Memory-mapping support for reducer hyperobjects

  • Authors:

  • I-Ting Angelina Lee;Aamir Shafi;Charles E. Leiserson

  • Affiliations:

  • MIT CSAIL, Cambridge, MA, USA;National University of Sciences and Technology, Islamabad, Pakistan;MIT CSAIL, Cambridge, MA, USA

  • Venue:
  • Proceedings of the twenty-fourth annual ACM symposium on Parallelism in algorithms and architectures
  • Year:
  • 2012

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Abstract

Reducer hyperobjects (reducers) provide a linguistic abstraction for dynamic multithreading that allows different branches of a parallel program to maintain coordinated local views of the same nonlocal variable. In this paper, we investigate how thread-local memory mapping (TLMM) can be used to improve the performance of reducers. Existing concurrency platforms that support reducer hyperobjects, such as Intel Cilk Plus and Cilk++, take a hypermap approach in which a hash table is used to map reducer objects to their local views. The overhead of the hash table is costly --- roughly 12x overhead compared to a normal L1-cache memory access on an AMD Opteron 8354. We replaced the Intel Cilk Plus runtime system with our own Cilk-M runtime system which uses TLMM to implement a reducer mechanism that supports a reducer lookup using only two memory accesses and a predictable branch, which is roughly a 3x overhead compared to an ordinary L1-cache memory access. An empirical evaluation shows that the Cilk-M memory-mapping approach is close to 4x faster than the Cilk Plus hypermap approach. Furthermore, the memory-mapping approach admits better locality than the hypermap approach during parallel execution, which allows an application using reducers to scale better.