Accelerating Precise Race Detection Using Commercially- Available Hardware Transactional Memory Support

نویسندگان

  • Hassan Salehe Matar
  • Ismail Kuru
  • Serdar Tasiran
  • Roman Dementiev
چکیده

It is typical for state-of-the-art dynamic race detection algorithms for C programs to slow down an application by a large factor. Our measurements indicate that a significant portion of this slowdown is due to additional lock-based synchronization performed by instrumentation code. This synchronization is necessary to ensure atomic update of analysis state. We present the first precise race detection tool that improves race-detection slowdown by using commercial hardware transactional memory support to synchronize analysis and program data. By careful choice of transaction sizes, we obtain noteworthy speedups over lock-based protection of race analysis metadata.

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تاریخ انتشار 2014