Benefits of using cluster computing
Learn about the benefits of cluster computing in high-performance computing systems. Understand how parallelism enables concurrent processing across many tasks, the types of problems suited for HPC, and the limitations to consider when deciding to use cluster computing.
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HPC systems derive their computational power by exploiting parallelism. Programs for HPC systems must be splitted up into many smaller sub-programs which can be executed in parallel on different processors. HPC systems can offer parallelism at a much larger scale, with 100’s or 1000’s, or even millions of tasks running concurrently.However, writing parallel software can be challenging, and many existing software packages do not already support parallelism & may require development.
Therefore, HPCs are userful when you have:
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A program that can be recompiled or reconfigured to use optimized numerical libraries that are available on HPC systems but not on your own system;
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You have a parallel problem, e.g. you have a single application that needs to be rerun many times with different parameters;
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You have an application that has already been designed with parallelism;
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To make use of the large memory available;
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problem solutions require backups for future use. HPC facilities are reliable and regularly backed up.
When not to use HPC systems?
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You have a single threaded job which will only run one job at a time (typical of MatLab users);
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You rely on DBMS/ databases;
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You have a lot of data to transfer between your local machine and the HPC on a continuous basis (e.g. per job);
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You need to have a GUI to interact with your program.