A framework approach for developing parallel adaptive multiphysics applications
Finite Elements in Analysis and Design - Special issue: The fifteenth annual Robert J. Melosh competition
Deriving Efficient Data Movement from Decoupled Access/Execute Specifications
HiPEAC '09 Proceedings of the 4th International Conference on High Performance Embedded Architectures and Compilers
Performance analysis of the OP2 framework on many-core architectures
ACM SIGMETRICS Performance Evaluation Review - Special issue on the 1st international workshop on performance modeling, benchmarking and simulation of high performance computing systems (PMBS 10)
Performance analysis of a hybrid MPI/CUDA implementation of the NASLU benchmark
ACM SIGMETRICS Performance Evaluation Review - Special issue on the 1st international workshop on performance modeling, benchmarking and simulation of high performance computing systems (PMBS 10)
Predictive analysis of a hydrodynamics application on large-scale CMP clusters
Computer Science - Research and Development
Liszt: a domain specific language for building portable mesh-based PDE solvers
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
Predictive modeling and analysis of OP2 on distributed memory GPU clusters
Proceedings of the second international workshop on Performance modeling, benchmarking and simulation of high performance computing systems
| Hi-index | 0.00 |
OP2 is an "active" library framework for the development and solution of unstructured mesh based applications. It aims to decouple the scientific specification of an application from its parallel implementation to achieve code longevity and near-optimal performance through re-targeting the backend to different multi-core/many-core hardware. This paper presents a predictive performance analysis and benchmarking study of OP2 on heterogeneous cluster systems. We first present the design of a new OP2 back-end that enables the execution of applications on distributed memory clusters, and benchmark its performance during the solution of a 1.5M and 26M edge-based CFD application written using OP2. Benchmark systems include a large-scale CrayXE6 system and an Intel Westmere/InfiniBand cluster. We then apply performance modeling to predict the application's performance on an NVIDIA Tesla C2070 based GPU cluster, enabling us to compare OP2's performance capabilities on emerging distributed memory heterogeneous systems. Results illustrate the performance benefits that can be gained through many-core solutions both on single-node and heterogeneous configurations in comparison to traditional homogeneous cluster systems for this class of applications.