Performance Analysis of and Tool Support for Transactional Memory on BG/Q

Schindewolf, M

Lawrence Livermore National Laboratory

关键词: Lawrence Livermore National Laboratory;    97 Mathematical Methods And Computing;    Genes;    Computer Networks;    Synchronization;   

DOI  :  10.2172/1034486
RP-ID  :  LLNL-TR-519833
RP-ID  :  W-7405-ENG-48
RP-ID  :  1034486

美国|英语

来源: UNT Digital Library

【 摘 要 】

Martin Schindewolf worked during his internship at the Lawrence Livermore National Laboratory (LLNL) under the guidance of Martin Schulz at the Computer Science Group of the Center for Applied Scientific Computing. We studied the performance of the TM subsystem of BG/Q as well as researched the possibilities for tool support for TM. To study the performance, we run CLOMP-TM. CLOMP-TM is a benchmark designed for the purpose to quantify the overhead of OpenMP and compare different synchronization primitives. To advance CLOMP-TM, we added Message Passing Interface (MPI) routines for a hybrid parallelization. This enables to run multiple MPI tasks, each running OpenMP, on one node. With these enhancements, a beneficial MPI task to OpenMP thread ratio is determined. Further, the synchronization primitives are ranked as a function of the application characteristics. To demonstrate the usefulness of these results, we investigate a real Monte Carlo simulation called Monte Carlo Benchmark (MCB). Applying the lessons learned yields the best task to thread ratio. Further, we were able to tune the synchronization by transactifying the MCB. Further, we develop tools that capture the performance of the TM run time system and present it to the application's developer. The performance of the TM run time system relies on the built-in statistics. These tools use the Blue Gene Performance Monitoring (BGPM) interface to correlate the statistics from the TM run time system with performance counter values. This combination provides detailed insights in the run time behavior of the application and enables to track down the cause of degraded performance. Further, one tool has been implemented that separates the performance counters in three categories: Successful Speculation, Unsuccessful Speculation and No Speculation. All of the tools are crafted around IBM's xlc compiler for C and C++ and have been run and tested on a Q32 early access system.