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By using this option, all particles are copied to a scratch disk, from where they will be read (every iteration) instead.We often use this option if we don't have enough RAM to read in all the particles, but we have large enough fast SSD scratch disk(s) (e.g. Each node of our cluster has at least 64GB RAM, and an Intel(R) Xeon(R) CPU E5-2667 0 (@ 2.90GHz).See wikipedia's CUDA page for a complete list of such GPUs.If you have one or more cuda-capable GPUs, using them in relion is as easy as adding the flag .Our cluster nodes do not have large enough scratch disks to store the data, nor is there enough RAM for all slaves to read the data into memory.This machine has 2 Titan-X (Pascal) GPUs, 64GB RAM, and a 12-core i7-6800K CPU (@3.40GHz).Use this option to have the master read all particles, and then send them all through the network. By default, all particles are read from the computer disk in every iteration.Using this option, they are all read into RAM once, at the very beginning of the job instead.

Note: this calculation used 10 nodes (with a total of 120 physical cores, or 240 hyperthreaded ones).

You can use an argument to the option to provide a list of device-indices.

The syntax is then to delimit ranks with colons [:], and threads by commas [,].

Each mpi-rank requires it’s own copy of large object in CPU and GPU memory, but if it can fit into memory it may in fact be faster to run 2 or more MPI processes on each GPU, as the MPI processes may become asynchronized so that one MPI process is busy doing calculations, while the other one for example is reading images from disk.

This section describes more advanced syntax for restricting RELION processes to certain GPUs on multi-GPU setups.

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