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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Proceedings ArticleDOI
Sextans: A Streaming Accelerator for General-Purpose Sparse-Matrix Dense-Matrix Multiplication
TL;DR: In this paper , the authors proposed a non-general-purpose accelerator design for sparse-matrix Dense-Matrix multiplication (SMM) where one accelerator can only process a fixed-size problem.
Proceedings ArticleDOI
Platform Independent Software Analysis for Near Memory Computing
TL;DR: PISA-NMC is presented, which extends a state-of-the-art hardware agnostic profiling tool with metrics concerning memory and parallelism, which are relevant for NMC, which include memory entropy, spatial locality, data-level, and basic-block-level parallelism.
Proceedings ArticleDOI
Computing In-Memory, Revisited
Dejan Milojicic,Kirk Bresniker,Gary Campbell,Paolo Faraboschi,John Paul Strachan,Stan Williams +5 more
TL;DR: This paper sketches the Computing-In-Memory (CIM) vision, and its substantial performance and power improvement potential, and discusses the programming model, which it is considered the biggest challenge.
Proceedings ArticleDOI
A Preliminary Study of Compiler Transformations for Graph Applications on the Emu System
Prasanth Chatarasi,Vivek Sarkar +1 more
TL;DR: Two high- level compiler optimizations, i.e., loop fusion and edge flipping, and one low-level compiler transformation leveraging hardware support for remote atomic updates to address overheads arising from thread migration, creation, synchronization, and atomic operations are explored.
Proceedings ArticleDOI
Towards optimal quantization of neural networks
TL;DR: This work develops an approach to quantizing deep networks using functional high-rate quantization theory and leads to an optimal quantizer that is computed using the celebrated backpropagation algorithm.
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