Offline symbolic analysis to infer Total Store Order

Dongyoon Lee, Mahmoud H. Said, S. Narayanasamy, Z. Yang
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引用次数: 22

Abstract

Ability to record and replay an execution can significantly help programmers debug their programs, especially parallel programs. De-terministically replaying a multiprocessor's execution under a relaxed memory model has remained a challenging problem. This is an important problem as most modern processors only support a relaxed memory model to enable many performance critical optimizations. The most common consistency model implemented in processors is the Total Store Order (TSO). We present an efficient and low-complexity processor based solution for recording and replaying under the Total Store Order (TSO) memory model. Processor provides support for logging data fetched on cache misses. Using this information each thread can be de-terministically replayed. A TSO-compliant casual order between the shared-memory accesses executed in different threads is then inferred using an offline algorithm based on Satisfiability Modulo Theory (SMT) solver. We also discuss methods to bound the search space during offline analysis and several optimizations to reduce the offline analysis time.
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离线符号分析,以推断总存储订单
记录和重放执行的能力可以极大地帮助程序员调试他们的程序,特别是并行程序。在宽松内存模型下确定地重放多处理器的执行仍然是一个具有挑战性的问题。这是一个重要的问题,因为大多数现代处理器只支持宽松的内存模型来实现许多性能关键优化。处理器中实现的最常见的一致性模型是Total Store Order (TSO)。我们提出了一种基于全存储顺序(TSO)存储器模型的高效、低复杂度的记录和重放解决方案。处理器提供了对缓存失败时获取的数据进行日志记录的支持。使用这些信息,每个线程都可以确定地重放。然后使用基于可满足模理论(SMT)求解器的离线算法推断在不同线程中执行的共享内存访问之间符合tso的临时顺序。我们还讨论了在离线分析时约束搜索空间的方法以及减少离线分析时间的几种优化方法。
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