Running COMSOL Multiphysics® with Cloud Computing

Pär Persson Mattsson February 20, 2015

We have previously written about HPC with the COMSOL Multiphysics® software, clusters, and hybrid computing. But not all of us have a cluster available in the office (or the hardware to build a Beowulf cluster). So what possibilities do we have if we really need that extra compute power that a cluster can give us? One solution is to utilize cloud computing, a service that provides compute power on a temporary basis, to give our computations and productivity a boost.

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Pär Persson Mattsson November 13, 2014

The first computer I used was a real performance beast. Equipped with Intel’s 486 clocking in at 66 MHz, this machine was ready to take on whatever challenges the future would bring us. Or so I thought. The CPU clock speeds increased and soon passed 500 MHz, 1 GHz, and continued upwards. Around 2005, the top speed of the high-end processors settled around 4 GHz and hasn’t increased much since then. Why is that? I’ll explain.

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Pär Persson Mattsson April 11, 2014

Many of us need up-to-date software and hardware in order to work efficiently. Therefore, we need to follow the pace of technological development. But, what should we do with the outdated hardware? It feels wasteful to send the old hardware to its grave or to just put it in a corner. Another, more productive, solution is to use the old hardware to build a Beowulf cluster and use it to speed up computations.

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Pär Persson Mattsson March 20, 2014

One thing we haven’t talked much about so far in the Hybrid Modeling blog series is what speedup we can expect when adding more resources to our computations. Today, we consider some theoretical investigations that explain the limitations in parallel computing. We will also show you how to use the COMSOL software’s Batch Sweeps option, which is a built-in, embarrassingly parallel functionality for improving performance when you reach these limits.

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Pär Persson Mattsson February 20, 2014

In the latest post in this Hybrid Modeling blog series, we discussed the basic principles behind shared memory computing — what it is, why we use it, and how the COMSOL software uses it in its computations. Today, we are going to discuss the other building block of hybrid parallel computing: distributed memory computing.

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Pär Persson Mattsson February 6, 2014

A couple of weeks ago, we published the first blog post in a Hybrid Modeling series, about hybrid parallel computing and how it helps COMSOL Multiphysics model faster. Today, we are going to briefly discuss one of the building blocks that make up the hybrid version, namely shared memory computing. Before that, we need to consider what it means that an “application is running in parallel”. You will also learn when and how to use shared memory with COMSOL.

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Pär Persson Mattsson January 23, 2014

Twenty years ago, the TOP500 list was dominated by vector processing supercomputers equipped with up to a thousand processing units. Later on, these machines were replaced by clusters for massively parallel computing, which soon dominated the list, and gave rise to distributed computing. The first clusters used dedicated single-core processors per compute node, but soon, additional processors were placed on the node requiring the sharing of memory. The capabilities of these shared-memory parallel machines heralded a sea change towards multicore […]

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