Modernizing the C++ Interface to MPI

Kambadur, Prabhanjan; Gregor, Douglas; Lumsdaine, Andrew; Dharurkar, Amey

doi:10.1007/11846802_39

Cited by 14 publications

(7 citation statements)

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“…It has successfully applied generic programming techniques through abstract data structures in order to deploy parallel algorithms 171, 172. Subsequently, template extensions to the MPI 173 and OpenMP 174 standards have been proposed in order to use built‐in lexical abstractions for generic parallel programming, and diverse template libraries now support multicore environments 175 and self‐scheduling task parallelism 176.…”

Section: Related Approachesmentioning

confidence: 99%

A survey of algorithmic skeleton frameworks: high‐level structured parallel programming enablers

2010

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Structured parallel programs ought to be conceived as two separate and complementary entities: computation, which expresses the calculations in a procedural manner, and coordination, which abstracts the interaction and communication. By abstracting commonly used patterns of parallel computation, communication, and interaction, algorithmic skeletons enable programmers to code algorithms without specifying platform‐dependent primitives. This article presents a literature review on algorithmic skeleton frameworks (ASKF), parallel software development environments furnishing a collection of parameterizable algorithmic skeletons, where the control flow, nesting, resource monitoring, and portability of the resulting parallel program is dictated by the ASKF as opposed to the programmer. Consequently, the ASKF can be positioned as high‐level structured parallel programming enablers, as their systematic utilization permits the abstract description of programs and fosters portability by focusing on the description of the algorithmic structure rather than on its detailed implementation. Copyright © 2010 John Wiley & Sons, Ltd.

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Section: Related Approachesmentioning

confidence: 99%

A survey of algorithmic skeleton frameworks: high‐level structured parallel programming enablers

2010

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“…Boost.MPI supports automatic serialization of objects, and has served as the model for our work on MPI.NET. In particular, the high-level interfaces in MPI.NET and their resulting use of the low-level MPI library were ported directly from Boost.MPI [7] and its predecessor [10]. However, the generic programming techniques used in Boost.MPI could not be duplicated within C#, forcing MPI.NET to use completely different implementation techniques to achieve suitable performance.…”

Section: Related Workmentioning

confidence: 99%

“…More recently, Boost.MPI [7,10] has been developed, providing direct support for the generic programming paradigm. Boost.MPI supports automatic serialization of objects, and has served as the model for our work on MPI.NET.…”

Section: Related Workmentioning

confidence: 99%

Design and implementation of a high-performance MPI for C# and the common language infrastructure

Gregor

Lumsdaine

2008

Proceedings of the 13th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

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As high-performance computing enters the mainstream, parallel programming mechanisms (including the Message Passing Interface, or MPI) must be supported in new environments such as C# and the Common Language Infrastructure (CLI). Making effective use of MPI with the CLI requires an interface that reflects the highlevel object-oriented nature of C# and that also supports its programming idioms. However, for performance reasons, this highlevel functionality must ultimately be mapped to low-level native MPI libraries. In addition to abstraction penalty concerns, avoiding unwanted overhead in this mapping process is significantly complicated by the safety and portability features of the CLI virtual machine, such as garbage collection and just-in-time compilation. In this paper, we describe our approach to using features of C# and the CLI-such as reflection, unsafe code regions, and run-time code generation-to realize an elegant, yet highly efficient, C# interface to MPI. Experimental results demonstrate that there is no appreciable overhead introduced by our approach when compared to the native MS-MPI library.

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“…The automatic tools that we consider closely related work include AutoMap/AutoLink [11], C++2MPI [14], and MPI Pre-Processor [28]. In addition, Boost.MPI and Boost.Serialization [17] provide library support for seamless marshaling and unmarshaling. Next we describe and compare the features of each of these approaches to motivate our approach and demonstrate how it improves on existing state-of-the-art.…”

Section: Directly Related Workmentioning

confidence: 99%

“…An MPI Datatype is a list of memory offsets describing the data to be marshaled given the base offset of a structure. However, the approach makes an implicit assumption that all memory in a structure has been allocated [17] aim at modernizing the C++ interface to MPI by utilizing advanced generic programming techniques [8]. These libraries provide support for automatic marshaling of primitive data types, user-defined classes, and STL container classes.…”

Section: Directly Related Workmentioning

confidence: 99%