SSDM: Smart stack data management for software managed multicores (SMMs)

Jing Lu, Ke Bai, Aviral Shrivastava

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

31 Scopus citations


Software Managed Multicore (SMM) architectures have been proposed as a solution for scaling the memory architecture. In an SMM architecture, there are no caches, and each core has only a local scratchpad memory. If all the code and data of the task to be executed on an SMM core cannot fit on the local memory, then data must be managed explicitly in the program through DMA instructions. While all code and data need to be managed, an efficient technique to manage stack data is of utmost importance since an average of 64% of all accesses may be to stack variables [16]. In this paper, we formulate the problem of stack data management optimization on an SMM core. We then develop both an ILP and a heuristic - SSDM (Smart Stack Data Management) to find out where to insert stack data management calls in the program. Experimental results demonstrate SSDM can reduce the overhead by 13X over the state-of-the-art stack data management technique [10]. Categories and Subject Descriptors D.3.4 [Software]: Processors-Code generation, Compilers, Optimization General Terms Algorithm, Design, Experimentation, Performance.

Original languageEnglish (US)
Title of host publicationProceedings of the 50th Annual Design Automation Conference, DAC 2013
StatePublished - 2013
Event50th Annual Design Automation Conference, DAC 2013 - Austin, TX, United States
Duration: May 29 2013Jun 7 2013

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X


Other50th Annual Design Automation Conference, DAC 2013
Country/TerritoryUnited States
CityAustin, TX


  • Embedded systems
  • Local memory
  • Multi-core processor
  • SPM
  • Scratchpad memory
  • Stack data

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation


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