Transparent Offloading of Computationally Demanding Operations in Microsoft .NET

Morten Larsen, Brian Vinter

Abstract


For many years, the group of preferred programming languages for writing algorithms meant for large clusters contains among others C/C++ and FORTRAN. However, normally one does not consider the Microsoft .NET programming languages as a part of this group. The reason for this is that only few tools exist that can help programmers simplify the process of writing parallel .NET code besides the official tools from Microsoft i.e. Task Parallel Library (TPL) (Microsoft, n.d.) and HPC Pack. (Microsoft, n.d.) Furthermore, most of the official tools only supports a Microsoft Windows or Microsoft Azure platform and not a mixture of non-virtualized platforms like a Linux machine with Mono (Mono, n.d.) or the decommissioned DotGNU (GNU, n.d.). In addition, some of the most useful tools for writing parallel .NET code does not support multiple machines and as a result, programmers seldom choose .NET as the framework for writing parallel programs. Therefore, this paper presents a .NET tool, which will use well-known parallel tools as inspiration and allow programmers to call a number of methods that can send a job consisting of a user-defined method (code) along with sets of parameters and shared data to a central machine. The central machine will then modify the code and afterwards distributes the work to the connected machines each running one or more workers. By implementing three simple benchmarks, initial tests shows that the benchmarks can achieve linear scaling on a small cluster consisting of Windows machines, and by presenting future design ideas, it is believed that it will be possible to extent the linear scaling to a larger mix-platform cluster consisting of both internal resources (workstations/servers) and external cloud resources.


Full Text: PDF DOI: 10.5539/nct.v2n1p52

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This work is licensed under a Creative Commons Attribution 3.0 License.

Network and Communication Technologies   ISSN 1927-064X(Print)   ISSN 1927-0658 (Online)
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