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Title: A programmable shared-memory system for an array of processing-in-memory devices

Abstract

Processing in memory (PIM), the concept of integrating processing directly with memory has been attracting a lot of attention, since PIM can assist in overcoming the throughput limitation caused by data movement between CPU and memory. The challenge, however, is that it requires the programmers to have a deep understanding of the PIM architecture to maximize the benefits such as data locality and parallel thread execution on multiple PIM devices. In this study, we present AnalyzeThat, a programmable shared-memory system for parallel data processing with PIM devices. Thematic to AnalyzeThat is a rich PIM-aware data structure (PADS), which is an encapsulation that integrally ties together the data, the analysis tasks and the runtime needed to interface with the PIM device array. The PADS abstraction provides (i) a sophisticated key-value data container that allows programmers to easily store data on multiple PIMs, (ii) a suite of parallel operations with which users can easily implement data analysis applications, and (iii) a runtime, hidden to programmers, which provides the mechanisms needed to overlay both the data and the tasks on the PIM device array in an intelligent fashion, based on PIM-specific information collected from the hardware. We have developed a PIM emulation frameworkmore » called AnalyzeThat. In conclusion, our experimental evaluation with representative data analytics applications suggests that the proposed system can significantly reduce the PIM programming effort without losing its technology benefits.« less

Authors:
ORCiD logo [1]; ORCiD logo [1];  [2]; ORCiD logo [1]
+ Show Author Affiliations
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Sogang Univ., Seoul (Republic of Korea)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1468266
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Cluster Computing
Additional Journal Information:
Journal Volume: 22; Journal ID: ISSN 1386-7857
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Programmable devices; Storage systems; Processing-in-memory; Big data processing

Citation Formats

Lee, Sangkuen, Sim, Hyogi, Kim, Youngjae, and Vazhkudai, Sudharshan S. A programmable shared-memory system for an array of processing-in-memory devices. United States: N. p., 2018. Web. doi:10.1007/s10586-018-2844-1.
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Lee, Sangkuen, Sim, Hyogi, Kim, Youngjae, & Vazhkudai, Sudharshan S. A programmable shared-memory system for an array of processing-in-memory devices. United States. https://doi.org/10.1007/s10586-018-2844-1
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Lee, Sangkuen, Sim, Hyogi, Kim, Youngjae, and Vazhkudai, Sudharshan S. Thu . "A programmable shared-memory system for an array of processing-in-memory devices". United States. https://doi.org/10.1007/s10586-018-2844-1. https://www.osti.gov/servlets/purl/1468266.
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@article{osti_1468266,
title = {A programmable shared-memory system for an array of processing-in-memory devices},
author = {Lee, Sangkuen and Sim, Hyogi and Kim, Youngjae and Vazhkudai, Sudharshan S.},
abstractNote = {Processing in memory (PIM), the concept of integrating processing directly with memory has been attracting a lot of attention, since PIM can assist in overcoming the throughput limitation caused by data movement between CPU and memory. The challenge, however, is that it requires the programmers to have a deep understanding of the PIM architecture to maximize the benefits such as data locality and parallel thread execution on multiple PIM devices. In this study, we present AnalyzeThat, a programmable shared-memory system for parallel data processing with PIM devices. Thematic to AnalyzeThat is a rich PIM-aware data structure (PADS), which is an encapsulation that integrally ties together the data, the analysis tasks and the runtime needed to interface with the PIM device array. The PADS abstraction provides (i) a sophisticated key-value data container that allows programmers to easily store data on multiple PIMs, (ii) a suite of parallel operations with which users can easily implement data analysis applications, and (iii) a runtime, hidden to programmers, which provides the mechanisms needed to overlay both the data and the tasks on the PIM device array in an intelligent fashion, based on PIM-specific information collected from the hardware. We have developed a PIM emulation framework called AnalyzeThat. In conclusion, our experimental evaluation with representative data analytics applications suggests that the proposed system can significantly reduce the PIM programming effort without losing its technology benefits.},
doi = {10.1007/s10586-018-2844-1},
journal = {Cluster Computing},
number = ,
volume = 22,
place = {United States},
year = {2018},
month = {8}
}
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https://doi.org/10.1007/s10586-018-2844-1
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