COTSon: infrastructure for full system simulation

  • Authors:
  • Eduardo Argollo;Ayose Falcón;Paolo Faraboschi;Matteo Monchiero;Daniel Ortega

  • Affiliations:
  • HP Labs -- Exascale Computing Lab;HP Labs -- Exascale Computing Lab;HP Labs -- Exascale Computing Lab;HP Labs -- Exascale Computing Lab;HP Labs -- Exascale Computing Lab

  • Venue:
  • ACM SIGOPS Operating Systems Review
  • Year:
  • 2009

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Abstract

Simulation has historically been the primary technique used for evaluating the performance of new proposals in computer architecture. Speed and complexity considerations have traditionally limited its applicability to single-thread processors running application-level code. This is no longer sufficient to model modern multicore systems running the complex workloads of commercial interest today. COTSon is a simulator framework jointly developed by HP Labs and AMD. The goal of COTSon is to provide fast and accurate evaluation of current and future computing systems, covering the full software stack and complete hardware models. It targets cluster-level systems composed of hundreds of commodity multicore nodes and their associated devices connected through a standard communication network. COTSon adopts a functional-directed philosophy, where fast functional emulators and timing models cooperate to improve the simulation accuracy at a speed sufficient to simulate the full stack of applications, middleware and OSs. This paper describes the changes in simulation philosophy we embraced in COTSon to address these new challenges. We base functional emulation on established, fast and validated tools that support commodity OSs and complex multitier applications. Through a robust interface between the functional and timing domain, we can leverage other existing simulators for individual sub-components, such as disks or networks. We abandon the idea of "always-on" cycle-based simulation in favor of statistical sampling approaches that can trade accuracy for speed. COTSon opens up a new dimension in the speed/accuracy space, allowing simulation of a cluster of nodes several orders of magnitude faster with a minimal accuracy loss.