Memory and parallelism analysis using a platform-independent approach

Stefano Corda; Gagandeep Singh; Ahsan Javed Awan; Roel Jordans; Henk Corporaal

doi:10.1145/3323439.3323988

Memory and parallelism analysis using a platform-independent approach

Stefano Corda, Gagandeep Singh, Ahsan Javed Awan, Roel Jordans, Henk Corporaal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

5 Citations (Scopus)

4 Downloads (Pure)

Abstract

Emerging computing architectures such as near-memory computing (NMC) promise improved performance for applications by reducing the data movement between CPU and memory. However, detecting such applications is not a trivial task. In this ongoing work, we extend the state-of-the-art platform-independent software analysis tool with NMC related metrics such as memory entropy, spatial locality, data-level, and basic-block-level parallelism. These metrics help to identify the applications more suitable for NMC architectures.

Original language	English
Title of host publication	Proceedings of the 22nd International Workshop on Software and Compilers for Embedded Systems, SCOPES 2019
Editors	Sander Stuijk
Place of Publication	New York
Publisher	Association for Computing Machinery, Inc
Pages	23-26
Number of pages	4
ISBN (Electronic)	978-1-4503-6762-2
DOIs	https://doi.org/10.1145/3323439.3323988
Publication status	Published - 27 May 2019
Event	22nd International Workshop on Software and Compilers for Embedded Systems, (SCOPES2019) - St. Goar, Germany Duration: 27 May 2019 → 28 May 2019 https://scopesconf.org/scopes-19/

Conference

Conference	22nd International Workshop on Software and Compilers for Embedded Systems, (SCOPES2019)
Abbreviated title	SCOPES2019
Country/Territory	Germany
City	St. Goar
Period	27/05/19 → 28/05/19
Internet address	https://scopesconf.org/scopes-19/

Keywords

Application characterization
LLVM IR
Memory
Near-Memory Computing
Parallelism

Access to Document

10.1145/3323439.3323988

Cite this

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title = "Memory and parallelism analysis using a platform-independent approach",

abstract = "Emerging computing architectures such as near-memory computing (NMC) promise improved performance for applications by reducing the data movement between CPU and memory. However, detecting such applications is not a trivial task. In this ongoing work, we extend the state-of-the-art platform-independent software analysis tool with NMC related metrics such as memory entropy, spatial locality, data-level, and basic-block-level parallelism. These metrics help to identify the applications more suitable for NMC architectures.",

keywords = "Application characterization, LLVM IR, Memory, Near-Memory Computing, Parallelism",

author = "Stefano Corda and Gagandeep Singh and Awan, {Ahsan Javed} and Roel Jordans and Henk Corporaal",

year = "2019",

month = may,

day = "27",

doi = "10.1145/3323439.3323988",

language = "English",

pages = "23--26",

editor = "Sander Stuijk",

booktitle = "Proceedings of the 22nd International Workshop on Software and Compilers for Embedded Systems, SCOPES 2019",

publisher = "Association for Computing Machinery, Inc",

address = "United States",

note = "22nd International Workshop on Software and Compilers for Embedded Systems, (SCOPES2019), SCOPES2019 ; Conference date: 27-05-2019 Through 28-05-2019",

url = "https://scopesconf.org/scopes-19/",

}

Corda, S, Singh, G, Awan, AJ, Jordans, R & Corporaal, H 2019, Memory and parallelism analysis using a platform-independent approach. in S Stuijk (ed.), Proceedings of the 22nd International Workshop on Software and Compilers for Embedded Systems, SCOPES 2019. Association for Computing Machinery, Inc, New York, pp. 23-26, 22nd International Workshop on Software and Compilers for Embedded Systems, (SCOPES2019), St. Goar, Germany, 27/05/19. https://doi.org/10.1145/3323439.3323988

Memory and parallelism analysis using a platform-independent approach. / Corda, Stefano; Singh, Gagandeep; Awan, Ahsan Javed; Jordans, Roel ; Corporaal, Henk.

Proceedings of the 22nd International Workshop on Software and Compilers for Embedded Systems, SCOPES 2019. ed. / Sander Stuijk. New York : Association for Computing Machinery, Inc, 2019. p. 23-26.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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T1 - Memory and parallelism analysis using a platform-independent approach

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AU - Singh, Gagandeep

AU - Awan, Ahsan Javed

AU - Jordans, Roel

AU - Corporaal, Henk

PY - 2019/5/27

Y1 - 2019/5/27

N2 - Emerging computing architectures such as near-memory computing (NMC) promise improved performance for applications by reducing the data movement between CPU and memory. However, detecting such applications is not a trivial task. In this ongoing work, we extend the state-of-the-art platform-independent software analysis tool with NMC related metrics such as memory entropy, spatial locality, data-level, and basic-block-level parallelism. These metrics help to identify the applications more suitable for NMC architectures.

AB - Emerging computing architectures such as near-memory computing (NMC) promise improved performance for applications by reducing the data movement between CPU and memory. However, detecting such applications is not a trivial task. In this ongoing work, we extend the state-of-the-art platform-independent software analysis tool with NMC related metrics such as memory entropy, spatial locality, data-level, and basic-block-level parallelism. These metrics help to identify the applications more suitable for NMC architectures.

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KW - LLVM IR

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KW - Near-Memory Computing

KW - Parallelism

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A2 - Stuijk, Sander

PB - Association for Computing Machinery, Inc

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T2 - 22nd International Workshop on Software and Compilers for Embedded Systems, (SCOPES2019)

Y2 - 27 May 2019 through 28 May 2019

ER -

Memory and parallelism analysis using a platform-independent approach

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Cite this

Memory and parallelism analysis using a platform-independent approach

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

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NeMeCo

Cite this