scholarly journals Adaptive parallelism in the Bulk-synchronous Parallel model

1996 ◽  
pp. 311-318 ◽  
Author(s):  
Mohan V. Nibhanupudi ◽  
Boleslaw K. Szymanski
Keyword(s):  
Parallel Model ◽  
Bulk Synchronous Parallel ◽  
Adaptive Parallelism

scholarly journals Elastic Bulk Synchronous Parallel Model for Distributed Deep Learning

2019 ◽  
Author(s):  
Xing Zhao ◽  
Manos Papagelis ◽  
Aijun An ◽  
Bao Xin Chen ◽  
Junfeng Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Parallel Model ◽  
Bulk Synchronous Parallel

ZipLine: An Optimized Algorithm for the Elastic Bulk Synchronous Parallel Model

2021 ◽  
Author(s):  
Xing Zhao ◽  
Manos Papagelis ◽  
Aijun An ◽  
Bao Xin Chen ◽  
Junfeng Liu ◽  
...  
Keyword(s):  
Parallel Model ◽  
Bulk Synchronous Parallel

BENCHMARKING CLUSTERS OF WORKSTATIONS THROUGH PARALLEL SORTING AND BSP LIBRARIES

2001 ◽  
Vol 11 (01) ◽  
pp. 25-40
Author(s):  
CHRISTOPHE CÉRIN ◽  
JEAN-LUC GAUDIOT
Keyword(s):  
Parallel Model ◽  
Parallel Sorting ◽  
Bulk Synchronous Parallel ◽  
Oxford University ◽  
General Terms ◽  
The University

We present our experiences in developping and tuning the performance at the user level, of (in core) parallel sorting on homogeneous and non homogeneous clusters with the use of the two available BSP (Bulk Synchronous Parallel model) libraries: BSPLib from Oxford university (UK) and PUB7 from the university of Paderborn (Germany). The paper is mainly about the communication performances of these two libraries and, in more general terms, it compares and summarizes the programming facilities and differences between them.

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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