Micro-pages: increasing DRAM efficiency with locality-aware data placement

  • Authors:

  • Kshitij Sudan;Niladrish Chatterjee;David Nellans;Manu Awasthi;Rajeev Balasubramonian;Al Davis

  • Affiliations:

  • University of Utah, Salt Lake City, UT, USA;University of Utah, Salt Lake City, UT, USA;University of Utah, Salt Lake City, UT, USA;University of Utah, Salt Lake City, UT, USA;University of Utah, Salt Lake City, UT, USA;University of Utah, Salt Lake City, UT, USA

  • Venue:
  • Proceedings of the fifteenth edition of ASPLOS on Architectural support for programming languages and operating systems
  • Year:
  • 2010

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Abstract

Power consumption and DRAM latencies are serious concerns in modern chip-multiprocessor (CMP or multi-core) based compute systems. The management of the DRAM row buffer can significantly impact both power consumption and latency. Modern DRAM systems read data from cell arrays and populate a row buffer as large as 8 KB on a memory request. But only a small fraction of these bits are ever returned back to the CPU. This ends up wasting energy and time to read (and subsequently write back) bits which are used rarely. Traditionally, an open-page policy has been used for uni-processor systems and it has worked well because of spatial and temporal locality in the access stream. In future multi-core processors, the possibly independent access streams of each core are interleaved, thus destroying the available locality and significantly under-utilizing the contents of the row buffer. In this work, we attempt to improve row-buffer utilization for future multi-core systems. The schemes presented here are motivated by our observations that a large number of accesses within heavily accessed OS pages are to small, contiguous "chunks" of cache blocks. Thus, the co-location of chunks (from different OS pages) in a row-buffer will improve the overall utilization of the row buffer contents, and consequently reduce memory energy consumption and access time. Such co-location can be achieved in many ways, notably involving a reduction in OS page size and software or hardware assisted migration of data within DRAM. We explore these mechanisms and discuss the trade-offs involved along with energy and performance improvements from each scheme. On average, for applications with room for improvement, our best performing scheme increases performance by 9% (max. 18%) and reduces memory energy consumption by 15% (max. 70%).