Dynamic partitioning of processing and memory resources in embedded MPSoC architectures

  • Authors:

  • Liping Xue;Ozcan ozturk;Feihui Li;Mahmut Kandemir;I. Kolcu

  • Affiliations:

  • Pennsylvania State University, University Park, PA;Pennsylvania State University, University Park, PA;Pennsylvania State University, University Park, PA;Pennsylvania State University, University Park, PA;UMIST, Manchester, UK

  • Venue:
  • Proceedings of the conference on Design, automation and test in Europe: Proceedings
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

Current trends indicate that multiprocessor-system-on-chip (MPSoC) architectures are being increasingly used in building complex embedded systems. While circuit/architectural support for MPSoC based systems are making significant strides, programming these devices and providing suitable software support (e.g., compiler and operating systems) seem to be a tougher problem. This is because either programmers or compilers will have to make code explicitly parallel to run on these systems. An additional difficulty occurs when multiple applications use an MPSoC at the same time, because MPSoC resources should be partitioned across these applications carefully. This paper explores a proactive resource partitioning scheme for parallel applications simultaneously exercising the same MPSoC system. The proposed approach has two major components. The first component includes an offline preprocessing of applications which gives us an estimated profile for each application. Each application to be executed on our MPSoC is profiled and annotated with the profile information. The second component of our approach is an online resource partitioning, which partitions both the processing cores (i.e., computation resources) and on-chip memory space (i.e., storage resource) among simultaneously-executing applications. Our experimental evaluation with this partitioner shows that it generates much better results than conventional operating system based resource management. The results also reveal that both memory partitioning and processor partitioning are very important for obtaining the best results.