Platform independent software analysis for near memory computing

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

6 Citations (Scopus)
75 Downloads (Pure)


Near-memory Computing (NMC) promises improved performance for the applications that can exploit the features of emerging memory technologies such as 3D-stacked memory. However, it is not trivial to find such applications and specialized tools are needed to identify them. In this paper, we present PISA-NMC, which extends a state-of-the-art hardware agnostic profiling tool with metrics concerning memory and parallelism, which are relevant for NMC. The metrics include memory entropy, spatial locality, data-level, and basic-block-level parallelism. By profiling a set of representative applications and correlating the metrics with the application's performance on a simulated NMC system, we verify the importance of those metrics. Finally, we demonstrate which metrics are useful in identifying applications suitable for NMC architectures.
Original languageEnglish
Title of host publicationProceedings of the 22nd Euromicro Conference on Digital System Design
EditorsNikos Konofaos, Paris Kitsos
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages4
ISBN (Electronic)978-1-7281-2862-7
Publication statusPublished - 24 Jun 2019
Event22nd Euromicro Conference on Digital System Design, DSD 2019 - Kallithea, Kallithea, Chalkidiki, Greece
Duration: 28 Aug 201930 Aug 2019
Conference number: 22


Conference22nd Euromicro Conference on Digital System Design, DSD 2019
Abbreviated titleDSD 2019
CityKallithea, Chalkidiki
Internet address


  • cs.PF
  • cs.ET
  • Memory Entropy
  • LLVM
  • NMC
  • Spatial Locality
  • Data-Level Parallelism


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