Detailed Information

Profiling dynamic data access patterns with controlled overhead and quality

Cited 10 time in Web of Science Cited 6 time in Scopus

Park, SeongJae; Lee, Yunjae; Yeom, Heonyoung

Issue Date
Association for Computing Machinery, Inc
Middleware Industry 2019 - Proceedings of the 2019 20th International Middleware Conference Industrial Track, Part of Middleware 2019, pp.1-7
© 2019 ACM.Modern workloads tend to have huge working sets and low locality. Despite this trend, the capacity of DRAM has not been increased enough to accommodate such huge working sets. Therefore, memory management mechanisms optimized for such modern workloads are widely required today. For such optimizations, knowing the data access pattern of given workloads is essential. However, manually extracting such patterns from huge and complex workloads is exhaustive. Worse yet, existing memory access analysis tools incur unacceptably high overheads for unnecessarily detailed analysis results. To mitigate this situation, we introduce a tool that is designed for data access pattern tracing. Two core mechanisms in this tool, a region-based sampling and an adaptive region adjustment, allow users to limit the tracing overhead in a bounded range regardless of the size and complexity of target workloads, while preserving the quality of results. Our empirical evaluations that conducted with 20 realistic workloads show the high quality, low overhead, and a potential use case of this tool.
Files in This Item:
There are no files associated with this item.
Appears in Collections:


Item View & Download Count

  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.