Efficient algorithms for mining biological datasets on modern Graphics Processing Units (GPUs)
SANDER Pedro V
Modern GPUs can offer tremendous computing power, which far exceeds CPUs. Thus GPUs may provide solutions for the grand challenge in computational biology which is to efficiently mine massive datasets generated by computers. In this interdisciplinary project, the students will be co-supervised by professors from Chemistry and Computer Science to implement data mining algorithms in GPUs specifically for biological datasets. Gaining insights of these datasets could help understand a wide range of biological processes and many human diseases. Students with sufficient skills in GPU programming are preferred.
UROP1000 UROP1100 UROP2100 UROP3100
The students will implement clustering algorithms in GPUs specifically for biological datasets. A couple of examples are K-center and K-means algorithms.
Applicant's Learning Objectives:
1. To learn GPU programming for the purpose of general scientific computing
2. To learn how to deal with massive biological datasets generated by computer simulations
3. To gain insights of these biological datasets which help understand many biological processes