File Entry: Distributed Data Partitioning Interface for Homogeneous Clusters in Protein Secondary Structure Prediction.

 The effort required to write efficient parallel programmes or to parallelize existing sequential algorithms remains daunting. This is true even for regularly structured problems. Much of the work takes place when partitioning and distributing workloads over processors in a distributed computing environment. To alleviate this task, we present a data parallel interface called Distributed Data Partitioning Interface (DDPI). Its simple interface permits parallel implementation even by users with little understanding of parallel computing technicalities. In this work we evaluate the performance of DDPI in several computationally intensive problems such as matrix multiplication, data clustering and neural network batch training. Through these problems, we demonstrate that it is possible to achieve almost ideal speedups when they are parallelized with DDPI.