRAM), NVM often pairs with DRAM to build a heterogeneous main memory system (HMS). Deciding data placement on NVM-based HMS is critical to enable future NVM-based HPC. In this paper, we study task-parallel programs, and introduce a runtime system to address the data placement problem on NVM-based HMS. Leveraging semantics and execution mode of task-parallel programs, we efficiently characterize memory access patterns of tasks and reduce data movement overhead. We also introduce a performance model to predict performance for tasks with various data placements on HMS. Evaluating with a set of HPC benchmarks, we show that our runtime system achieves higher performance than a conventional HMS-oblivious runtime (24% improvement on average) and two state-of-the-art HMS-aware solutions (16% and 11% improvement on average, respectively).