A debugging scheme for fine-grain threads on massively parallel processors with a small amount of log information —Replay and race detection—

1996 ◽  
pp. 108-127 ◽  
Author(s):  
Tomio Kamada ◽  
Akinori Yonezawa
Keyword(s):  
Parallel Processors ◽  
Massively Parallel ◽  
Race Detection ◽  
Fine Grain

Back to Thin-Core Massively Parallel Processors

Computer ◽  
2011 ◽  
Vol 44 (12) ◽  
pp. 49-54 ◽  
Author(s):  
Ami Marowka
Keyword(s):  
Parallel Processors ◽  
Massively Parallel

scholarly journals The Fortran-P Translator: Towards Automatic Translation of Fortran 77 Programs for Massively Parallel Processors

1995 ◽  
Vol 4 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Matthew O'keefe ◽  
Terence Parr ◽  
B. Kevin Edgar ◽  
Steve Anderson ◽  
Paul Woodward ◽  
...  
Keyword(s):  
High Performance ◽  
Parallel Machines ◽  
Parallel Processors ◽  
Massively Parallel ◽  
Automatic Translation ◽  
Efficient Code ◽  
Self Similar ◽  
User Friendly ◽  
Application Codes ◽  
Fortran 77

Massively parallel processors (MPPs) hold the promise of extremely high performance that, if realized, could be used to study problems of unprecedented size and complexity. One of the primary stumbling blocks to this promise has been the lack of tools to translate application codes to MPP form. In this article we show how applications codes written in a subset of Fortran 77, called Fortran-P, can be translated to achieve good performance on several massively parallel machines. This subset can express codes that are self-similar, where the algorithm applied to the global data domain is also applied to each subdomain. We have found many codes that match the Fortran-P programming style and have converted them using our tools. We believe a self-similar coding style will accomplish what a vectorizable style has accomplished for vector machines by allowing the construction of robust, user-friendly, automatic translation systems that increase programmer productivity and generate fast, efficient code for MPPs.

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