Data parallel computers and the FORALL statement

2002 ◽  
Author(s):  
E. Albert ◽  
J.D. Lukas ◽  
G.L. Steele
Keyword(s):  
Parallel Computers ◽  
Data Parallel

Data parallel computers and the Forall statement

1991 ◽  
Vol 13 (2) ◽  
pp. 185-192 ◽  
Author(s):  
Eugene Albert ◽  
Joan D. Lukas ◽  
Guy L. Steele
Keyword(s):  
Parallel Computers ◽  
Data Parallel

DATA PARALLEL COMPUTATION OF EUCLIDEAN DISTANCE TRANSFORMS

1992 ◽  
Vol 02 (04) ◽  
pp. 331-339 ◽  
Author(s):  
TERRY BOSSOMAIER ◽  
NATALINA ISIDORO ◽  
ADRIAN LOEFF
Keyword(s):  
High Speed ◽  
Euclidean Distance ◽  
Maximum Error ◽  
Parallel Computers ◽  
Data Sets ◽  
Data Parallel ◽  
Distance Transforms ◽  
Diverse Data ◽  
Mimd Architectures ◽  
New Strategies

The Euclidean Distance Transform is an important, but computationally expensive, technique of computational geometry, with applications in many areas including image processing, graphics and pattern recognition. Since the data sets used are typically large, one might hope that parallel computers would be suitable for its determination. We show that existing parallel algorithms perform poorly on certain data sets and introduce new strategies. These achieve high speed on diverse data sets, but fail occasionally in pathological cases. We determine the maximum error in such cases and demonstrate that it is satisfactorily low. Although adequate efficiency is achievable on SIMD machines, we demonstrate that problems of this kind are data parallel yet best suited to MIMD architectures.

MASSIVELY DATA-PARALLEL MOLECULAR DYNAMICS

1992 ◽  
Vol 03 (04) ◽  
pp. 709-731
Author(s):  
ERNESTO BONOMI ◽  
MARCO TOMASSINI
Keyword(s):  
Molecular Dynamics ◽  
Heat Bath ◽  
Hard Core ◽  
Parallel Computers ◽  
Particle Systems ◽  
Data Parallel ◽  
Connection Machine ◽  
Large N ◽  
Parallel Molecular Dynamics ◽  
Dynamics Simulations

In light of present day data-parallel computers, an appraisal of molecular dynamics simulations of large N-particle systems, isolated or in contact with a heat-bath, is given. Special attention is focused 011 the Connection Machine CM-2. Particularly the cases of long-range potentials and impulsive hard-core interactions are discussed in detail. Data-parallel strategies including data distribution, communications and computation are presented and compared with well-known sequential approaches. The conclusion offered is that the methods described here are easy to design and offer the possibility of reasonably fast implementations for the reliable simulation of macroscopic samples of matter.

POMPC: A C LANGUAGE FOR DATA PARALLELISM

1993 ◽  
Vol 04 (01) ◽  
pp. 85-96
Author(s):  
NICOLAS PARIS
Keyword(s):  
Parallel Computers ◽  
Massively Parallel ◽  
C Language ◽  
Parallel Language ◽  
Data Parallel ◽  
Massively Parallel Computers ◽  
Connection Machine ◽  
Compilation Process ◽  
Points Of View ◽  
Definition Of

POMPC is a parallel language dedicated to the programming of Massively Parallel Computers according to a synchronous Data Parallel model. It is an extension of the ANSI C language and follows its philosophy. Parallelism is explicitly handled by the definition of collections of parallel variables and the definition of communication primitives. A methodology is presented in order to easily port the language on different target architectures. Virtualization is introduced to handle simultaneously several collections of different sizes and shapes. Virtualization management is a key point of the compilation process. Programmer, architecture, compilation and system points of view lead to a special implementation of the virtualization mixing physical and virtual parallel objects. The implementation of the virtualization is adapted for the development of communication libraries and also adapted to enlarge the asynchronous sections of code for SPMD architecture. The portability of the POMPC language is validated by several implementations for mono/multi-process simulation on UNIX machines, for the Connection Machine CM-2, for the MasPar MP-1 and a compiler is in preparation for the iPSC-860.

Video Signal Processing and Coding on Data-Parallel Computers

1995 ◽  
Vol 5 (2) ◽  
pp. 118-129 ◽  
Author(s):  
P. Moulin ◽  
A.T. Ogielski ◽  
G. Lilienfeld ◽  
J.W. Woods
Keyword(s):  
Signal Processing ◽  
Parallel Computers ◽  
Video Signal ◽  
Video Signal Processing ◽  
Data Parallel

scholarly journals C++ and Massively Parallel Computers

1993 ◽  
Vol 2 (4) ◽  
pp. 193-202 ◽  
Author(s):  
Daniel J. Lickly ◽  
Philip J. Hatcher
Keyword(s):  
Object Oriented ◽  
Feasibility Studies ◽  
Parallel Computers ◽  
Parallel Architectures ◽  
Parallel Execution ◽  
Object Oriented Programming ◽  
Massively Parallel ◽  
Data Parallel ◽  
Massively Parallel Computers ◽  
Massively Parallel Architectures

Our goal is to apply the software engineering advantages of object-oriented programming to the raw power of massively parallel architectures. To do this we have constructed a hierarchy of C++ classes to support the data-parallel paradigm. Feasibility studies and initial coding can be supported by any serial machine that has a C++ compiler. Parallel execution requires an extended Cfront, which understands the data-parallel classes and generates C*code. (C*is a data-parallel superset of ANSI C developed by Thinking Machines Corporation). This approach provides potential portability across parallel architectures and leverages the existing compiler technology for translating data-parallel programs onto both SIMD and MIMD hardware.

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