Cloud computing, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. To study the effects of moving parallel scientific applications onto the cloud, we deployed several benchmark applications like matrix–vector operations and NAS parallel benchmarks, and DOUG (Domain decomposition On Unstructured Grids) on the cloud. DOUG is an open source software package for parallel iterative solution of very large sparse systems of linear equations. The detailed analysis of DOUG on the cloud showed that parallel applications benefit a lot and scale reasonable on the cloud. We could also observe the limitations of the cloud and its comparison with cluster in terms of performance. However, for efficiently running the scientific applications on the cloud infrastructure, the applications must be reduced to frameworks that can successfully exploit the cloud resources, like the MapReduce framework. Several iterative and embarrassingly parallel algorithms are reduced to the MapReduce model and their performance is measured and analyzed. The analysis showed that Hadoop MapReduce has significant problems with iterative methods, while it suits well for embarrassingly parallel algorithms. Scientific computing often uses iterative methods to solve large problems. Thus, for scientific computing on the cloud, this paper raises the necessity for better frameworks or optimizations for MapReduce.

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云上并行科学应用程序的可扩展性

具有几乎无限资源前景的云计算似乎非常适合解决资源贪婪的科学计算问题。为了研究将并行科学应用程序移动到云中的影响，我们在云上部署了一些基准应用程序，例如矩阵向量运算和NAS并行基准，以及DOUG（非结构化网格上的域分解）。DOUG是一个开源软件包，用于对大型稀疏线性方程组进行并行迭代求解。在云上对DOUG的详细分析表明，并行应用程序在云上受益匪浅，而且规模合理。我们还可以观察到云的局限性以及在性能方面与集群的比较。但是，为了在云基础架构上高效运行科学应用程序，必须将应用程序简化为可以成功利用云资源的框架，例如MapReduce框架。几种迭代的和令人尴尬的并行算法被简化为MapReduce模型，并对其性能进行了测量和分析。分析表明，Hadoop MapReduce的迭代方法存在重大问题，而它非常适合令人尴尬的并行算法。科学计算通常使用迭代方法来解决大问题。因此，对于在云上进行科学计算，本文提出了为MapReduce建立更好的框架或进行优化的必要性。几种迭代的和令人尴尬的并行算法被简化为MapReduce模型，并对其性能进行了测量和分析。分析表明，Hadoop MapReduce的迭代方法存在重大问题，而它非常适合令人尴尬的并行算法。科学计算通常使用迭代方法来解决大问题。因此，对于在云上进行科学计算，本文提出了为MapReduce建立更好的框架或进行优化的必要性。几种迭代的和令人尴尬的并行算法被简化为MapReduce模型，并对其性能进行了测量和分析。分析表明，Hadoop MapReduce的迭代方法存在重大问题，而它非常适合令人尴尬的并行算法。科学计算通常使用迭代方法来解决大问题。因此，对于在云上进行科学计算，本文提出了为MapReduce建立更好的框架或进行优化的必要性。