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Efficiently utilizing the computational resources of many core systems is one of the most prominent challenges. The problem worsens when resource requirements vary unpredictably and applications may be started/stopped at any time. To address this challenge, we propose two schemes that calculate and adapt task mappings at runtime: a centralized, optimal mapping scheme and a distributed, hierarchical mapping scheme that trades optimality for a high degree of scalability. Experiments on Intel's 48-core Single-Chip Cloud Computer and in a many core simulator show that a significant improvement in system performance can be achieved over current state-of-the-art.

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Systems continue to comprise a rapidly growing number of cores on a single chip to gain performance benefits from parallel processing. A key challenge is how their computational resources can be used efficiently, which depends to a large degree on how their resources are allocated to the applications. In this paper, we describe our current research for addressing this challenge and highlight current and upcoming hurdles that need to be addressed.

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