Mapping of Applications to MPSoCs - Marwedel, Peter; Teich, Jürgen; Kouveli, Georgia; Bacivarov, Iuliana; Thiele, Lothar; Ha, Soonhoi; Lee, Chanhee; Xu, Qiang; Huang, Lin (Proceedings of the 9th International Conference on Hardware/Software Codesign and System Synthesis, part of ESWeek'11, 2011)
The advent of embedded many-core architectures results in the need to come up with techniques for mapping embedded applications onto such architectures. This paper presents a representative set of such techniques. The techniques focus on optimizing performance, temperature distribution, reliability and fault tolerance for various models. Copyright 2011 ACM.
UnivIS-Import:2015-04-16:Pub.2011.tech.IMMD.inform.mappin
Keywords: Application mapping; Embedded systems; Multi-processor systems on a chip (mpsocs)

Towards Resource-Aware Programming on Intel's Single-Chip Cloud Computer Processor - Kouveli, Georgia; Hannig, Frank; Lupp, Jan-Hugo; Teich, Jürgen (Proceedings of the 3rd MARC Symposium, 2011)
In this paper, we apply a new programming paradigm called resource-aware programming to the Single-Chip Cloud Computer. According to this paradigm, an application may change at certain points of execution its allocation of resources. This gives application engineers the opportunity to dynamically adapt an algorithm's behavior and parallelism to the work load and state of the underlying resources (e. g., availability, clock frequency, temperature). Resource-aware programming can provide a selforganizing behavior to conventional programs for being able to not only tolerate certain types of faults and cope with feature variations, but also to provide scalability, higher resource utilization, as well as performance and power gains by managing voltage/frequency islands and adjusting the amount of allocated resources to the temporal needs of a running application. We discuss the details of resource-aware programming as well as three alternative implementation concepts that we intend to evaluate. Finally, we present the results of initial experiments, we conducted using a centralized resource management framework on the Single- Chip Cloud Computer.
UnivIS-Import:2015-04-16:Pub.2011.tech.IMMD.inform.toward_0