Abstract: Systems and methods for parallelization of machine learning computing code are described herein. In one aspect, embodiments of the present disclosure include a method of generating a plurality of instruction sets from machine learning computing code for parallel execution in a multi-processor environment, which may be implemented on a system, of, partitioning training data into two or more training data sets for performing machine learning, identifying a set of concurrently-executable tasks from the machine learning computing code, assigning the set of tasks to two or more of the computing elements in the multi-processor environment, and/or generating the plurality of instruction sets to be executed in the multi-processor environment to perform a set of processes represented by the machine learning computing code.

Abstract: Systems and methods for architecture-adaptable automatic parallelization of computing code are described herein. In one aspect, embodiments of the present disclosure include a method of generating a plurality of instruction sets from a sequential program for parallel execution in a multi-processor environment, which may be implemented on a system, of, identifying an architecture of the multi-processor environment in which the plurality of instruction sets are to be executed, determining running time of each of a set of functional blocks of the sequential program based on the identified architecture, determining communication delay between a first computing unit and a second computing unit in the multi-processor environment, and/or assigning each of the set of functional blocks to the first computing unit or the second computing unit based on the running times and the communication time.