Numerical algorithms for high-performance computational science

Jack Dongarra, Laura Grigori, Nicholas J. Higham
2020 Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences  
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
more » ... g 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'.
doi:10.1098/rsta.2019.0066 pmid:31955676 fatcat:2l4iy3yxwvc3njht5smpsqloma