A data parallel programming model based on distributed objects

2003 ◽  
Author(s):  
R. Diaconescu ◽  
R. Conradi
Keyword(s):  
Parallel Programming ◽  
Programming Model ◽  
Distributed Objects ◽  
Data Parallel ◽  
Model Based ◽  
Parallel Programming Model ◽  
Data Parallel Programming

scholarly journals PDDP, A Data Parallel Programming Model

1996 ◽  
Vol 5 (4) ◽  
pp. 319-327
Author(s):  
Karen H. Warren
Keyword(s):  
Parallel Programming ◽  
High Performance ◽  
Parallel Machines ◽  
Programming Model ◽  
Data Distribution ◽  
Interprocessor Communication ◽  
Data Parallel ◽  
Parallel Programming Model ◽  
Data Objects ◽  
Data Parallel Programming

PDDP, the parallel data distribution preprocessor, is a data parallel programming model for distributed memory parallel computers. PDDP implements high-performance Fortran-compatible data distribution directives and parallelism expressed by the use of Fortran 90 array syntax, the FORALL statement, and the WHERE construct. Distributed data objects belong to a global name space; other data objects are treated as local and replicated on each processor. PDDP allows the user to program in a shared memory style and generates codes that are portable to a variety of parallel machines. For interprocessor communication, PDDP uses the fastest communication primitives on each platform.

scholarly journals Data-Parallel Programming in a Multithreaded Environment

1997 ◽  
Vol 6 (2) ◽  
pp. 187-200
Author(s):  
Matthew Haines ◽  
Piyush Mehrotra ◽  
David Cronk
Keyword(s):  
Parallel Programming ◽  
Distributed Memory ◽  
Programming Model ◽  
Time System ◽  
Data Parallel ◽  
Parallel Programming Model ◽  
Collective Communications ◽  
Multiple Threads ◽  
Run Time ◽  
Data Parallel Programming

Research on programming distributed memory multiprocessors has resulted in a well-understood programming model, namely data-parallel programming. However, data-parallel programming in a multithreaded environment is far less understood. For example, if multiple threads within the same process belong to different data-parallel computations, then the architecture, compiler, or run-time system must ensure that relative indexing and collective operations are handled properly and efficiently. We introduce a run-time-based solution for data-parallel programming in a distributed memory environment that handles the problems of relative indexing and collective communications among thread groups. As a result, the data-parallel programming model can now be executed in a multithreaded environment, such as a system using threads to support both task and data parallelism.

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