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Numerical algorithms for high-performance computational science [PDF]
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2020 A number of features of today’s high-performance computers make it challenging to exploit these machines fully for computational science. These include increasing core counts but stagnant clock frequencies; the high cost of data movement; use of accelerators (GPUs, FPGAs, coprocessors), making architectures increasingly heterogeneous; and multi- ple ...
Dongarra, Jack +2 more
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High performance computing tools in science and engineering [PDF]
The Journal of Supercomputing, 2010 This special issue collects research papers selected among those presented at the second minisymposium “HPC applied to Computational Problems in Science and Engineering” which was held in June 2010, in Almeria, Spain. This workshop was a special event organized within the framework of the “10th International Conference on Computational and Mathematical
Enrique S. Quintana-Ortí +2 more
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High-performance special-purpose computers in science [PDF]
Computing in Science & Engineering, 1999 5 pages, to apper in IEEE Computing in Science and ...
Toshiyuki Fukushige +2 more
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LAPACK95 ‐ high performance linear algebra package
Mathematical Modelling and Analysis, 2000 LAPACK95 is a set of FORTRAN95 subroutines which interfaces FORTRAN95 with LAPACK. All LAPACK driver subroutines (including expert drivers) and some LAPACK computationals have both generic LAPACK95 interfaces and generic LAPACK77 interfaces.
J. Dongarra, J. Waśniewski
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Female Performance and Participation in Computer Science [PDF]
ACM Transactions on Computing Education, 2019 The change in the English computing curriculum and the shift towards computer science (CS) has been closely observed by other countries. Female participation remains a concern in most jurisdictions, but female attainment in CS is relatively unstudied. Using the English national pupil database, we analyzed all exam results (n = 5,370,064) for students ...
Peter E. J. Kemp +2 more
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Editorial: High-performance tensor computations in scientific computing and data science
Frontiers in Applied Mathematics and Statistics, 2022 Introduction In the last two decade, tensor computations developed from a small and little known subject to a vast and heterogeneous field with many diverse topics ranging from high-order decomposition and low-rank approximation to optimization and multi-linear contractions.
Di Napoli, Edoardo +3 more
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High performance computing tools in science and engineering [PDF]
The Journal of Supercomputing, 2013 The combination of multicore architectures with the potential acceleration of Graphics Processing Units allows to design, low cost, desktop machines with hundreds of processing cores and at the same time to build large supercomputers with hundreds of thousands of computational units.
Francisco Almeida, Jesús Vigo-Aguiar
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Interval Arithmetic and Computational Science: Performance Considerations [PDF]
, 2006 Interval analysis is an alternative to conventional floating-point computations that offers guaranteed error bounds. Despite this advantage, interval methods have not gained widespread use in large scale computational science applications. This paper addresses this issue from a performance perspective, comparing the performance of floating point and ...
Alistair P. Rendell +2 more
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Multiscale computing for science and engineering in the era of exascale performance [PDF]
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2019 In this position paper, we discuss two relevant topics: (i) generic multiscale computing on emerging exascale high-performing computing environments, and (ii) the scaling of such applications towards the exascale. We will introduce the different phases when developing a multiscale model and simulating it on available computing ...
Alfons G. Hoekstra +4 more
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IMPLEMENTATION OF THE PARALLEL ARNOLDI METHOD IN THE IBM SP2 DISTRIBUTED MEMORY SYSTEM
TASK Quarterly, 1997 The following article discusses the Implicitly Restarted Arnoldi Method used for solving large sparse eigenvalue problems. It presents the parallel implementation of this algorithm for a distributed memory architecture developed by Maschhoff and ...
MICHAŁ REWIEŃSKI
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