优化锁定密集型多线程并行应用程序的跟踪工具开销

Ajit Singh, P. Chakraborty
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引用次数: 0

摘要

通常,为锁密集型应用程序收集跟踪的工具会增加自身的开销,并扭曲与锁相关的测量。我们强调了为什么工具开销对于锁密集型应用程序尤其成问题。在现有的研究中,工具开销受到的关注有限。根据我们的分析,高工具开销的主要原因是与跟踪工具数据结构的缓存一致性相关的开销。利用这一见解,我们开发了Mutexis,这是一个优化的用户级动态二进制仪表(DBI)跟踪PIN工具。为了显示工具的有效性,我们使用了PARSEC和Splash3X基准测试中的锁密集型应用程序。我们将提出的工具开销与其他研究人员的工具开销进行了比较。互斥锁的工具开销很小,对于锁密集型应用程序增长到2.1X(对于其他应用程序从4X增长到lOOX),并且在大多数情况下可以忽略不计。即使与其他研究人员工具收集的有限的汇总统计数据相比,我们的工具捕获了POSIX锁函数的详细循环痕迹,情况也是如此。
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Optimizing Trace Tool-overhead for Lock-Intensive Multi-threaded Parallel Applications
Often a tool collecting traces for lock-intensive applications adds overheads of its own and distorts the lock-related measurements. We highlight why tool-overhead is particularly problematic for lock-intensive applications. Tool-overhead has received limited attention in existing research. The primary reason for high tool-overhead, as per our analysis is cache- coherence related overheads for tracing tool data structure. Using the insight, we develop Mutexis, an optimized user-level dynamic binary instrumentation (DBI) tracing PIN tool. To show tool effectiveness, we use lock-intensive applications from PARSEC and Splash3X benchmarks. We compare the proposed tool's overhead with tool-overhead of other researchers. The tool-overhead of mutexis is minimal, growing up to 2.1X for lock- intensive applications (4X to lOOX for others) and is negligible in most cases. This is so, even when our tool captures detail cycle- stamped traces of POSIX lock function compared to limited aggregate statistics collected by other researchers tools.
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