Distributed run-time resource management for malleable applications on many-core platforms

Todays prevalent solutions for modern embedded systems and general computing employ many processing units connected by an on-chip network leaving behind complex superscalar architectures In this paper, we couple the concept of distributed computing with parallel applications and present a workload-aware distributed run-time framework for malleable applications on many-core platforms. The presented framework is responsible for serving in a distributed way and at run-time, the needs of malleable applications, maximizing resource utilization avoiding dominating effects and taking into account the type of processors supporting platform heterogeneity, while having a small overhead in overall inter-core communication. Our framework has been implemented as part of a C simulator and additionally as a runtime service on the Single-Chip Cloud Computer (SCC), an experimental processor created by Intel Labs, and we compared it against a state-of-art run-time resource manager. Experimental results showed that our framework has on average 70% less messages, 64% smaller message size and 20% application speed-up gain.