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
GraphH: A Processing-in-Memory Architecture for Large-Scale Graph Processing
Guohao Dai,Tianhao Huang,Yuze Chi,Jishen Zhao,Guangyu Sun,Yongpan Liu,Yu Wang,Yuan Xie,Huazhong Yang +8 more
TL;DR: GraphH, a PIM architecture for graph processing on the hybrid memory cube array, is proposed to tackle all four problems mentioned above, including random access pattern causing local bandwidth degradation, poor locality leading to unpredictable global data access, heavy conflicts on updating the same vertex, and unbalanced workloads across processing units.
Journal ArticleDOI
GRIM-Filter: Fast seed location filtering in DNA read mapping using processing-in-memory technologies.
Jeremie S. Kim,Jeremie S. Kim,Damla Senol Cali,Hongyi Xin,Donghyuk Lee,Saugata Ghose,Mohammed Alser,Hasan Hassan,Oguz Ergin,Can Alkan,Onur Mutlu,Onur Mutlu +11 more
TL;DR: GRIM-Filter as discussed by the authors uses 3D-stacked memory to improve the performance of a read mapper by introducing a new representation of coarse-grained segments of the reference genome, and using massively-parallel in-memory operations to identify read presence within each coarsegrained segment.
Proceedings ArticleDOI
RecNMP: accelerating personalized recommendation with near-memory processing
Liu Ke,Udit Gupta,Benjamin Youngjae Cho,David Brooks,Vikas Chandra,Utku Diril,Amin Firoozshahian,Kim Hazelwood,Bill Jia,Hsien-Hsin S. Lee,Meng Li,Bert Maher,Dheevatsa Mudigere,Maxim Naumov,Martin Schatz,Mikhail Smelyanskiy,Xiaodong Wang,Brandon Reagen,Carole-Jean Wu,Mark Hempstead,Xuan Zhang +20 more
TL;DR: RecNMP as mentioned in this paper proposes a lightweight, commodity DRAM compliant, near-memory processing solution to accelerate personalized recommendation inference, which is specifically tailored to production environments with heavy co-location of operators on a single server.
Proceedings ArticleDOI
ForeGraph: Exploring Large-scale Graph Processing on Multi-FPGA Architecture
TL;DR: ForeGraph, a large-scale graph processing framework based on the multi-FPGA architecture, is proposed, which outperforms state-of-the-art FPGA-based large- scale graph processing systems by 4.54x when executing PageRank on the Twitter graph.
Journal ArticleDOI
Energy efficient architecture for graph analytics accelerators
Muhammet Mustafa Ozdal,Serif Yesil,Taemin Kim,Andrey Ayupov,John Greth,Steven M. Burns,Ozcan Ozturk +6 more
TL;DR: This paper proposes a configurable architecture template that is specifically optimized for iterative vertex-centric graph applications with irregular access patterns and asymmetric convergence and addresses the limitations of the existing multi-core CPU and GPU architectures for these types of applications.
References
More filters
Journal ArticleDOI
The anatomy of a large-scale hypertextual Web search engine
Sergey Brin,Lawrence Page +1 more
TL;DR: This paper provides an in-depth description of Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and looks at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.
Journal Article
The Anatomy of a Large-Scale Hypertextual Web Search Engine.
Sergey Brin,Lawrence Page +1 more
TL;DR: Google as discussed by the authors is a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems.
Journal ArticleDOI
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
George Karypis,Vipin Kumar +1 more
TL;DR: This work presents a new coarsening heuristic (called heavy-edge heuristic) for which the size of the partition of the coarse graph is within a small factor of theSize of the final partition obtained after multilevel refinement, and presents a much faster variation of the Kernighan--Lin (KL) algorithm for refining during uncoarsening.
Journal ArticleDOI
Pin: building customized program analysis tools with dynamic instrumentation
Chi-Keung Luk,Robert Cohn,Robert Muth,Harish Patil,Artur Klauser,Geoff Lowney,Steven Wallace,Vijay Janapa Reddi,Kim Hazelwood +8 more
TL;DR: The goals are to provide easy-to-use, portable, transparent, and efficient instrumentation, and to illustrate Pin's versatility, two Pintools in daily use to analyze production software are described.
Proceedings ArticleDOI
Pregel: a system for large-scale graph processing
Grzegorz Malewicz,Matthew H. Austern,Aart J. C. Bik,James C. Dehnert,Ilan Horn,Naty Leiser,Grzegorz Czajkowski +6 more
TL;DR: A model for processing large graphs that has been designed for efficient, scalable and fault-tolerant implementation on clusters of thousands of commodity computers, and its implied synchronicity makes reasoning about programs easier.