McVerSi:模拟中快速内存一致性验证的测试生成框架

M. Elver, V. Nagarajan
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引用次数: 28

摘要

存储器一致性模型(MCM)正式规定了存储器系统的行为,被程序员用来推理并行程序。硬件必须遵守承诺的MCM。因此,硬件设计必须根据指定的MCM进行验证。一种常见的方法是通过执行测试,其中生成指令序列的特定线程,并检查它们的执行是否符合MCM。在模拟环境下执行这些测试是非常有益的,例如,当硬件的功能设计实现正在原型化时。然而,大多数先前的验证方法针对的是后硅环境,在模拟下应用时速度太慢。我们提出McVerSi,一个测试生成框架,用于在仿真下对全系统设计实现进行快速MCM验证。我们的主要贡献是基于遗传规划(GP)的MCM测试生成方法,该方法依赖于一种新的交叉函数,该函数优先考虑导致不确定性的内存操作,从而增加发现MCM错误的概率。为了引导测试尽可能多地运用逻辑,模拟器报告的覆盖率被用作适应度函数。此外,我们通过使测试工作负载模拟感知来提高测试吞吐量。我们在Ruby的全系统模式下使用Gem5周期精确模拟器来评估我们提出的框架。由于管道和缓存一致性协议的错误交互,我们发现了2个新的错误。至关重要的是,这些漏洞不会通过对管道或一致性协议的单独验证被发现。我们总共研究了11种虫子。我们基于gp的测试生成方法可以一致地找到所有的bug,因此与其他方法(伪随机测试生成和石蕊测试)相比,提供了更高的保证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
McVerSi: A test generation framework for fast memory consistency verification in simulation
The memory consistency model (MCM), which formally specifies the behaviour of the memory system, is used by programmers to reason about parallel programs. It is imperative that hardware adheres to the promised MCM. For this reason, hardware designs must be verified against the specified MCM. One common way to do this is via executing tests, where specific threads of instruction sequences are generated and their executions are checked for adherence to the MCM. It would be extremely beneficial to execute such tests under simulation, i.e. when the functional design implementation of the hardware is being prototyped. Most prior verification methodologies, however, target post-silicon environments, which when applied under simulation would be too slow. We propose McVerSi, a test generation framework for fast MCM verification of a full-system design implementation under simulation. Our primary contribution is a Genetic Programming (GP) based approach to MCM test generation, which relies on a novel crossover function that prioritizes memory operations contributing to non-determinism, thereby increasing the probability of uncovering MCM bugs. To guide tests towards exercising as much logic as possible, the simulator's reported coverage is used as the fitness function. Furthermore, we increase test throughput by making the test workload simulation-aware. We evaluate our proposed framework using the Gem5 cycle accurate simulator in full-system mode with Ruby. We discover 2 new bugs due to the faulty interaction of the pipeline and the cache coherence protocol. Crucially, these bugs would not have been discovered through individual verification of the pipeline or the coherence protocol. We study 11 bugs in total. Our GP-based test generation approach finds all bugs consistently, therefore providing much higher guarantees compared to alternative approaches (pseudo-random test generation and litmus tests).
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