  Public cloud computing environments, such as Amazon AWS, Microsoft Azure, and
the Google Cloud Platform, have achieved remarkable improvements in
computational performance in recent years, and are also expected to be able to
perform massively parallel computing. As the cloud enables users to use
thousands of CPU cores and GPU accelerators casually, and various software
types can be used very easily by cloud images, the cloud is beginning to be
used in the field of bioinformatics. In this study, we ported the original
protein-protein interaction prediction (protein-protein docking) software,
MEGADOCK, into Microsoft Azure as an example of an HPC cloud environment. A
cloud parallel computing environment with up to 1,600 CPU cores and 960 GPUs
was constructed using four CPU instance types and two GPU instance types, and
the parallel computing performance was evaluated. Our MEGADOCK on Azure system
showed a strong scaling value of 0.93 for the CPU instance when H16 instance
with 100 instances were used compared to 50, and a strong scaling value of 0.89
for the GPU instance when NC24 instance with 20 were used compared to 5.
Moreover, the results of the usage fee and total computation time supported
that using a GPU instance reduced the computation time of MEGADOCK and the
cloud usage fee required for the computation. The developed environment
deployed on the cloud is highly portable, making it suitable for applications
in which an on-demand and large-scale HPC environment is desirable.
