Parallelizations are based on the concept of modular parallelism, i.e. parallel execution of network modules.
Extending on these previous approaches, we had three goals for parallelization of AD4: 1) enable parallel execution of AD4 across multiple HPC architectures, 2) reduce I/O, and 3) parallelize the execution of individual docking jobs.
The fork-join parallelism refers to a method of specifying the parallel execution of a program whereby the program flow diverges into two or more flows that can be executed concurrently, and then, all flows come back together into a single flow when all of the parallel work is completed.
The fields 'procedure' and 'disease' at the top of Figure 3 help the user to handle the parallel execution of tests, since independent procedures can be executed simultaneously in different equipment or laboratory areas.
A second metric parallelism can be orthogonally extracted from the BCJR algorithm through a parallel execution of the three BCJR computations (forward recursion, backward recursion, and extrinsic information) [3, 5].
This architecture allows a parallel execution of MATLAB requests.
In this paper we present several basic techniques for achieving parallel execution of constraint networks.
These solutions are programming language specific and provide no solution for the parallel execution of tasks.
This allows for improved parallel execution of instructions, which may otherwise stall.
Parallel execution of optimization algorithms could greatly save computational time [21, 22].
Parallel execution of antenna design is able to greatly save simulation time [7].
