A single-program-multiple-data computational model for EPEX/FORTRAN

Darema, Frederica; George, David; Norton, Vic; Pfister, Gregory F.

doi:10.1016/0167-8191(88)90094-4

Cited by 144 publications

(54 citation statements)

References 2 publications

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“…By default, data is shared as per the OpenMP standard. The target execution model for our translation scheme is SPMD [7] with the following characteristics:…”

Section: Baseline Openmp To Mpi Translation Schemementioning

confidence: 99%

Programming Distributed Memory Sytems Using OpenMP

Basumallik

Min

Eigenmann

2007

2007 IEEE International Parallel and Distributed Processing Symposium

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“…By default, data is shared as per the OpenMP standard. The target execution model for our translation scheme is SPMD [7] with the following characteristics:…”

Section: Baseline Openmp To Mpi Translation Schemementioning

confidence: 99%

Programming Distributed Memory Sytems Using OpenMP

Basumallik

Min

Eigenmann

2007

2007 IEEE International Parallel and Distributed Processing Symposium

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“…Consequently, performance programming is an extensive research area that ranges from compiling techniques for exploiting SIMD architectures to programming models for MIMD architectures (e.g., shared memory and message passing models) to programming paradigms and languages. According to [123], Flynn's taxonomy can be extended from the perspective of software architecture. Performance programming can then be classified into three main categories:…”

Section: Performance Programmingmentioning

confidence: 99%

High‐performance computing: to boldly go where no human has gone before

Limet

Smari

Spalazzi

2015

Concurrency and Computation

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International audienceComputing and computational science are well on their way to enter into the exascale era while High Performance Computing (HPC) is now present in many spheres and domains of modern society. New technologies such as cloud computing, big data, and connected objects open many perspectives and possibilities that were unattainable until now. These are high times for computing and for HPC in particular. The High Performance Computing \& Simulation conference aims to bring together researchers working on various aspects of HPC, their design and use, and their applications. As has been the conference tradition, selected extended papers of HPCS 2012 are presented in this special issue. These should be interesting contributions that supplement the current state-of-the-art research in HPC. To relate these works and highlight their value, in this article, we briefly trace back the history of HPC, sketch the state of the present research and development in the field, and project some of the challenges and future trends anticipated

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“…We apply a coarse-grained parallelization strategy, following the Single Program Multiple Data (SPMD) model [18]. Parallel triangulation of planar segments is easily accomplished by dividing the set of segments into subsets that are distributed across a pool of threads.…”

Section: Computationally Efficient Architecturementioning

confidence: 99%

Incremental and batch planar simplification of dense point cloud maps

Whelan

Bondarev

et al. 2015

Robotics and Autonomous Systems

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Dense RGB-D SLAM techniques and high-fidelity LIDAR scanners are examples from an abundant set of systems capable of providing multi-million point datasets. These datasets quickly become difficult to process due to the sheer volume of data, typically containing significant redundant information, such as the representation of planar surfaces with millions of points. In order to exploit the richness of information provided by dense methods in real-time robotics, techniques are required to reduce the inherent redundancy of the data. In this paper we present a method for incremental planar segmentation of a gradually expanding point cloud map and a method for efficient triangulation and texturing of planar surface segments. Experimental results show that our incremental segmentation method is capable of running in real-time while producing a segmentation faithful to what would be achieved using a batch segmentation method. Our results also show that the proposed planar simplification and triangulation algorithm removes more than 90% of the input planar points, leading to a triangulation with only 10% of the original quantity of triangles per planar segment. Additionally, our texture generation algorithm preserves all colour information contained within planar segments, resulting in a visually appealing and geometrically accurate simplified representation.

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A single-program-multiple-data computational model for EPEX/FORTRAN

Cited by 144 publications

References 2 publications

Programming Distributed Memory Sytems Using OpenMP

Programming Distributed Memory Sytems Using OpenMP

High‐performance computing: to boldly go where no human has gone before

Incremental and batch planar simplification of dense point cloud maps

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