As the number of atoms corresponding to data points (images) or the number of data points themselves in the train data set increases, serial computing takes a long time, and thus, resorting to parallel computing becomes inevitable. Two common approaches of parallel computing are either to parallelize over multiple threads or to parallelize over multiple processes. In the former approach, threads usually have access to the same memory area, which can lead to conflicts in case of improper synchronization. But in the second approach, separate memory locations are allocated to each spawned process which avoids any possible interference between sub-tasks. Amp takes the second approach via “multiprocessing”, a built-in module in Python 2.6 (and above) standard library. For details about multiprocessing module, the reader is directed to multiprocessing documentation. Only local concurrency (multi-computing over local cores) is possible in the current release of Amp. Amp starts with serial computing, but when arriving to calculating atomic fingerprints and their derivatives, multiple processes are spawned each doing a sub-task on part of the atomic configuration, and writing the results in temporary JSON files. Temporary files are next unified and saved in the script directory. If Fortran modules are utilized, data of all images including real energies and forces, atomic identities, and calculated values of fingerprints (as well as their derivatives and neighbor lists if force-training) are then reshaped to rectangular arrays. Portions of the reshaped data corresponding to each process are sent to the copy of Fortran modules at each core. Partial cost function and its derivative with respect to variables are calculated on each core, and the calculated values are returned back to the main python code via multiprocessing “Queues”. The main python code then adds up partial values. Amp automatically finds the number of requested cores, prints it out at the top of “train-log.txt” script, and makes use of all of them. A schematic of how parallel computing is designed in Amp is shown in the below figure. When descriptor is None, no fingerprint operation is carried out.