A statistical multiprocessor cache model

The introduction of general-purpose microprocessors running multiple threads will put a focus on methods and tools helping a programmer to write efficient parallel applications. Such a tool should be fast enough to meet a software developer's need for short turn-around time, but also be accurate and flexible enough to provide trend-correct and intuitive feedback. This paper presents a novel sample-based method for analyzing the data locality of a multithreaded application. Very sparse data is collected during a single execution of the studied application. The architectural-independent information collected during the execution is fed to a mathematical memory-system model for predicting the cache miss ratio. The sparse data can be used to characterize the application's data locality with respect to almost any possible memory system, such as complicated multiprocessor multilevel cache hierarchies. Any combination of cache size, cache-line size and degree of sharing can be modeled. Each modeled design point takes only a fraction of a second to evaluate, even though the application from which the sampled data was collected may have executed for hours. This makes the tool not just usable for software developers, but also for hardware developers who need to evaluate a huge memory-system design space. The accuracy of the method is evaluated using a large number of commercial and technical multi-threaded applications. The result produced by the algorithm is shown to be consistent with results from a traditional (and much slower) architecture simulation.