Architectural requirements for energy efficient execution of graph analytics applications

Intelligent data analysis has become more important in the last decade especially because of the significant increase in the size and availability of data. In this paper, we focus on the common execution models and characteristics of iterative graph analytics applications. We show that the features that improve work efficiency can lead to significant overheads on existing systems. We identify the opportunities for custom hardware implementation, and outline the desired architectural features for energy efficient computation of graph analytics applications. © 2015 IEEE

Emerging accelerator platforms for data centers

CPU and GPU platforms may not be the best options for many emerging compute patterns, which led to a new breed of emerging accelerator platforms. This article gives a comprehensive overview with a focus on commercial platforms