Address-aware fences

  • Authors:

  • Changhui Lin;Vijay Nagarajan;Rajiv Gupta

  • Affiliations:

  • CSE Department, University of California, Riverside, Riverside, CA, USA;School of Informatics, University of Edinburgh, Edinburgh, United Kingdom;CSE Department, University of California, Riverside, Riverside, CA, USA

  • Venue:
  • Proceedings of the 27th international ACM conference on International conference on supercomputing
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

Many modern multicore architectures support shared memory for ease of programming and relaxed memory models to deliver high performance. With relaxed memory models, memory accesses can be reordered dynamically and seen by other processors. Therefore, fence instructions are provided to enforce the memory orderings that are critical to the correctness of a program. However, fence instructions are costly as they cause the processor to stall. Prior works have observed that most of the executions of fence instructions are unnecessary. In this paper we propose address-aware fence, a hardware solution for reducing the overhead of fence instructions without resorting to speculation. Address-aware fence only enforces memory orderings that are necessary to maintain the effect that the traditional fence strives to enforce. This is achieved by dynamically checking a condition for when an execution of a fence must take effect and delay the memory accesses following the fence. When a fence instruction is encountered, first, necessary memory addresses are collected to form a watchlist, and then, only the memory accesses to addresses that are contained in the watchlist are delayed. The memory accesses whose addresses are not contained in the watchlist are allowed to complete without waiting for the completion of pending memory accesses from before the fence. Our experiments conducted on a group of concurrent lock-free algorithms and SPLASH-2 benchmarks show that address-aware fence eliminates nearly all the overhead due to fences and achieves an average improvement of 12.2\% on programs with traditional fences.