scholarly journals A floating-point validation suite for high-performance shared and distributed memory computing systems

2002 ◽  
Author(s):  
S.K. Ghoshal
Keyword(s):  
High Performance ◽  
Distributed Memory ◽  
Floating Point ◽  
Computing Systems ◽  
Distributed Memory Computing

scholarly journals A Framework for HI Spectral Source Finding Using Distributed-Memory Supercomputing

2014 ◽  
Vol 31 ◽  
Author(s):  
Stefan Westerlund ◽  
Christopher Harris
Keyword(s):  
High Performance ◽  
Distributed Memory ◽  
Computing Systems ◽  
Sky Surveys ◽  
Local Statistics ◽  
Wide Range ◽  
Gaussian Source ◽  
High Bandwidth ◽  
Traditional Approaches ◽  
Performance Computing

AbstractThe latest generation of radio astronomy interferometers will conduct all sky surveys with data products consisting of petabytes of spectral line data. Traditional approaches to identifying and parameterising the astrophysical sources within this data will not scale to datasets of this magnitude, since the performance of workstations will not keep up with the real-time generation of data. For this reason, it is necessary to employ high performance computing systems consisting of a large number of processors connected by a high-bandwidth network. In order to make use of such supercomputers substantial modifications must be made to serial source finding code. To ease the transition, this work presents the Scalable Source Finder Framework, a framework providing storage access, networking communication and data composition functionality, which can support a wide range of source finding algorithms provided they can be applied to subsets of the entire image. Additionally, the Parallel Gaussian Source Finder was implemented using SSoFF, utilising Gaussian filters, thresholding, and local statistics. PGSF was able to search on a 256GB simulated dataset in under 24 minutes, significantly less than the 8 to 12 hour observation that would generate such a dataset.

scholarly journals Use cases of lossy compression for floating-point data in scientific data sets

2019 ◽  
Vol 33 (6) ◽  
pp. 1201-1220 ◽  
Author(s):  
Franck Cappello ◽  
Sheng Di ◽  
Sihuan Li ◽  
Xin Liang ◽  
Ali Murat Gok ◽  
...  
Keyword(s):  
High Performance ◽  
Scientific Computing ◽  
Lossy Compression ◽  
Scientific Data ◽  
Use Cases ◽  
Floating Point ◽  
Data Sets ◽  
Use Case ◽  
Computing Systems ◽  
Point Data

Architectural and technological trends of systems used for scientific computing call for a significant reduction of scientific data sets that are composed mainly of floating-point data. This article surveys and presents experimental results of currently identified use cases of generic lossy compression to address the different limitations of scientific computing systems. The article shows from a collection of experiments run on parallel systems of a leadership facility that lossy data compression not only can reduce the footprint of scientific data sets on storage but also can reduce I/O and checkpoint/restart times, accelerate computation, and even allow significantly larger problems to be run than without lossy compression. These results suggest that lossy compression will become an important technology in many aspects of high performance scientific computing. Because the constraints for each use case are different and often conflicting, this collection of results also indicates the need for more specialization of the compression pipelines.

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’.

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