Last level cache (LLC) performance of data affining workloads on a CMP - A case study of parallel bioinformatics workloads

With the continuing growth in the amount of genetic data, members of the bioinformatics community are developing a variety of data-mining applications to understand the data and discover meaningful information. These applications are important in defining the design and performance decisions of future high performance microprocessors. This paper presents a detailed data-sharing analysis and chip-multiprocessor (CAMP) cache study of several multi-threaded data-mining bioinformatics workloads. For a CMP with a three-level cache hierarchy, we model the last-level of the cache hierarchy as either multiple private caches or a single cache shared amongst different cores of the CA P. Our experiments show that the bioinformatics workloads exhibit significant data-sharing-50-95% of the data cache is shared by the different threads of the workload. Furthermore, regardless of the amount of data cache shared for some workloads, as many as 98% of the accesses to the last-level cache are to shared data cache lines. Additionally, the amount of data-sharing exhibited by the workloads is a function of the total cache size available-the larger the data cache the better the sharing behavior Thus partitioning the available last-level cache silicon area into multiple private caches can cause applications to lose their inherent data-sharing behavior For the workloads in this study, a shared 32AM last-level cache is able to capture a tremendous amount of data-sharing and outperform a 32AM private cache configuration by several orders of magnitude Specifically, with shared last-level caches the bandwidth demands beyond the last-level cache can he reduced by factors of 3-625 when compared to private last-level caches.