@inproceedings{francois2014volume,

title = {VOLUME-OF-FLUID INTERFACE RECONSTRUCTION ALGORITHMS ON NEXT-GENERATION COMPUTER ARCHITECTURES},

author = {Marianne Francois and Li-Ta Lo and Christopher Sewell},

url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/Volume-Of-FluidInterfaceReconstructionAlgorighmsOnNext-GenerationComputerArchitectures.pdf},

year  = {2014},

date = {2014-01-01},

booktitle = {Proceedings of the ASME},

abstract = {With the increasing heterogeneity and on-node parallelism of high-performance computing hardware, a major challenge to computational physicists is to work in close collaboration with computer scientists to develop portable and efficient algorithms and software. The objective of our work is to implement a portable code to perform interface reconstruction using NVIDIA’s Thrust library. Interface reconstruction is a technique commonly used in volume tracking methods for simulations of interfacial flows. For that, we have designed a two-dimensional mesh data structure that is easily mapped to the 1D vectors used by Thrust and at the same time is simple to work with using familiar data structures terminology (such as cell, vertices and edges). With this new data structure in place, we have implemented a recursive volume-of-fluid initialization algorithm and a standard piecewise interface reconstruction algorithm. Our interface reconstruction algorithm makes use of a table look-up to easily identify all intersection cases, as this design is efficient on parallel architectures such as GPUs. Finally, we report performance results which show that a single implementation of these algorithms can be compiled to multiple backends (specifically, multi-core CPUs, NVIDIA GPUs, and Intel Xeon Phi coprocessors), making efficient use of the available parallelism on each.},

note = {LA-UR-14-20777},

keywords = {COMPUTER ARCHITECTURES, INTERFACE RECONSTRUCTION, PISTON},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sewell2013portable,

title = {Portable data-parallel visualization and analysis in distributed memory environments},

author = {Christopher Sewell and Li-ta Lo and James Ahrens},

url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/PortableData-ParallelVisualizationAndAnalysisInDistributedMemoryEnvironments.pdf},

year  = {2013},

date = {2013-01-01},

booktitle = {Large-Scale Data Analysis and Visualization (LDAV), 2013 IEEE Symposium on},

pages = {25--33},

organization = {IEEE},

abstract = {Data-parallelism is a programming model that maps well to architectures with a high degree of concurrency. Algorithms written using data-parallel primitives can be easily ported to any architecture for which an implementation of these primitives exists, making efficient use of the available parallelism on each. We have previously published results demonstrating our ability to compile the same data-parallel code for several visualization algorithms onto different on-node parallel architectures (GPUs and multi-core CPUs) using our extension of NVIDIAÕs Thrust library. In this paper, we discuss our extension of Thrust to support concurrency in distributed memory environments across multiple nodes. This enables the application developer to write data-parallel algorithms while viewing the data as single, long vectors, essentially without needing to explicitly take into consideration whether the values are actually distributed across nodes. Our distributed wrapper for Thrust handles the communication in the backend using MPI, while still using the standard Thrust library to take advantage of available on-node parallelism. We describe the details of our distributed implementations of several key data-parallel primitives, including scan, scatter/ gather, sort, reduce, and upper/lower bound. We also present two higher-level distributed algorithms developed using these primitives: isosurface and KD-tree construction. Finally, we provide timing results demonstrating the ability of these algorithms to take advantage of available parallelism on nodes and across multiple nodes, and discuss scaling limitations for communication-intensive algorithms such as KD-tree construction.},

note = {LA-UR-13-23809},

keywords = {analysis, Concurrent Programming, data-parallel, distributed memory, parallel programming, PISTON, visualization},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lo2012piston,

title = {PISTON: A Portable Cross-Platform Framework for Data-Parallel Visualization Operators.},

author = {Li-ta Lo and Christopher Sewell and James Ahrens},

url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/PISTONAPortableCrossPlatformFrameworkForData-ParallelVisualizationOperators.pdf},

year  = {2012},

date = {2012-01-01},

booktitle = {EGPGV},

pages = {11--20},

abstract = {Due to the wide variety of current and next-generation supercomputing architectures, the development of highperformance parallel visualization and analysis operators frequently requires re-writing the underlying algorithms for many different platforms. In order to facilitate portability, we have devised a framework for creating such operators that employs the data-parallel programming model. By writing the operators using only data-parallel primitives (such as scans, transforms, stream compactions, etc.), the same code may be compiled to multiple targets using architecture-specific backend implementations of these primitives. Specifically, we make use of and extend NVIDIAÕs Thrust library, which provides CUDA and OpenMP backends. Using this framework, we have implemented isosurface, cut surface, and threshold operators, and have achieved good parallel performance on two different architectures (multi-core CPUs and NVIDIA GPUs) using the exact same operator code. We have applied these operators to several large, real scientific data sets, and have open-source released a beta version of our code base.},

note = {LA-UR-12-10227},

keywords = {Concurrent Programming, parallel programming, PISTON},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{francois2014volume,

title = {VOLUME-OF-FLUID INTERFACE RECONSTRUCTION ALGORITHMS ON NEXT-GENERATION COMPUTER ARCHITECTURES},

author = {Marianne Francois and Li-Ta Lo and Christopher Sewell},

url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/Volume-Of-FluidInterfaceReconstructionAlgorighmsOnNext-GenerationComputerArchitectures.pdf},

year  = {2014},

date = {2014-01-01},

booktitle = {Proceedings of the ASME},

abstract = {With the increasing heterogeneity and on-node parallelism of high-performance computing hardware, a major challenge to computational physicists is to work in close collaboration with computer scientists to develop portable and efficient algorithms and software. The objective of our work is to implement a portable code to perform interface reconstruction using NVIDIA’s Thrust library. Interface reconstruction is a technique commonly used in volume tracking methods for simulations of interfacial flows. For that, we have designed a two-dimensional mesh data structure that is easily mapped to the 1D vectors used by Thrust and at the same time is simple to work with using familiar data structures terminology (such as cell, vertices and edges). With this new data structure in place, we have implemented a recursive volume-of-fluid initialization algorithm and a standard piecewise interface reconstruction algorithm. Our interface reconstruction algorithm makes use of a table look-up to easily identify all intersection cases, as this design is efficient on parallel architectures such as GPUs. Finally, we report performance results which show that a single implementation of these algorithms can be compiled to multiple backends (specifically, multi-core CPUs, NVIDIA GPUs, and Intel Xeon Phi coprocessors), making efficient use of the available parallelism on each.},

note = {LA-UR-14-20777},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sewell2013portable,

title = {Portable data-parallel visualization and analysis in distributed memory environments},

author = {Christopher Sewell and Li-ta Lo and James Ahrens},

url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/PortableData-ParallelVisualizationAndAnalysisInDistributedMemoryEnvironments.pdf},

year  = {2013},

date = {2013-01-01},

booktitle = {Large-Scale Data Analysis and Visualization (LDAV), 2013 IEEE Symposium on},

pages = {25--33},

organization = {IEEE},

abstract = {Data-parallelism is a programming model that maps well to architectures with a high degree of concurrency. Algorithms written using data-parallel primitives can be easily ported to any architecture for which an implementation of these primitives exists, making efficient use of the available parallelism on each. We have previously published results demonstrating our ability to compile the same data-parallel code for several visualization algorithms onto different on-node parallel architectures (GPUs and multi-core CPUs) using our extension of NVIDIAÕs Thrust library. In this paper, we discuss our extension of Thrust to support concurrency in distributed memory environments across multiple nodes. This enables the application developer to write data-parallel algorithms while viewing the data as single, long vectors, essentially without needing to explicitly take into consideration whether the values are actually distributed across nodes. Our distributed wrapper for Thrust handles the communication in the backend using MPI, while still using the standard Thrust library to take advantage of available on-node parallelism. We describe the details of our distributed implementations of several key data-parallel primitives, including scan, scatter/ gather, sort, reduce, and upper/lower bound. We also present two higher-level distributed algorithms developed using these primitives: isosurface and KD-tree construction. Finally, we provide timing results demonstrating the ability of these algorithms to take advantage of available parallelism on nodes and across multiple nodes, and discuss scaling limitations for communication-intensive algorithms such as KD-tree construction.},

note = {LA-UR-13-23809},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lo2012piston,

title = {PISTON: A Portable Cross-Platform Framework for Data-Parallel Visualization Operators.},

author = {Li-ta Lo and Christopher Sewell and James Ahrens},

url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/PISTONAPortableCrossPlatformFrameworkForData-ParallelVisualizationOperators.pdf},

year  = {2012},

date = {2012-01-01},

booktitle = {EGPGV},

pages = {11--20},

abstract = {Due to the wide variety of current and next-generation supercomputing architectures, the development of highperformance parallel visualization and analysis operators frequently requires re-writing the underlying algorithms for many different platforms. In order to facilitate portability, we have devised a framework for creating such operators that employs the data-parallel programming model. By writing the operators using only data-parallel primitives (such as scans, transforms, stream compactions, etc.), the same code may be compiled to multiple targets using architecture-specific backend implementations of these primitives. Specifically, we make use of and extend NVIDIAÕs Thrust library, which provides CUDA and OpenMP backends. Using this framework, we have implemented isosurface, cut surface, and threshold operators, and have achieved good parallel performance on two different architectures (multi-core CPUs and NVIDIA GPUs) using the exact same operator code. We have applied these operators to several large, real scientific data sets, and have open-source released a beta version of our code base.},

note = {LA-UR-12-10227},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}