Cross-architecture performance predictions for scientific applications using parameterized models
Proceedings of the joint international conference on Measurement and modeling of computer systems
Rodinia: A benchmark suite for heterogeneous computing
IISWC '09 Proceedings of the 2009 IEEE International Symposium on Workload Characterization (IISWC)
On the Use of Machine Learning to Predict the Time and Resources Consumed by Applications
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Magellan: experiences from a science cloud
Proceedings of the 2nd international workshop on Scientific cloud computing
Understanding scheduling implications for scientific applications in clouds
Proceedings of the 9th International Workshop on Middleware for Grids, Clouds and e-Science
Snow White Clouds and the Seven Dwarfs
CLOUDCOM '11 Proceedings of the 2011 IEEE Third International Conference on Cloud Computing Technology and Science
Predicting application performance for multi-vendor clouds using dwarf benchmarks
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
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Scientific computing often requires high performance and distributed computational resources to perform large scale experiments in order to achieve accurate results in due course. These needs have been addressed with dedicated high performance computing (HPC) infrastructures. New models to achieve high performance are the combined multi-core architectures with accelerators (manycore). A new dimension is also added by the Cloud Computing model, which has garnered significant attention from industrial and scientific community, as a potential model to address a broad array of computing needs. Even so, it is necessary to evaluate how scientific applications behave on these models. In this work, we propose an approach based on the Dwarfs classification to evaluate architectures, considering that applications have different characteristics in terms of resource consumption. The results show how the Dwarfs approach could help in choosing the architecture and in Cloud Computing environment evaluation when looking for better performance.