Harmonizing high performance computing (HPC) with large-scale complex systems in computational science and engineering

2009 ◽  
pp. 1-11 ◽  
Author(s):  
Clyde Chittister ◽  
Yacov Y. Haimes
Keyword(s):  
Complex Systems ◽  
High Performance Computing ◽  
High Performance ◽  
Large Scale ◽  
Computational Science ◽  
Science And Engineering ◽  
Computational Science And Engineering ◽  
Performance Computing

High-Performance Customizable Computing

2012 ◽  
pp. 48-77
Author(s):  
Domingo Benitez
Keyword(s):  
High Performance ◽  
General Purpose ◽  
Performance Comparison ◽  
Computational Science ◽  
Science And Engineering ◽  
Current Trends ◽  
Computational Science And Engineering ◽  
Top View ◽  
Performance Computing ◽  
Hpcc Systems

Many accelerator-based computers have demonstrated that they can be faster and more energy-efficient than traditional high-performance multi-core computers. Two types of programmable accelerators are available in high-performance computing: general-purpose accelerators such as GPUs, and customizable accelerators such as FPGAs, although general-purpose accelerators have received more attention. This chapter reviews the state-of-the-art and current trends of high-performance customizable computers (HPCC) and their use in Computational Science and Engineering (CSE). A top-down approach is used to be more accessible to the non-specialists. The “top view” is provided by a taxonomy of customizable computers. This abstract view is accompanied with a performance comparison of common CSE applications on HPCC systems and high-performance microprocessor-based computers. The “down view” examines software development, describing how CSE applications are programmed on HPCC computers. Additionally, a cost analysis and an example illustrate the origin of the benefits. Finally, the future of the high-performance customizable computing is analyzed.

REDUCING THE BULK IN THE BULK SYNCHRONOUS PARALLEL MODEL

2013 ◽  
Vol 23 (04) ◽  
pp. 1340010 ◽  
Author(s):  
R. F. BARRETT ◽  
C. T. VAUGHAN ◽  
S. D. HAMMOND ◽  
D. ROWETH
Keyword(s):  
Parallel Programming ◽  
High Performance ◽  
Large Scale ◽  
Computational Science ◽  
Parallel Model ◽  
Science And Engineering ◽  
Bulk Synchronous Parallel ◽  
Model Code ◽  
Computational Science And Engineering ◽  
Processing Architectures

For over two decades the dominant means for enabling portable performance of computational science and engineering applications on parallel processing architectures has been the bulk-synchronous parallel programming (BSP) model. Code developers, motivated by performance considerations to minimize the number of messages transmitted, have typically pursued a strategy of aggregating message data into fewer, larger messages. Emerging and future high-performance architectures, especially those seen as targeting Exascale capabilities, provide motivation and capabilities for revisiting this approach. In this paper we explore alternative configurations within the context of a large-scale complex multi-physics application and a proxy that represents its behavior, presenting results that demonstrate some important advantages as the number of processors increases in scale.

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