Most modern computers have at a minimum 4 cores. A decade or two ago this would have been called 4 CPUs, however, now you may still have 2 physical CPUs in your computer, but each may have multiple cores, effectively multiple CPUs in a single package. For that reason, we often make the distinction between the CPU (the physical package) and the number of cores (the effective number of CPUs). Typical computers in 2025 will have 4-12 cores, but can potentially have as many as 96.

Also please note that many vendors advertise the number of threads the CPU supports. In most cases this is 2x the number of cores, and really isn't very useful for heavy duty math and image processing. When specifying the number of threads in EMAN2, use the number of cores instead, though you may get a small benefit out of using ~25% more.

This page explains how to make use of multiple compute cores on your efficiently when running EMAN2 jobs. This parallelism mode is the most efficient by far, but will only work on a SINGLE computer at a time. Historically there is/was an additional mode for running EMAN2 jobs using MPI on multiple nodes at once. While this mode still exists, it requires some knowledge to configure, and with so many cores on a node and with increasing reliance on GPU computations, the MPI mechanism is no longer much of a focus.

If you do absolutely need to make use of multiple computers or clusters together, please see the main Parallelism page.

Programs with parallelism support will take the –parallel command line option as follows:

--parallel=thread:<n>

where <n> should be replaced by the number of cores you wish to use. That's it. Quite simple.

If using the project manager, any parallel boxes should contain thread:<N>. Any threads boxes should just contain the number of threads.

This will put (sometimes large) temporary files in /tmp. On some systems now /tmp is a ramdisk, which can cause real problems. You can use an alternate folder for these temporary files, but make sure they are on the local computer, not a remote filesystem shared among machines for the same account:

--parallel=thread:<n>:<tmp_path>

for example:

--parallel=thread:32:/home/stevel/tmp

The –threads option should not have this problem.

As above, in essence all you need to do is say, for example:

--parallel=thread:4

to make use of 4 cores on your computer.

If the specific program supports it, you should also specify:

--threads=4

This option is for cases where –parallel (which also supports MPI and other types of parallelism) cannot be used.

Specifying a number of threads significantly larger than the number of cores your computer has will quite probably cause the job to run more slowly, and in some cases may cause it to run disastrously slowly.

• What about hyperthreading - Some computers support the concept of hyperthreading. This is when a CPU pretends to have more cores than it actually has, and tries to run 2 jobs using the same core. Sometimes this can result in improved efficiency, as there may be pieces of the core which can work semi-independently. So, for example, if one job is trying to do floating point math and another job is trying to do integer math, it may be that they can both do their computations at the same time on the same core. This sort of coincidence is rare in most programs. With current generation Intel chips, you can get some benefit out of hyperthreading, but only a little. If your computer has 16 physical cores, and thus 32 “threads”, it may be useful to specify as many as 20 or even 24 threads. This will not make the job run 50% faster, but may make it run 5 or 10% faster. Using 32 threads is not generally useful, and will likely be counterproductive.

• How do I know how many cores my machine has ? - This depends on what OS you are using.
  • On a Mac, simply use the 'About this Mac' item on the apple menu then press the “System Report” button. On the main Hardware page you'll see something like “Total Number of Cores: 10 (8 performance and 2 efficiency)”, in which case 8 cores is a good number to use.
  • Under linux, you can 'cat /proc/cpuinfo', and look for the “cpu cores” entry for any one of the processors. As long as your machine has only 1 physical CPU, this is the number of cores. If the last processor has a “physical id”>0 then you have multiple CPUs and need to multiply the number of cores by the number of CPUs.

Note about disk space: - This parallelism option will put a bunch of scratch files in /tmp. These files can get quite large, so if your /tmp filesystem is small, you may wish to put the scratch files elsewhere. You can just specify –parallel=thread:<n>:</path/to/scratch> to do this, but be warned: The scratch directory MUST be on a local hard drive, NOT a shared filesystem from another computer !!! Violating this could lead to database corruption !

IMPORTANT WARNING ABOUT MEMORY - For most tasks, if you specify thread:8, that job will use 8x as much memory (RAM, NOT disk space) as if you use only a single thread. If you have, for example, only 16 gigs of RAM, and your job was using 4 gigs when you ran it on a single processor, if you then specify thread:8, you will probably exhaust your system memory, and likely cause either excessive swapping (making your machine seem to run like molasses), or possibly even crash the entire machine. This is particularly important if you have a machine with, say, 64 cores.

Note that not all programs will run in parallel, and some will intrinsically use the GPU rather than threads on the CPU. If a program does not accept the –parallel option or the –threads option, then it isn't parallelized.