Automatic local memory management for multicores having global address space

Kouhei Yamamoto, Tomoya Shirakawa, Yoshitake Oki, Akimasa Yoshida, Keiji Kimura, Hironori Kasahara*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


Embedded multicore processors for hard real-time applications like automobile engine control require the usage of local memory on each processor core to precisely meet the real-time deadline constraints, since cache memory cannot satisfy the deadline requirements due to cache misses. To utilize local memory, programmers or compilers need to explicitly manage data movement and data replacement for local memory considering the limited size. However, such management is extremely difficult and time consuming for programmers. This paper proposes an automatic local memory management method by compilers through (i) multi-dimensional data decomposition techniques to fit working sets onto limited size local memory (ii) suitable block management structures, called Adjustable Blocks, to create application specific fixed size data transfer blocks (iii) multi-dimensional templates to preserve the original multi-dimensional representations of the decomposed multi-dimensional data that are mapped onto one-dimensional Adjustable Blocks (iv) block replacement policies from liveness analysis of the decomposed data, and (v) code size reduction schemes to generate shorter codes. The proposed local memory management method is implemented on the OSCAR multigrain and multi-platform compiler and evaluated on the Renesas RP2 8 core embedded homogeneous multicore processor equipped with local and shared memory. Evaluations on 5 programs including multimedia and scientific applications show promising results. For instance, speedups on 8 cores compared to single core execution using off-chip shared memory on an AAC encoder program, a MPEG2 encoder program, Tomcatv, and Swim are improved from 7.14 to 20.12, 1.97 to 7.59, 5.73 to 7.38, and 7.40 to 11.30, respectively, when using local memory with the proposed method. These evaluations indicate the usefulness and the validity of the proposed local memory management method on real embedded multicore processors.

Original languageEnglish
Title of host publicationLanguages and Compilers for Parallel Computing - 29th International Workshop, LCPC 2016, Revised Papers
EditorsChen Ding, John Criswell, Peng Wu
PublisherSpringer Verlag
Number of pages15
ISBN (Print)9783319527086
Publication statusPublished - 2017
Event29th International Workshop on Languages and Compilers for Parallel Computing, LCPC 2016 - Rochester, United States
Duration: 2016 Sept 282016 Sept 30

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10136 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other29th International Workshop on Languages and Compilers for Parallel Computing, LCPC 2016
Country/TerritoryUnited States


  • DMA
  • Data decomposition
  • Global address space
  • Local memory management
  • Multicore
  • Parallelizing compiler

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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