Using MPI (2nd ed.): portable parallel programming with the message-passing interface
Using MPI (2nd ed.): portable parallel programming with the message-passing interface
Using MPI-2: Advanced Features of the Message Passing Interface
Using MPI-2: Advanced Features of the Message Passing Interface
Scalable computation of streamlines on very large datasets
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Extreme Scaling of Production Visualization Software on Diverse Architectures
IEEE Computer Graphics and Applications
In Situ Visualization for Large-Scale Combustion Simulations
IEEE Computer Graphics and Applications
OpenMPC: Extended OpenMP Programming and Tuning for GPUs
Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis
A domain-specific approach to heterogeneous parallelism
Proceedings of the 16th ACM symposium on Principles and practice of parallel programming
Keeneland: Bringing Heterogeneous GPU Computing to the Computational Science Community
Computing in Science and Engineering
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
Parallel in situ coupling of simulation with a fully featured visualization system
EG PGV'11 Proceedings of the 11th Eurographics conference on Parallel Graphics and Visualization
Mesh independent loop fusion for unstructured mesh applications
Proceedings of the 9th conference on Computing Frontiers
A classification of scientific visualization algorithms for massive threading
UltraVis '13 Proceedings of the 8th International Workshop on Ultrascale Visualization
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The coming generation of supercomputing architectures will require fundamental changes in programming models to effectively make use of the expected million to billion way concurrency and thousand-fold reduction in per-core memory. Most current parallel analysis and visualization tools achieve scalability by partitioning the data, either spatially or temporally, and running serial computational kernels on each data partition, using message passing as needed. These techniques lack the necessary level of data parallelism to execute effectively on the underlying hardware. This paper introduces a framework that enables the expression of analysis and visualization algorithms with memory-efficient execution in a hybrid distributed and data parallel manner on both multi-core and many-core processors. We demonstrate results on scientific data using CPUs and GPUs in scalable heterogeneous systems.