Documentation/Network parallel rendering - PixieWiki
Keywords: Documentation/Network parallel rendering,Documentation/Multithreading

# Documentation/Network parallel rendering

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# [edit] Network Rendering

Pixie supports network parallel rendering where bunch of computers work on a single frame. Through this document, I'll use "client" as the computer that's dispatching the rendering job. This is usually the computer that you are working on. I'll use "server" for the computers that actually do the rendering. So for a given rib file, multiple servers work for a single client. For a computer to work as a server, a network rendering deamon has to be running on it. You can spawn a network rendering deamon on a computer by

    rndr -r

To render a rib file on the net, you can use:

    rndr -s <server1:port1,server2:port2,...> <rib_file>

Where `<server:port>` is an address descriptor for the server. The address of a server is displayed when you start the deamon. All the external files (rib, sdr, [dll/so], tif etc.) that the server can not find on the local computer will be downloaded from the client computer to a temporary folder (default: temp). At the end of rendering, the contents of the default folder is deleted. It is important that the directory that you launch the deamon is writable for this purpose. You also want this directory to be a local one so that the renderer does not have to go thru an NFS.

# [edit] Multiprocessor Rendering

To render using more than one processor (on a multiprocessor system) you can do the following:

    rndr -P:<n> <rib_file>

Where `<n>` is the number of processors to use. There is some overhead in paralellizing a render, so very simple renders may not achieve the full speed possible. In general the most efficiency will be attained when the scene is shading-heavy.

Starting with 2.0.1, Pixie supports multi threaded rendering. In general, this is faster and leaner than multi-processor rendering as described above. To start a rendering job with n threads, use

    rndr -t:<n> <rib_file>

See Documentation/Multithreading for more information.

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