scholarly journals A Software Implementation of the IEEE 754R Decimal Floating-Point Arithmetic Using the Binary Encoding Format

2009 ◽  
Vol 58 (2) ◽  
pp. 148-162 ◽  
Author(s):  
Marius Cornea ◽  
John Harrison ◽  
Cristina Anderson ◽  
Ping Tak Peter Tang ◽  
Eric Schneider ◽  
...  
Keyword(s):  
Software Implementation ◽  
Floating Point ◽  
Floating Point Arithmetic ◽  
Binary Encoding ◽  
Point Arithmetic

Software Implementation of Floating-Point Arithmetic

2009 ◽  
pp. 321-372
Author(s):  
Jean-Michel Muller ◽  
Nicolas Brisebarre ◽  
Florent de Dinechin ◽  
Claude-Pierre Jeannerod ◽  
Vincent Lefèvre ◽  
...  
Keyword(s):  
Software Implementation ◽  
Floating Point ◽  
Floating Point Arithmetic ◽  
Point Arithmetic

Software Implementation of Floating-Point Arithmetic

2018 ◽  
pp. 321-374 ◽  
Author(s):  
Jean-Michel Muller ◽  
Nicolas Brunie ◽  
Florent de Dinechin ◽  
Claude-Pierre Jeannerod ◽  
Mioara Joldes ◽  
...  
Keyword(s):  
Software Implementation ◽  
Floating Point ◽  
Floating Point Arithmetic ◽  
Point Arithmetic

scholarly journals Numerical algorithms for high-performance computational science

2020 ◽  
Vol 378 (2166) ◽  
pp. 20190066 ◽  
Author(s):  
Jack Dongarra ◽  
Laura Grigori ◽  
Nicholas J. Higham
Keyword(s):  
High Performance ◽  
Numerical Algorithms ◽  
Computational Science ◽  
Floating Point ◽  
Important Criterion ◽  
Data Movement ◽  
Floating Point Arithmetic ◽  
High Performance Computers ◽  
Point Arithmetic ◽  
Speed And Accuracy

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 precisions of floating-point arithmetic, including half-precision. Moreover, as well as maximizing speed and accuracy, minimizing energy consumption is an important criterion. New generations of algorithms are needed to tackle these challenges. We discuss some approaches that we can take to develop numerical algorithms for high-performance computational science, with a view to exploiting the next generation of supercomputers. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.

Fast and robust mesh arrangements using floating-point arithmetic

2020 ◽  
Vol 39 (6) ◽  
pp. 1-16
Author(s):  
Gianmarco Cherchi ◽  
Marco Livesu ◽  
Riccardo Scateni ◽  
Marco Attene
Keyword(s):  
Floating Point ◽  
Floating Point Arithmetic ◽  
Point Arithmetic
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