Guest post from Dr. Xuhui Huang, Hong Kong University of Science and Technology
In this post, I want to introduce a new GPU-powered clustering algorithm we recently developed to analyze the large molecular dynamics simulation datasets generated by Folding@home. Folding@home can generate enormous sets of protein structures. A critical step in analyzing these large datasets involves some form of reduction in the dataset, usually in the form of clustering. We recently developed a GPU powered clustering algorithm using the intrinsic properties of a metric space to rapidly accelerate the clustering. Overall, our algorithm is up to two orders of magnitude faster than the CPU implementation, and holds even more promise with the ever increasing performance in GPU hardware.
This algorithm should facilitate numerous applications. For example, one of the systems we tested our code on is the human islet amyloid polypeptide (hIAPP) peptide, whose aggregation is implicated in Type 2 diabetes. We hope further analysis of this data will provide insights that will inform the development of treatments for diabetes.
