NDC: Analyzing the impact of 3D-stacked memory+logic devices on MapReduce workloads


While Processing-in-Memory has been investigated for decades, it has not been embraced commercially. A number of emerging technologies have renewed interest in this topic. In particular, the emergence of 3D stacking and the imminent release of Micron's Hybrid Memory Cube device have made it more practical to move computation near memory. However, the literature is missing a detailed analysis of a killer application that can leverage a Near Data Computing (NDC) architecture. This paper focuses on in-memory MapReduce workloads that are commercially important and are especially suitable for NDC because of their embarrassing parallelism and largely localized memory accesses. The NDC architecture incorporates several simple processing cores on a separate, non-memory die in a 3D-stacked memory package; these cores can perform Map operations with efficient memory access and without hitting the bandwidth wall. This paper describes and evaluates a number of key elements necessary in realizing efficient NDC operation: (i) low-EPI cores, (ii) long daisy chains of memory devices, (iii) the dynamic activation of cores and SerDes links. Compared to a baseline that is heavily optimized for MapReduce execution, the NDC design yields up to 15X reduction in execution time and 18X reduction in system energy.

DOI: 10.1109/ISPASS.2014.6844483

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@article{Pugsley2014NDCAT, title={NDC: Analyzing the impact of 3D-stacked memory+logic devices on MapReduce workloads}, author={Seth H. Pugsley and Jeffrey Jestes and Huihui Zhang and Rajeev Balasubramonian and Vijayalakshmi Srinivasan and Alper Buyuktosunoglu and Al Davis and Feifei Li}, journal={2014 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)}, year={2014}, pages={190-200} }