Title :
Evaluating High-Performance Computing on Google App Engine
Author :
Prodan, Radu ; Sperk, Michael ; Ostermann, Simon
Author_Institution :
Univ. of Innsbruck, Innsbruck, Austria
Abstract :
An experimental approach employs the Google App Engine (GAE) for high-performance parallel computing. A generic master-slave framework enables fast prototyping and integration of parallel algorithms that are transparently scheduled and executed on the Google cloud infrastructure. Compared to Amazon Elastic Compute Cloud (EC2), GAE offers lower resource-provisioning overhead and is cheaper for jobs shorter than one hour. Experiments demonstrated good scalability of a Monte Carlo simulation algorithm. Although this approach produced important speedup, two main obstacles limited its performance: middleware overhead and resource quotas.
Keywords :
Monte Carlo methods; cloud computing; middleware; parallel algorithms; Amazon Elastic Compute Cloud; GAE; Google App Engine; Google cloud infrastructure; Monte Carlo simulation algorithm; generic master-slave framework; high-performance parallel computing; middleware overhead; parallel algorithm; resource quotas; resource-provisioning overhead; Computational modeling; Computer applications; Computer performance; Google; Parallel processing; Servers; Amazon EC2; Amazon Elastic Compute Cloud; GAE; Google App Engine; cloud computing; high-performance computing; performance analysis; software engineering;
Journal_Title :
Software, IEEE
DOI :
10.1109/MS.2011.131
