The Performance of a Molecular Dynamics Simulation for the Plasmodium falciparum Enoyl-acyl carrier-protein Reductase Enzyme using Amber and GTX 780 and 970 Double Graphical Processing Units

The invention of graphical processing units (GPUs) has significantly improved the speed of long processes used in molecular dynamics (MD) to search for drug candidates to treat diseases, such as malaria. Previous work using a single GTX GPU showed considerable improvement compared to GPUs run in a cluster environment. In the current work, AMBER and dual GTX 780 and 970 GPUs were used to run an MD simulation on the Plasmodium falciparum enoyl-acyl carrier protein reductase enzyme; the results showed that performance was improved, particularly for molecules with a large number of atoms using single GPU.

GPU; Molecular dynamic simulation; PfENR

The results show that GPU specifications can be used to determine the performance of MD simulations, with the number of cores and memory clock rate being primary factors affecting simulation performance. Using AMBER and two GPUs for large proteins, such as PfENR (37,873 atoms) which is a potential target for antimalarial drugs, JAC (23,558 atoms), and myoglobin (2,492 atoms), can improve performance in ns/day by two-fold and reduce execution time compared to using a single GPU. For small proteins, such as TRP cage (304 atoms), using two GPUs does not improve performance. A significant improvement in simulation performance occurred for molecules that had more atoms because, to communicate between GPUs, the hardware must synchronize data from the first GPU, the CPU memory, the CPU, and the second GPU. If the amount of data is small, there will be overhead to this synchronization, but if the amount of data is large the synchronization process is more efficient, which improves simulation performance by reducing simulation time. In addition to inputting the size of the protein, the size of the threads per block also affects performance as measured by ns/day. Thus, a maximum thread value per block based on GPU specifications should be used to improve simulation performance. MD simulations can be performed in a public cloud-computing environment, in which all infrastructure requirements for the hardware and software have been provided by the service provider, including support tools to simplify the user interface. This is very helpful for researchers who do not have the ability to build the infrastructure, install the hardware or software, or configure system administration. Additionally, users do not have to solve problems in the infrastructure, such as power or cooling issues. Nevertheless, the hardware provided by the service provider, as well as the waiting time prior to execution, depends on the level of activity in the computing environment, which also affects the overall simulation time.

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