Understanding the Memory Performance of Data-Mining Workloads on Small, Medium, and Large-Scale CMPs Using Hardware-Software Co-simulation

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Abstract

With the amount of data continuing to grow, extracting "data of interest" is becoming popular, pervasive, and more important than ever. Data mining, as this process is known as, seeks to draw meaningful conclusions, extract knowledge, and acquire models from vast amounts of data. These compute-intensive data-mining applications, where thread-level parallelism can be effectively exploited, are the design targets of future multi-core systems. As a result, future multi-core systems will be required to process terabyte-level workloads. To understand the memory system performance of data-mining applications, this paper presents the use of hardware-software co-simulation to explore the cache design space of several multi-threaded data mining applications. Our study reveals that the workloads are memory intensive, have large working-set sizes, and exhibit good data locality. We find that large DRAM caches can be useful to address their large working-set sizes

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