Scalable Programming and Algorithms for Data-Intensive Life Science Applications

摘要

Cloud computing (Ekanayake et al., 2010) offers new approaches for scientific computing that leverage the major commercial hardware and software investment in this area. Closely coupled applications are still unclear in clouds as synchronization costs are still higher than on optimized MPI machines. However, loosely coupled problems are very important in many fields and can achieve good cloud performance even when pleasingly parallel steps are followed by reduction operations as supported by MapReduce. It appears that many data analysis problems fit the MapReduce paradigm but there is no definitive analysis here. For example, analysis of LHC (Large Hadron Collider) data corresponds to a data selection step followed by forming histograms; this naturally corresponds "perfectly" to the MapReduce paradigm. In Life Science, "all-pairs" applications like BLAST can run well with MapReduce but are particularly simple corresponding to "pleasingly parallel" or "map-only" structure.