In-Memory Data Rearrangement for Irregular, Data-Intensive Computing

Data Intensive Computing for Bioinformatics

Resistive Memory Based Acceleration of Data Intensive Computing

Resistive memory technologies hold the promise of replacing mainstream on-chip memory while providing enhanced throughput and capacity in modern compute systems. Demonstrating material, process, and circuit compatibility with existing CMOS infrastructures, resistive memories deliver non-volatility, no static power consumption, and improved density. Application of these technologies, however, re...

Data classification algorithm for data-intensive computing environments

Data-intensive computing has received substantial attention since the arrival of the big data era. Research on data mining in data-intensive computing environments is still in the initial stage. In this paper, a decision tree classification algorithm called MR-DIDC is proposed that is based on the programming framework of MapReduce and the SPRINT algorithm. MR-DIDC inherits the advantages of Ma...

Mobile Cloud Computing for Data-Intensive Applications

The computational and storage capabilities of today’s mobile devices are rapidly catching up with those of our traditional desktop computers and servers. In fact, mobile phones with 1 GHz processors are readily available in the market today. Unfortunately, all of these processing resources are mostly under-utilised and are generally used to process local data and programs only. With the use of ...

Scalable Storage for Data-Intensive Computing

Cloud computing applications require a scalable, elastic and fault tolerant storage system. We survey how storage systems have evolved from the traditional distributed filesystems, peer-to-peer storage systems and how these ideas have been synthesized in current cloud computing storage systems. Then, we describe how metadata management can be improved for a file system built to support large sc...