Title page for ETD etd-05242007-220451

Type of Document Master's Thesis
Author Pyla, Hari Krishna
Author's Email Address harip@vt.edu
URN etd-05242007-220451
Title Tempest: A Framework for High Performance Thermal-Aware Distributed Computing
Degree Master of Science
Department Computer Science
Advisory Committee
Advisor Name Title
Varadarajan, Srinidhi Committee Chair
Cameron, Kirk W. Committee Co-Chair
Ramakrishnan, Naren Committee Member
Ribbens, Calvin J. Committee Member
  • parallel processing
  • thermal profiling
Date of Defense 2007-05-18
Availability unrestricted
Compute clusters are consuming more power at higher densities than ever before. This results in increased thermal dissipation, the need for powerful cooling systems, and ultimately a reduction in system reliability as temperatures increase. Over the past several years, the research community has reacted to this problem by producing software tools such as HotSpot and Mercury to estimate system thermal characteristics and validate thermal-management techniques. While these tools are flexible and useful, they suffer several limitations: for the average user such simulation tools can be cumbersome to use, these tools may take significant time and expertise to port to different systems. Further, such tools produce significant detail and accuracy at the expense of execution time enough to prohibit iterative testing. We propose a fast, easy to use, accurate, portable, software framework called Tempest (for temperature estimator) that leverages emergent thermal sensors to enable user profiling, evaluating, and reducing the thermal characteristics of systems and applications.

In this thesis, we illustrate the use of Tempest to analyze the thermal effects of various parallel benchmarks in clusters. We also show how users can analyze the effects of thermal optimizations on cluster applications. Dynamic Voltage and Frequency Scaling (DVFS) reduces the power consumption of high-performance clusters by reducing processor voltage during periods of low utilization. We designed Tempest to measure the runtime effects of processor frequency on thermals. Our experiments indicate HPC workload characteristics greatly impact the effects of DVFS on temperature. We propose a thermal-aware DVFS scheduling approach that proactively controls processor voltage across a cluster by evaluating and predicting trends in processor temperature. We identify approaches that can maintain temperature thresholds and reduce temperature with minimal impact on performance. Our results indicate that proactive, temperature-aware scheduling of DVFS can reduce cluster-wide processor thermals by more than 10 degrees Celsius, the threshold for improving electronic reliability by 50%.

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