PDL ABSTRACT

Gather-Scatter DRAM: In-DRAM Address Translation to Improve the Spatial Locality of Non-unit Strided Accesses

Proceedings of the 48th International Symposium on Microarchitecture (MICRO), Waikiki, Hawaii, USA, December 2015..

Vivek Seshadri, Thomas Mullins, Amirali Boroumand, Onur Mutlu, Phillip B. Gibbons,
Michael A. Kozuch*, Todd C. Mowry

Carnegie Mellon University
* Intel Labs

http://www.pdl.cmu.edu/

Many data structures (e.g., matrices) are typically ac- cessed with multiple access patterns. Depending on the layout of the data structure in physical address space, some access patterns result in non-unit strides. In existing systems, which are optimized to store and access cache lines, non-unit strided accesses exhibit low spatial locality. Therefore, they incur high latency, and waste memory bandwidth and cache space.

We propose the Gather-Scatter DRAM (GS-DRAM) to address this problem. We observe that a commodity DRAM module contains many chips. Each chip stores a part of every cache line mapped to the module. Our idea is to enable the memory controller to access multiple values that belong to a strided pattern from different chips using a single read/write command. To realize this idea, GS-DRAM first maps the data of each cache line to different chips such that multiple values of a strided access pattern are mapped to different chips. Second, instead of sending a separate address to each chip, GS-DRAM maps each strided pattern to a small pattern ID that is communicated to the module. Based on the pattern ID, each chip independently computes the address of the value to be accessed. The cache line returned by the module contains different values of the strided pattern gathered from different chips. We show that this approach enables GS-DRAM to achieve near-ideal memory bandwidth and cache utilization for many common access patterns. We design an end-to-end system to exploit GS-DRAM. Our evaluations show that 1) for in-memory databases, GS-DRAM obtains the best of the row store and the col- umn store layouts, in terms of both performance and energy, and 2) for matrix-matrix multiplication, GS-DRAM seamlessly enables SIMD optimizations and outperforms the best tiled layout. Our framework is general, and can benefit many modern data-intensive applications.

KEYWORDS: memory errors, software reliability, memory architectures, soft errors, hard errors, datacenter cost, DRAM.

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