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Main memory controller with multiple media technologies for big data workloads

Authors :
Miguel A. Avargues
Manel Lurbe
Salvador Petit
Maria E. Gomez
Rui Yang
Xiaoping Zhu
Guanhao Wang
Julio Sahuquillo
Source :
Journal of Big Data, Vol 10, Iss 1, Pp 1-19 (2023)
Publication Year :
2023
Publisher :
SpringerOpen, 2023.

Abstract

Abstract SRAM and DRAM memory technologies have been dominant in the implementations of memory subsystems. In recent years, and mainly driven by the huge memory demands of big data applications, NVRAM technology has emerged as a denser memory technology, enabling the design of new hybrid DRAM/NVRAM memory hierarchies that combine multiple memory media technologies to balance memory capacity, latency, cost, and endurance. Two main approaches are being applied to the design of hybrid memory hierarchies: the hybrid address space approach, which relies on the programmer or the operating system to choose the memory technology where each memory page should be stored; and the (only) NVM address space approach, where a faster technology (e.g. commodity DRAM) is needed to acts as a cache of NVRAM to boost the performance. This approach presents architectural challenges such the organization of metadata (e.g. cache tags) and the selection of the proper technology for each memory component. In contrast to existing approaches, this work proposes a memory controller that leverages novel memory technologies such as eDRAM and MRAM to mitigate NVRAM bus contention and improve the performance of the NVM address space. The devised solution proposes a two-level cache hierarchy in the memory controller: a SRAM sector cache and a (x)RAM cache. The (x)RAM cache, much denser, helps significantly reduce the number of accesses to NVRAM. Experimental results show that implementing the (x)RAM cache with eDRAM or MRAM is the best performing approach. Moreover, the eRAM is able to improve the SRAM cache miss penalty by up to 50% and 80%, and overall system performance by 15% and 23%.

Details

Language :
English
ISSN :
21961115
Volume :
10
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Journal of Big Data
Publication Type :
Academic Journal
Accession number :
edsdoj.bb7ffe324fd443c38ca90099ebb258a3
Document Type :
article
Full Text :
https://doi.org/10.1186/s40537-023-00761-0