Theses and Dissertations from UMD

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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

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Author: search.filters.author.Wu, Meng-JuSubject: search.filters.subject.Parallel Computing

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    Reuse Distance Analysis for Large-Scale Chip Multiprocessors
    (2012) Wu, Meng-Ju; Yeung, Donald; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Multicore Reuse Distance (RD) analysis is a powerful tool that can potentially provide a parallel program's detailed memory behavior. Concurrent Reuse Distance (CRD) and Private-stack Reuse Distance (PRD) measure RD across thread-interleaved memory reference streams, addressing shared and private caches. Sensitivity to memory interleaving makes CRD and PRD profiles architecture dependent, preventing them from analyzing different processor configurations. However such instability is minimal when all threads exhibit similar data-locality patterns. For loop-based parallel programs, interleaving threads are symmetric. CRD and PRD profiles are stable across cache size scaling, and exhibit predictable coherent movement across core count scaling. Hence, multicore RD analysis can provide accurate analysis for different processor configurations. Due to the prevalence of parallel loops, RD analysis will be valuable to multicore designers. This dissertation uses RD analysis to analyze multicore cache performance for loop-based parallel programs. First, we study the impacts of core count scaling and problem size scaling on CRD and PRD profiles. Two application parameters with architectural implications are identified: Ccore and Cshare. Core count scaling only impacts cache performance significantly below Ccore in shared caches, and Cshare is the capacity at which shared caches begin to outperform private caches in terms of data locality. Then, we develop techniques, in particular employing reference groups, to predict the coherent movement of CRD and PRD profiles due to scaling, and achieve accuracy of 80%-96%. After comparing our prediction techniques against profile sampling, we find that the prediction achieves higher speedup and accuracy, especially when the design space is large. Moreover, we evaluate the accuracy of using CRD and PRD profile predictions to estimate multicore cache performance, especially MPKI. When combined with the existing problem scaling prediction, our techniques can predict shared LLC (private L2 cache) MPKI to within 12% (14%) of simulation across 1,728 (1,440) configurations using only 36 measured CRD (PRD) profiles. Lastly, we propose a new framework based on RD analysis to optimize multicore cache hierarchies. Our study not only reveals several new insights, but it also demonstrates that RD analysis can help computer architects improve multicore designs.