On Chip Multiprocessors (CMP), it is common that multiple cores share certain levels of cache. The sharing increases the contention in cache and memory-to-chip bandwidth, further highlighting the importance of data locality analysis. As a rigorous and hardware-independent locality metric, reuse distance has served for a variety of locality analysis, program transformations, and performance prediction. However, previous studies have concentrated on sequential programs running on unicore processors. On CMP, accesses by different threads (or jobs) interact in the shared cache. How reuse distance applies to the new architecture remains an open question-particularly, how the interactions in shared cache affect the collection and application of reuse distance, and how reusedistance-based locality analysis should adapt to such architecture changes. This paper presents our explorations towards answering those questions. It first introduces the concept of concurrent reuse distance, a direct extension of the traditional concept of reuse distance with data references by all co-running threads (or jobs) considered. It then discusses the properties of concurrent reuse distance, revealing the special challenges facing the collection and application of concurrent reuse distance on CMP platforms. Finally, it presents the solutions to those challenges for a class of multithreading applications. The solutions center on a probabilistic model that connects concurrent reuse distance with the data locality of each individual thread. Experiments demonstrate the effectiveness of the proposed techniques in facilitating the uses of concurrent reuse distance for CMP computing.

Most modern Chip Multiprocessors (CMP) feature shared cache on chip. For multithreaded applications, the sharing reduces communication latency among co-running threads, but also results in cache contention.A number of studies have examined the influence of cache sharing on multithreaded applications, but most of them have concentrated on the design or management of shared cache, rather than a systematic measurement of the influence. Consequently, prior measurements have been constrained by the reliance on simulators, the use of out-of-date benchmarks, and the limited coverage of deciding factors. The influence of CMP cache sharing on contemporary multithreaded applications remains preliminarily understood.In this work, we conduct a systematic measurement of the influence on two kinds of commodity CMP machines, using a recently released CMP benchmark suite, PARSEC, with a number of potentially important factors on program, OS, and architecture levels considered. The measurement shows some surprising results. Contrary to commonly perceived importance of cache sharing, neither positive nor negative effects from the cache sharing are significant for most of the program executions, regardless of the types of parallelism, input datasets, architectures, numbers of threads, and assignments of threads to cores. After a detailed analysis, we find that the main reason is the mismatch of current development and compilation of multithreaded applications and CMP architectures. By transforming the programs in a cache-sharing-aware manner, we observe up to 36% performance increase when the threads are placed on cores appropriately.

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