Benchmarks & computer hardware
Standard benchmarks
With the addition of GPU-acceleration to release 2.0, standard benchmarks to compare the performance of new hardware has become more necessary than ever. Therefore, we suggest the following standard tests on the Plasmodium ribosome data set presented in Wong et al, eLife 2014:
ascp -QT -l 2G -i ~/.aspera/connect/etc/asperaweb_id_dsa.openssh emp_ext@fasp.ebi.ac.uk:archive/10028/data/Particles .
wget ftp://ftp.ebi.ac.uk/pub/databases/emdb/structures/EMD-2660/map/emd_2660.map.gz .
gunzip emd_2660.map.gz
2D classification
Run the (mpi-version of) the relion_refine program with the following command line arguments:
--i Particles/shiny_2sets.star --ctf --iter 25 --tau2_fudge 2 --particle_diameter 360 --K 200 --zero_mask --oversampling 1 --psi_step 6 --offset_range 5 --offset_step 2 --norm --scale --random_seed 0 --o class2d
3D classification
Run the (mpi-version of) the relion_refine program with the following command line arguments:
--i Particles/shiny_2sets.star --ref emd_2660.map:mrc --firstiter_cc --ini_high 60 --ctf --ctf_corrected_ref --iter 25 --tau2_fudge 4 --particle_diameter 360 --K 6 --flatten_solvent --zero_mask --oversampling 1 --healpix_order 2 --offset_range 5 --offset_step 2 --sym C1 --norm --scale --random_seed 0 --o class3d
3D auto-refine
Run the mpi-version of the relion_refine program with the following command line arguments:
--i Particles/shiny_2sets.star --ref emd_2660.map:mrc --firstiter_cc --ini_high 60 --auto_refine --split_random_halves --low_resol_join_halves 40 --ctf --ctf_corrected_ref --iter 25 --tau2_fudge 4 --particle_diameter 360 --flatten_solvent --zero_mask --oversampling 1 --healpix_order 2 --auto_local_healpix_order 4 --offset_range 5 --offset_step 2 --sym C1 --norm --scale --random_seed 0 --o refine3d
Options to play with
One major variable to play with is of course the number of parallel MPI processes to run. Besides, depending on your system, you may want to investigate the usage of the following options:
--jThe number of parallel threads to run on each CPU. We often use 4-6.--dont_combine_weights_via_discBy default large messages are passed between MPI processes through files on the computer disk. By giving this option, the messages will be passed through the network instead. We often use this option.--gpuUse GPU-acceleration.--pool--no_parallel_disc_io--preread_images--scratch_dir
Computer hardware options
Relion will run on any Linux-like machine, most typically on CPU/GPU clusters, or on GPU desktop (gamer-like) machines. A minimum of 64 GB of RAM is recommended to run relion.
Our collaborator in Stockholm, Erik Lindahl, has made a useful blog with GPU hardware recommendations. Briefly, you'll need an NVIDIA GPU with a CUDA compute ability of at least 3.0, but you don't need the expensive double-precision NVIDIA cards, i.e. the high-end gamer cards will also do, but do see Erik's blog for details! Note that 3D auto-refine will benefit from 2 GPUs, while 2D and 3D classification can be run just as well with 1 GPU. Apart from your GPUs you'll need a decent amount of RAM on the CPU (at least 64Gb), and you may also benefit from a fast (e.g. a 400Gb SSD!) scratch disk, especially of your working directories will be mounted over the network connecting multiple machines.