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
Mesh independent loop fusion for unstructured mesh applications
Proceedings of the 9th conference on Computing Frontiers
Proceedings of the 1st ACM SIGPLAN workshop on Functional high-performance computing
Predictive modeling and analysis of OP2 on distributed memory GPU clusters
ACM SIGMETRICS Performance Evaluation Review
Abstractions to separate concerns in semi-regular grids
Proceedings of the 27th international ACM conference on International conference on supercomputing
Vectorizing Unstructured Mesh Computations for Many-core Architectures
Proceedings of Programming Models and Applications on Multicores and Manycores
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This paper presents a benchmarking, performance analysis and optimization study of the OP2 ‘active’ library, which provides an abstraction framework for the parallel execution of unstructured mesh applications. OP2 aims to decouple the scientific specification of the application from its parallel implementation, and thereby achieve code longevity and near-optimal performance through re-targeting the application to execute on different multi-core/many-core hardware. Runtime performance results are presented for a representative unstructured mesh application on a variety of many-core processor systems, including traditional X86 architectures from Intel (Xeon based on the older Penryn and current Nehalem micro-architectures) and GPU offerings from NVIDIA (GTX260, Tesla C2050). Our analysis demonstrates the contrasting performance between the use of CPU (OpenMP) and GPU (CUDA) parallel implementations for the solution of an industrial-sized unstructured mesh consisting of about 1.5脗聽million edges. Results show the significance of choosing the correct partition and thread-block configuration, the factors limiting the GPU performance and insights into optimizations for improved performance.