Learning to Classify Parallel Input/Output Access Patterns
IEEE Transactions on Parallel and Distributed Systems
The Tau Parallel Performance System
International Journal of High Performance Computing Applications
Tool Support for Inspecting the Code Quality of HPC Applications
ICSEW '07 Proceedings of the 29th International Conference on Software Engineering Workshops
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Automatic Memory Access Analysis with Periscope
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
A view of the parallel computing landscape
Communications of the ACM - A View of Parallel Computing
A comparison of high-level full-system power models
HotPower'08 Proceedings of the 2008 conference on Power aware computing and systems
Modeling and simulation of data center energy-efficiency in coolemall
E2DC'12 Proceedings of the First international conference on Energy Efficient Data Centers
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With the rise of Clouds and PaaS (Platform as a Service) usage, providers of large computing facilities are completely disconnected from users running jobs on their infrastructure. Thus, the old adage knowledge is power has never been so true. By having good insight on application running on their infrastructure, providers can save up to 30% of their energy consumption while not impacting too much applications. Without access to application source code, it can be quite difficult to have a precise vision of the type of application. For instance, in NAS Parallel Benchmark (NPB), seven different benchmarks are available and have different behaviors (memory consumption patterns, performance decreasing with processor frequency,...) but discriminating between them can be costly due to the monitoring infrastructure. In this article we show that using power consumption of hosts we can discriminate between applications with nearly no impact on the application execution and without a-priori knowledge.