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Performance Potential of Mixed Data Management Modes for Heterogeneous Memory Systems...

by Timothy C Effler, Michael R Jantz, Terry R Jones
Publication Type
Conference Paper
Journal Name
IEEE/ACM Workshop on Memory Centric High Performance Computing
Book Title
Proceedings of MCHPC 2020: Workshop on Memory Centric High Performance Computing
Publication Date
Page Numbers
10 to 16
Publisher Location
District of Columbia, United States of America
Conference Name
IEEE/ACM Workshop on Memory Centric High Performance Computing (MCHPC)
Conference Location
Atlanta, Georgia, United States of America
Conference Sponsor
Conference Date

Many high-performance systems now include different types of memory devices within the same compute platform to meet strict performance and cost constraints. Such heterogeneous memory systems often include an upper-level tier with better performance, but limited capacity, and lower-level tiers with higher capacity, but less bandwidth and longer latencies for reads and writes. To utilize the different memory layers efficiently, current systems rely on hardware-directed, memory -side caching or they provide facilities in the operating system (OS) that allow applications to make their own data-tier assignments. Since these data management options each come with their own set of trade-offs, many systems also include mixed data management configurations that allow applications to employ hardware- and software-directed management simultaneously, but for different portions of their address space. Despite the opportunity to address limitations of stand-alone data management options, such mixed management modes are under-utilized in practice, and have not been evaluated in prior studies of complex memory hardware. In this work, we develop custom program profiling, configurations, and policies to study the potential of mixed data management modes to outperform hardware- or software-based management schemes alone. Our experiments, conducted on an Intel ® Knights Landing platform with high-bandwidth memory, demonstrate that the mixed data management mode achieves the same or better performance than the best stand-alone option for five memory intensive benchmark applications (run separately and in isolation), resulting in an average speedup compared to the best stand-alone policy of over 10 %, on average.