Using focused regression for accurate time-constrained scaling of scientific applications

Barnes, Brittany F.; Garren, Jeonifer; Lowenthal, David K.; Reeves, Jaxk; Supinski, Bronis R. de; Schulz, Martin; Rountree, Barry

doi:10.1109/ipdps.2010.5470431

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…In [3], several program executions were used on a small subset of the processors to predict execution time on larger number of processors. An improved focused regression approach is proposed in [17] to study time-constrained scaling of scientific applications.…”

Section: Related Workmentioning

confidence: 99%

CAP3: A Cloud Auto-Provisioning Framework for Parallel Processing Using On-Demand and Spot Instances

Huang

Wang

Tak

et al. 2013

2013 IEEE Sixth International Conference on Cloud Computing

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Abstract-Cloud computing has drawn increasing attention from the scientific computing community due to its ease of use, elasticity, and relatively low cost. Because a high-performance computing (HPC) application is usually resource demanding, without careful planning, it can incur a high monetary expense even in Cloud. We design a tool called CAP 3 (Cloud AutoProvisioning framework for Parallel Processing) to help a user minimize the expense of running an HPC application in Cloud, while meeting the user-specified job deadline. Given an HPC application, CAP 3 automatically profiles the application, builds a model to predict its performance, and infers a proper cluster size that can finish the job within its deadline while minimizing the total cost. To further reduce the cost, CAP 3 intelligently chooses the Cloud's reliable on-demand instances or low-cost spot instances, depending on whether the remaining time is tight in meeting the application's deadline. Experiments on Amazon EC2 show that the execution strategy given by CAP 3 is costeffective, by choosing a proper cluster size and a proper instance type (on-demand or spot).

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Section: Related Workmentioning

confidence: 99%

CAP3: A Cloud Auto-Provisioning Framework for Parallel Processing Using On-Demand and Spot Instances

Huang

Wang

Tak

et al. 2013

2013 IEEE Sixth International Conference on Cloud Computing

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“…We hypothesize that the rate of growth required for problem size can be lower with iso-power-efficiency than with iso-efficiency, thus yielding better scalability. Our approach is to use a regression modeling methodology, similar to the focused regression modeling described in [3], to fit observed execution data to an iso-power-efficiency function.…”

Section: Chapter 1: Introductionmentioning

confidence: 99%

Iso-Power-Efficiency: An Approach to Scaling Application Codes with a Power Budget

Long

Moore

Rountree

2015

2015 IEEE International Parallel and Distributed Processing Symposium Workshop

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Ravelo. I offer my sincere appreciation for the learning opportunities provided by my committee. I am highly indebted to my parents. Without your support I would have most likely not made it through graduate school. Thank you for being the best parents anyone could ask for. I am grateful for all my friends for always throwing ideas around and for providing me an outlet for relieving stress. Finally, to my caring, loving, and supportive soon to be wife, Marisol: my upmost gratitude. Your encouragement is what got me through all the hard times. You are truly the best.

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Adaptive, transparent CPU scaling algorithms leveraging inter-node MPI communication regions

2011

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Using focused regression for accurate time-constrained scaling of scientific applications

Cited by 4 publications

References 36 publications

CAP3: A Cloud Auto-Provisioning Framework for Parallel Processing Using On-Demand and Spot Instances

CAP3: A Cloud Auto-Provisioning Framework for Parallel Processing Using On-Demand and Spot Instances

Iso-Power-Efficiency: An Approach to Scaling Application Codes with a Power Budget

Adaptive, transparent CPU scaling algorithms leveraging inter-node MPI communication regions

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