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
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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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Book ChapterDOI
GNN-PIM: A Processing-in-Memory Architecture for Graph Neural Networks
TL;DR: Wang et al. as discussed by the authors proposed a processing-in-memory (PIM) architecture to accelerate GNN inference and developed an optimized dataflow to leverage the inherent parallelism of GNNs.
Proceedings ArticleDOI
REDUCT: keep it close, keep it cool!: efficient scaling of DNN inference on multi-core CPUs with near-cache compute
Anant V. Nori,Rahul Bera,Shankar Balachandran,Joydeep Rakshit,Omer Om J,Avishaii Abuhatzera,Belliappa Kuttanna,Sreenivas Subramoney +7 more
TL;DR: Reduce as discussed by the authors enables instruction delivery/decode close to execution and instruction execution close to data to enable both raw performance scaling and overall performance/Watt improvements for multi-core CPU DNN inference.
Journal ArticleDOI
CODA: Enabling Co-location of Computation and Data for Multiple GPU Systems
Hyojong Kim,Ramyad Hadidi,Lifeng Nai,Hyesoon Kim,Nuwan Jayasena,Yasuko Eckert,Onur Kayiran,Gabriel H. Loh +7 more
TL;DR: In this paper, the authors propose a mechanism that identifies exclusively accessed data and place the data along with the thread block that accesses it in the same GPU to enable co-placement of compute and data in the presence of fine-grained interleaved memory.
Journal ArticleDOI
Efficient and High-quality Sparse Graph Coloring on the GPU
TL;DR: Xiao et al. as discussed by the authors presented a work-efficient parallel graph coloring implementation on GPUs with good coloring quality, which employs the speculative greedy scheme which inherently yields better quality than the method of finding maximal independent set.
Posted Content
Sextans: A Streaming Accelerator for General-Purpose Sparse-Matrix Dense-Matrix Multiplication.
TL;DR: Sextans as discussed by the authors is an accelerator for general-purpose sparse-matrices processing with PE-aware non-zero scheduling for balanced workload with an II=1 pipeline, and hardware flexibility to enable prototyping the hardware once to support SpMMs of different size.
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.