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Using your pi cluster to parallelize a closed-source PC application seems very difficult. First of all, some piece of the application must run on the Pi. For open source applications which run on the Pi and already have cluster support, it is quite possible.

For Blender (as @InkBlend says), there is multiblend, which runs rendering in parallel on a cluster. Also, since version 2.6, Blender natively supports network rendering. I would, however, not expect great speed-ups, a program using several cores on the laptop will perform a lot faster than the PIs, especially if they utilize the GPU for computing too. For Blender, GPU rendering is in development. The GPU on the Pi is quite powerful, but is hard to use for general computation since OpenCL is not (yet?)not (yet?) supported.

Another possibility is distributed compiling using distcc.

I think it might be more fun and educational to try to write some parallel program on the Pi cluster, for example using MPI, than trying to use the PIs to speed up a PC application. See the Pi supercomputer @InkBlend mentions.

Using your pi cluster to parallelize a closed-source PC application seems very difficult. First of all, some piece of the application must run on the Pi. For open source applications which run on the Pi and already have cluster support, it is quite possible.

For Blender (as @InkBlend says), there is multiblend, which runs rendering in parallel on a cluster. Also, since version 2.6, Blender natively supports network rendering. I would, however, not expect great speed-ups, a program using several cores on the laptop will perform a lot faster than the PIs, especially if they utilize the GPU for computing too. For Blender, GPU rendering is in development. The GPU on the Pi is quite powerful, but is hard to use for general computation since OpenCL is not (yet?) supported.

Another possibility is distributed compiling using distcc.

I think it might be more fun and educational to try to write some parallel program on the Pi cluster, for example using MPI, than trying to use the PIs to speed up a PC application. See the Pi supercomputer @InkBlend mentions.

Using your pi cluster to parallelize a closed-source PC application seems very difficult. First of all, some piece of the application must run on the Pi. For open source applications which run on the Pi and already have cluster support, it is quite possible.

For Blender (as @InkBlend says), there is multiblend, which runs rendering in parallel on a cluster. Also, since version 2.6, Blender natively supports network rendering. I would, however, not expect great speed-ups, a program using several cores on the laptop will perform a lot faster than the PIs, especially if they utilize the GPU for computing too. For Blender, GPU rendering is in development. The GPU on the Pi is quite powerful, but is hard to use for general computation since OpenCL is not (yet?) supported.

Another possibility is distributed compiling using distcc.

I think it might be more fun and educational to try to write some parallel program on the Pi cluster, for example using MPI, than trying to use the PIs to speed up a PC application. See the Pi supercomputer @InkBlend mentions.

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source | link

Using your pi cluster to parallelize a closed-source PC application seems very difficult. First of all, some piece of the application must run on the Pi. For open source applications which run on the Pi and already have cluster support, it is quite possible.

For Blender (as @InkBlend says), there is multiblend, which runs rendering in parallel on a cluster. Also, since version 2.6, Blender natively supports network rendering. I would, however, not expect great speed-ups, a program using several cores on the laptop will perform a lot faster than the PIs, especially if they utilize the GPU for computing too. For Blender, GPU rendering is in development. The GPU on the Pi is quite powerful, but is hard to use for general computation since OpenCL is not (yet?) supported.

Another possibility is distributed compiling using distcc.

I think it might be more fun and educational to try to write some parallel program on the Pi cluster, for example using MPI, than trying to use the PIs to speed up a PC application. See the Pi supercomputer @InkBlend mentions.