in Proc. of the 7th International Supercomputing Conference in Mexico (ISUM 2016) (2016)
in Proceedings of the Fourth International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering (2015, February)
As Cloud Computing services become ever more prominent, it appears necessary to assess the efficiency of these solutions. This paper presents a performance evaluation of the OpenStack Cloud Computing middleware using our XDEM application simulating the pyrolysis of biomass as a benchmark. We propose a systematic study based on a fully automated benchmarking framework to evaluate 3 different configurations: Native (i.e. no virtualization), OpenStack with KVM and XEN hypervisors. Our approach features the following advantages: real user application, the fair comparison using the same hardware, the large scale distributed execution, while fully automated and reproducible. Experiments has been run on two different clusters, using up to 432 cores. Results show a moderate overhead for sequential execution and a significant penalty for distributed execution under the Cloud middleware. The overhead on multiple nodes is between 10% and 30% for OpenStack/KVM and 30% and 60% for OpenStack/XEN.
in Proceedings of the Third International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering (2013)
This paper presents the enhanced design of the Discrete Particle Method (DPM), a simulation tool which provides high quality and fast simulations to solve a broad range industrial processes involving granular materials. It enables to resolve mechanical and thermodynamics problems through different simulation modules (motions, chemical conversion). This new design allows to transparently couple the simulation modules in parallel execution. It relies on a unified interface and timebase of the simulation modules and a flexible decomposition in cells of the simulation space. Experimental results study the behavior of the Orthogonal Recursive Bisection (ORB) partitioning algorithm. A good scalability is achieved as the parallel execution on a distributed platform provides a 17-times speedup using 64 processes.