Nonblocking Data Structures for Distributed-Memory Machines: Stacks as an Example

Multiprocessor task (M-task) programming is a suitable parallel programming model for coding application problems with an inherent modular structure. An M-task can be executed on a group of processors of arbitrary size, concurrently to other M-tasks of the same application program. The data of a multiprocessor task program usually include composed data structures, like vectors or arrays. For distributed memory machines or cluster platforms, those composed data structures are distributed within one or more processor groups. Thus, a concise parallel programming model for M-tasks requires a standardized distributed data format for composed data structures. Additionally, functions for data re-distribution with respect to different data distributions and different processor group layouts are needed to glue program parts together. In this paper, we present a data re-distribution library which extends the M-task programming with Tlib, a library providing operations to split processor groups and to map M-tasks to processor groups.

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SPMD execution of programs with dynamic data structures on distributed memory machines

Proceedings of the 1992 International Conference on Computer Languages ◽

10.1109/iccl.1992.185487 ◽

2003 ◽

Cited By ~ 7

Author(s):

R. Gupta

Keyword(s):

Data Structures ◽

Distributed Memory ◽

Dynamic Data Structures ◽

Dynamic Data ◽

Distributed Memory Machines

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Object-Oriented Support for Adaptive Methods on Paranel Machines

Scientific Programming ◽

10.1155/1993/474972 ◽

1993 ◽

Vol 2 (4) ◽

pp. 179-192

Author(s):

Sandeep Bhatt ◽

Marina Chen ◽

James Cowie ◽

Cheng-Yee Lin ◽

Pangfeng Liu

Keyword(s):

Load Balancing ◽

Data Structures ◽

Large Scale ◽

Distributed Memory ◽

Object Oriented ◽

Adaptive Methods ◽

Programming Methodology ◽

Distributed Memory Machines ◽

Irregular Data ◽

Tree Codes

This article reports on experiments from our ongoing project whose goal is to develop a C++ library which supports adaptive and irregular data structures on distributed memory supercomputers. We demonstrate the use of our abstractions in implementing "tree codes" for large-scale N-body simulations. These algorithms require dynamically evolving treelike data structures, as well as load-balancing, both of which are widely believed to make the application difficult and cumbersome to program for distributed-memory machines. The ease of writing the application code on top of our C++ library abstractions (which themselves are application independent), and the low overhead of the resulting C++ code (over hand-crafted C code) supports our belief that object-oriented approaches are eminently suited to programming distributed-memory machines in a manner that (to the applications programmer) is architecture-independent. Our contribution in parallel programming methodology is to identify and encapsulate general classes of communication and load-balancing strategies useful across applications and MIMD architectures. This article reports experimental results from simulations of half a million particles using multiple methods.

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Supporting dynamic data structures on distributed-memory machines

ACM Transactions on Programming Languages and Systems ◽

10.1145/201059.201065 ◽

1995 ◽

Vol 17 (2) ◽

pp. 233-263 ◽

Cited By ~ 151

Author(s):

Anne Rogers ◽

Martin C. Carlisle ◽

John H. Reppy ◽

Laurie J. Hendren

Keyword(s):

Data Structures ◽

Distributed Memory ◽

Dynamic Data Structures ◽

Dynamic Data ◽

Distributed Memory Machines

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SPMD execution of programs with pointer-based data structures on distributed-memory machines

Journal of Parallel and Distributed Computing ◽

10.1016/0743-7315(92)90026-j ◽

1992 ◽

Vol 16 (2) ◽

pp. 92-107 ◽

Cited By ~ 3

Author(s):

Rajiv Gupta

Keyword(s):

Data Structures ◽

Distributed Memory ◽

Distributed Memory Machines

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Programming in Vienna Fortran

Scientific Programming ◽

10.1155/1992/258136 ◽

1992 ◽

Vol 1 (1) ◽

pp. 31-50 ◽

Cited By ~ 140

Author(s):

Barbara Chapman ◽

Piyush Mehrotra ◽

Hans Zima

Keyword(s):

Shared Memory ◽

Data Structures ◽

Distributed Memory ◽

Data Distribution ◽

Performance Potential ◽

Distributed Memory Machines ◽

Programming Paradigm ◽

Language Extension ◽

Wide Range ◽

Global Data

Exploiting the full performance potential of distributed memory machines requires a careful distribution of data across the processors. Vienna Fortran is a language extension of Fortran which provides the user with a wide range of facilities for such mapping of data structures. In contrast to current programming practice, programs in Vienna Fortran are written using global data references. Thus, the user has the advantages of a shared memory programming paradigm while explicitly controlling the data distribution. In this paper, we present the language features of Vienna Fortran for FORTRAN 77, together with examples illustrating the use of these features.

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