SDAFT: A novel scalable data access framework for parallel BLAST

In this paper, we develop a scalable data access framework to solve the data movement problem for scientific applications that are dominated by “read” operation for data analysis. SDAFT employs a distributed file system (DFS) to provide scalable data access for parallel sequence searches. SDAFT consists of two interlocked components: (1) a data centric load-balanced scheduler (DC-scheduler) to enforce data-process locality and (2) a translation layer to translate conventional parallel I/O operations into HDFS I/O. By experimenting our SDAFT prototype system with real-world database and queries at a wide variety of computing platforms, we found that SDAFT can reduce I/O cost by a factor of 4-10 and double the overall execution performance as compared with existing schemes.