Tessellation: refactoring the OS around explicit resource containers with continuous adaptation

  • Authors:

  • Juan A. Colmenares;Gage Eads;Steven Hofmeyr;Sarah Bird;Miquel Moretó;David Chou;Brian Gluzman;Eric Roman;Davide B. Bartolini;Nitesh Mor;Krste Asanović;John D. Kubiatowicz

  • Affiliations:

  • The Parallel Computing Laboratory, UC Berkeley, Berkeley, CA and Samsung Research America - Silicon Valley, San Jose, CA;The Parallel Computing Laboratory, UC Berkeley, Berkeley, CA;Lawrence Berkeley National Laboratory, Berkeley, CA;The Parallel Computing Laboratory, UC Berkeley, Berkeley, CA;The Parallel Computing Laboratory, UC Berkeley, Berkeley, CA;The Parallel Computing Laboratory, UC Berkeley, Berkeley, CA;The Parallel Computing Laboratory, UC Berkeley, Berkeley, CA;The Parallel Computing Laboratory, UC Berkeley, Berkeley, CA;The Parallel Computing Laboratory, UC Berkeley, Berkeley, CA;The Parallel Computing Laboratory, UC Berkeley, Berkeley, CA;The Parallel Computing Laboratory, UC Berkeley, Berkeley, CA;The Parallel Computing Laboratory, UC Berkeley, Berkeley, CA

  • Venue:
  • Proceedings of the 50th Annual Design Automation Conference
  • Year:
  • 2013

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Abstract

Adaptive Resource-Centric Computing (ARCC) enables a simultaneous mix of high-throughput parallel, real-time, and interactive applications through automatic discovery of the correct mix of resource assignments necessary to achieve application requirements. This approach, embodied in the Tessellation manycore operating system, distributes resources to QoS domains called cells. Tessellation separates global decisions about the allocation of resources to cells from application-specific scheduling of resources within cells. We examine the implementation of ARCC in the Tessellation OS, highlight Tessellation's ability to provide predictable performance, and investigate the performance of Tessellation services within cells.