Proceedings ArticleDOI
A scalable processing-in-memory accelerator for parallel graph processing
Junwhan Ahn,Sungpack Hong,Sungjoo Yoo,Onur Mutlu,Kiyoung Choi +4 more
- Vol. 43, Iss: 3, pp 105-117
TLDR
This work argues that the conventional concept of processing-in-memory (PIM) can be a viable solution to achieve memory-capacity-proportional performance and designs a programmable PIM accelerator for large-scale graph processing called Tesseract.Abstract:
The explosion of digital data and the ever-growing need for fast data analysis have made in-memory big-data processing in computer systems increasingly important. In particular, large-scale graph processing is gaining attention due to its broad applicability from social science to machine learning. However, scalable hardware design that can efficiently process large graphs in main memory is still an open problem. Ideally, cost-effective and scalable graph processing systems can be realized by building a system whose performance increases proportionally with the sizes of graphs that can be stored in the system, which is extremely challenging in conventional systems due to severe memory bandwidth limitations. In this work, we argue that the conventional concept of processing-in-memory (PIM) can be a viable solution to achieve such an objective. The key modern enabler for PIM is the recent advancement of the 3D integration technology that facilitates stacking logic and memory dies in a single package, which was not available when the PIM concept was originally examined. In order to take advantage of such a new technology to enable memory-capacity-proportional performance, we design a programmable PIM accelerator for large-scale graph processing called Tesseract. Tesseract is composed of (1) a new hardware architecture that fully utilizes the available memory bandwidth, (2) an efficient method of communication between different memory partitions, and (3) a programming interface that reflects and exploits the unique hardware design. It also includes two hardware prefetchers specialized for memory access patterns of graph processing, which operate based on the hints provided by our programming model. Our comprehensive evaluations using five state-of-the-art graph processing workloads with large real-world graphs show that the proposed architecture improves average system performance by a factor of ten and achieves 87% average energy reduction over conventional systems.read more
Citations
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Journal ArticleDOI
PIM-GraphSCC: PIM-Based Graph Processing Using Graph’s Community Structures
TL;DR: PIM-GraphSCC is proposed, the first PIM-based graph processor that exploits a graph’s connectivity to significantly reduce communication over critical resources: the inter-accelerator links, and provides a community-aware graph partitioning scheme that reduces inter-ACcelerator data movement by up to 93 percent compared to modern graph processing schemes.
Posted Content
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Proceedings ArticleDOI
GraphVine: exploiting multicast for scalable graph analytics
Leul Belayneh,Valeria Bertacco +1 more
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Journal ArticleDOI
Computing En-Route for Near-Data Processing
Jiayi Huang,Pritam Majumder,Sungkeun Kim,Troy Fulton,Ramprakash Reddy Puli,Ki Hwan Yum,Eun Jung Kim +6 more
TL;DR: This article proposes Active-Routing, an in-network near-data processing architecture for data-flow execution, which enables computation en-route by exploiting patterns of aggregation over intermediate results and introduces page granular computation offloading to amortize the offloading overhead and improve the throughput.
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