Performance impact of a slower main memory: a case study of STT-MRAM in HPC
- Publisher: ACM
Processors and memory architectures | Supercomputadors | Massively parallel and high-performance simulations | Supercomputers | Computer storage devices | Ordinadors--Dispositius de memòria | High-performance computing. | STT-MRAM | :Enginyeria electrònica [Àrees temàtiques de la UPC] | Càlcul intensiu (Informàtica) | Main memory | :Informàtica::Arquitectura de computadors [Àrees temàtiques de la UPC] | Non-volatile memory | High performance computing | Ordinadors -- Dispositius de memòria | Supercomputers--Programming | Memory--Computer simulation | Processors, High performance | High-performance computing
In high-performance computing (HPC), significant effort is invested in research and development of novel memory technologies. One of them is Spin Transfer Torque Magnetic Random Access Memory (STT-MRAM) --- byte-addressable, high-endurance non-volatile memory with slightly higher access time than DRAM. In this study, we conduct a preliminary assessment of HPC system performance impact with STT-MRAM main memory with recent industry estimations. Reliable timing parameters of STT-MRAM devices are unavailable, so we also perform a sensitivity analysis that correlates overall system slowdown trend with respect to average device latency. Our results demonstrate that the overall system performance of large HPC clusters is not particularly sensitive to main-memory latency. Therefore, STT-MRAM, as well as any other emerging non-volatile memories with comparable density and access time, can be a viable option for future HPC memory system design.
This work was supported by the Collaboration Agreement between Samsung Electronics Co., Ltd. and BSC, Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project and by the Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272).
This work has also received funding from the European Union's Horizon 2020 research and innovation programme under ExaNoDe project (grant agreement No 671578).