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Cloud computing has become a viable option for high-performance computing. In this article, we discuss the use case for cloud-based HPC, introduce the StarCluster toolkit, and show how to build a custom machine image for compute nodes.
The Matlab numerical computing environment is a good candidate for HPC systems applications, but a number of free and open source Matlab-like tools are available as well. These tools have a large number of built-in computational routines and are easily programmed.
Computing hardware is constantly changing, with new CPUs and accelerators, and the integration of both. How do you know which processors are right for your code?
As core counts increase in both CPUs and GPUs, the HPC lifestyle could become a bit more difficult.
Parallel programming is not easy, but one tool you can use to help parallelize your application is OpenMP. Most compilers are compatible with OpenMP and allow you to parallelize your code on a single node.
The Modules package makes life easier in the HPC world (and beyond).
In this second article on scalable storage in cloud environments, we cover the inner workings of RADOS and how to avoid pitfalls.
HPC has a unique set of requirements that might not fit into standard clouds. However, plenty of commercial options, including cloud-like services, provide the advantages of real HPC without the capital expense of buying hardware.
HPC systems are really designed to be shared by several users. One way to share them is through a software tool called a resource manager. Openlava is an open source version of the commercial scheduler LSF. It shares the robustness of LSF while being freely available, very scalable, and easy to install and customize.
Two years after the Oracle acquisition of Sun, Grid Engine is still alive and scheduling jobs.
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