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Exploration of memory hierarchy configurations for efficient garbage collection on high-performance embedded systems
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Great Lakes Symposium on VLSI archive
Proceedings of the 19th ACM Great Lakes symposium on VLSI table of contents
Boston Area, MA, USA
SESSION: VLSI design table of contents
Pages 3-8  
Year of Publication: 2009
ISBN:978-1-60558-522-2
Authors
Jose Manuel Velasco  Complutense University of Madrid, Madrid, Spain
David Atienza  Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
Katzalin Olcoz  Complutense University of Madrid, Madrid, Spain
Sponsors
ACM: Association for Computing Machinery
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
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ABSTRACT

Modern embedded devices (e.g., PDAs, mobile phones) are now incorporating Java as a very popular implementation language in their designs. These new embedded systems include multiple applications that are dynamically launched by the user, which can produce very energy-hungry systems if the interactions between the applications and the garbage collectors (GCs) are not properly understood. In this paper we present a complete exploration, from an energy viewpoint, of the different possibilities of memory hierarchies for high-performance embedded systems when used by state-of-the-art GCs. Moreover, we explore the potential peformance improvement and energy reductions of using a scratchpad memory directed by the virtual machine to store critical code and data structures of the GCs; thus, enabling up to 40% performance improvements and 41% leakage reduction with respect to classical cache-based memory architectures. Our experimental results show that the key for an efficient low-power implementation of Java Virtual Machines (JVM) for high-performance embedded systems is the synergy between the GC choice, the memory architecture tuning, and the inclusion of power management schemes controlled by the JVM, exploiting knowledge of the used GC.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Jose Manuel Velasco: colleagues
David Atienza: colleagues
Katzalin Olcoz: colleagues