Scalable I/O-bound parallel incremental gradient descent for big data analytics in GLADE

Incremental gradient descent is a general technique to solve a large class of convex optimization problems arising in many machine learning tasks. GLADE is a parallel infrastructure for big data analytics providing a generic task specification interface. In this paper, we present a scalable and efficient parallel solution for incremental gradient descent in GLADE. We provide empirical evidence that our solution is limited only by the physical hardware characteristics, uses effectively the available resources, and achieves maximum scalability. When deployed in the cloud, our solution has the potential to dramatically reduce the cost of complex analytics over massive datasets.