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Title: Co-design of Advanced Architectures for Graph Analytics using Machine Learning

Abstract

A graph is an excellent way of representing relationships among entities. We can use graph analytics to synthesize and analyze such relational data, and extract relevant features that are useful for various tasks such as machine learning. Considering the crucial role of graph analytics in various domains, it is important and timely to investigate the right hardware configurations that can achieve optimal performance for graph workloads on future high-performance computing systems. Design space exploration studies facilitate the selection of appropriate configurations (e.g. memory) to achieve a desired system performance. Recently, the approach of accelerating graph analytics using persistent non-volatile memory has gained a lot of attention. Traditional system simulators such as Gem5 and NVMain can be used to explore the design space of these advanced memory architectures for graph workloads. However, these simulators are slow in execution thus limiting the efficiency of design space exploration studies. To overcome this challenge, we proposed a machine learning based approach to co-design advanced memory architectures for graph workloads. We tested our approach with DRAM, non-volatile memory, and hybrid memory (DRAM+NVM) using a breadth first search benchmark algorithm. Our results showed the applicability of the proposed machine learning based approach to the co-design ofmore » the advanced memory architectures. In this paper, we provide recommendations on selecting advanced memory architectures to achieve desired performance for graph workloads. We also discuss the performances of different machine learning models that were considered in this study.« less

Authors:
 [1]; ORCiD logo [1]; ORCiD logo [1];  [1]; ORCiD logo [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1808193
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: GrAPL 2021: IPDPS 2021 Workshop on Graphs, Architectures, Programming, and Learning - Portland (To be held Virtually), Oregon, United States of America - 5/17/2021 12:00:00 PM-5/21/2021 12:00:00 PM
Country of Publication:
United States
Language:
English

Citation Formats

Kurte, Kuldeep, Imam, Neena, Kannan, Ramakrishnan, Hasan, S M Shamimul, and Yoginath, Srikanth. Co-design of Advanced Architectures for Graph Analytics using Machine Learning. United States: N. p., 2021. Web.
Kurte, Kuldeep, Imam, Neena, Kannan, Ramakrishnan, Hasan, S M Shamimul, & Yoginath, Srikanth. Co-design of Advanced Architectures for Graph Analytics using Machine Learning. United States.
Kurte, Kuldeep, Imam, Neena, Kannan, Ramakrishnan, Hasan, S M Shamimul, and Yoginath, Srikanth. 2021. "Co-design of Advanced Architectures for Graph Analytics using Machine Learning". United States. https://www.osti.gov/servlets/purl/1808193.
@article{osti_1808193,
title = {Co-design of Advanced Architectures for Graph Analytics using Machine Learning},
author = {Kurte, Kuldeep and Imam, Neena and Kannan, Ramakrishnan and Hasan, S M Shamimul and Yoginath, Srikanth},
abstractNote = {A graph is an excellent way of representing relationships among entities. We can use graph analytics to synthesize and analyze such relational data, and extract relevant features that are useful for various tasks such as machine learning. Considering the crucial role of graph analytics in various domains, it is important and timely to investigate the right hardware configurations that can achieve optimal performance for graph workloads on future high-performance computing systems. Design space exploration studies facilitate the selection of appropriate configurations (e.g. memory) to achieve a desired system performance. Recently, the approach of accelerating graph analytics using persistent non-volatile memory has gained a lot of attention. Traditional system simulators such as Gem5 and NVMain can be used to explore the design space of these advanced memory architectures for graph workloads. However, these simulators are slow in execution thus limiting the efficiency of design space exploration studies. To overcome this challenge, we proposed a machine learning based approach to co-design advanced memory architectures for graph workloads. We tested our approach with DRAM, non-volatile memory, and hybrid memory (DRAM+NVM) using a breadth first search benchmark algorithm. Our results showed the applicability of the proposed machine learning based approach to the co-design of the advanced memory architectures. In this paper, we provide recommendations on selecting advanced memory architectures to achieve desired performance for graph workloads. We also discuss the performances of different machine learning models that were considered in this study.},
doi = {},
url = {https://www.osti.gov/biblio/1808193}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2021},
month = {6}
}

Conference:
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