A Performance/cost Model for a CUDA Drug Discovery Application on Physical and Public Cloud Infrastructures
The Journal of Concurrency and Computation: Practice and Experience has published online our work entitled “A performance/cost model for a CUDA drug discovery application on physical and public cloud infrastructures”, while waiting for its inclusion in a special issue on distributed, parallel, and GPU-accelerated approaches to Computational Biology.
Virtual Screening (VS) methods can considerably aid Drug Discovery research, predicting how ligands interact with drug targets. BINDSURF is an efficient and fast blind VS methodology for the determination of protein binding sites depending on the ligand, that uses the massively parallel architecture of GPUs for fast unbiased pre-screening of large ligand databases. In this contribution, we provide a performance/cost model for the execution of this application on both a physical and public cloud infrastructure. With our model it is possible to determine which is the best infrastructure by means of execution time and costs for any given problem to be solved by BINDSURF. Conclusions obtained from our study can be extrapolated to other GPU based VS methodologies.
This work is the result of a collaboration with a multidisciplinary research group from Catholic University of Murcia (Spain). Also, this is the first paper published by my PhD student Richard M. Wallace with our research group. Congratulations!