Max Planck Institute of Biochemistry uses MathWorks parallel computing tools for cancer research
24 November 2008
The MathWorks has announced that the Max Planck Institute of Biochemistry is using its MATLAB, Parallel Computing Toolbox, MATLAB Distributed Computing Server, and other MathWorks tools to accelerate its workflow and reduce research and development time.
Researchers at the department of Molecular Structural Biology at the Max Planck Institute of Biochemistry, based in Martinsried, near Munich, Germany, study the relationship between the structure and activity of macromolecular protein complexes involved protein degradation in cells. This complex research requires high-throughput tools and procedures capable of efficiently processing vast amounts of data.
Max Planck Institute researchers turned to MathWorks tools to develop streamlined procedures for their data-intensive applications, including image acquisition, filtering, processing, and 3-D reconstruction. For techniques such as pattern matching and single-particle reconstruction that required computationally intensive algorithms, researchers used Parallel Computing Toolbox and MATLAB Distributed Computing Server to accelerate computation over a 64-node cluster.
“Reconstructing a 3-D volume typically takes days on a single CPU, but by using Parallel Computing Toolbox and MATLAB Distributed Computing Server from The MathWorks we were able to speed up our processing by 20 to 30 times,” said Andreas Korinek, scientist at the Max Planck Institute of Biochemistry.
“Particularly helpful was the ability to use our cluster productively from the MATLAB environment without having to be experts in parallel programming or having to learn another programming language. The changes to the existing serial applications are minimal; in most cases, our researchers did not have to go beyond changing for-loops to parallel for-loops to parallelize our MATLAB code and use the cluster productively.”
“Intensive research projects that require complex data processing can benefit greatly from the efficiencies that parallel computing affords. However, parallel programming is hard and traditionally engineers and scientists have had to program their applications using low-level languages,” said Silvina Grad-Freilich, manager of parallel computing and application deployment marketing at The MathWorks.
“With tools such as Parallel Computing Toolbox and MATLAB Distributed Computing Server, The MathWorks is committed to helping MATLAB users seamlessly make the transition to parallel programming.”
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