Modeling the performance of an algebraic multigrid cycle on HPC platforms
Proceedings of the international conference on Supercomputing
Algebraic Multigrid for Linear Systems Obtained by Explicit Element Reduction
SIAM Journal on Scientific Computing
Multigrid Smoothers for Ultraparallel Computing
SIAM Journal on Scientific Computing
Past, present and future scalability of the Uintah software
Proceedings of the Extreme Scaling Workshop
Determination of performance characteristics of scientific applications on IBM Blue Gene/Q
IBM Journal of Research and Development
Enabling fair pricing on HPC systems with node sharing
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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Algebraic multigrid (AMG) is a popular solver for large-scale scientific computing and an essential component of many simulation codes. AMG has shown to be extremely efficient on distributed-memory architectures. However, when executed on modern multicore architectures, we face new challenges that can significantly deteriorate AMG's performance. We examine its performance and scalability on three disparate multicore architectures: a cluster with four AMD Opteron Quad-core processors per node (Hera), a Cray XT5 with two AMD Opteron Hex-core processors per node (Jaguar), and an IBM Blue Gene/P system with a single Quad-core processor (Intrepid). We discuss our experiences on these platforms and present results using both an MPI-only and a hybrid MPI/OpenMP model. We also discuss a set of techniques that helped to overcome the associated problems, including thread and process pinning and correct memory associations.