In recent years, malware has emerged as a critical security threat. In addition, malware
authors continue to embed numerous anti-detection features to evade the existing malware …

Modern analytics and recommendation systems are increasingly based on graph data that
capture the relations between entities being analyzed. Practical graphs come in huge sizes …

Graph label propagation (LP) is a core component in many downstream applications such
as fraud detection, recommendation and image segmentation. In this paper, we propose …

GPU is a powerful accelerator for parallel computation. Graph sampling is a fundamental
technology for large-scale graph analysis and learning. To accelerate graph sampling using …

Graph processing on GPUs received much attention in the industry and the academia
recently, as the hardware accelerator offers attractive potential for performance boost …

Fast, scalable, low-cost, and low-power execution of parallel graph algorithms is important
for a wide variety of commercial and public sector applications. Breadth First Search (BFS) …

In recent years the increasing necessity to speed up the execution of numerical algorithms
has leaded researchers to the use of co-processors and graphic cards such as the NVIDIA …

The combination of the growing size and complexity of application datasets is introducing a
new challenge to accelerators. The growing size of datasets forces us to place them in a …

The advancement of modern technologies has resulted in an explosive growth of complex
systems, such as the Internet, biological, social, and various infrastructure networks, which …

Limitations of I/O bandwidth and latency are a serious burden for many data intensive
algorithms. This is even more noticeable in distributed heterogeneous clusters that heavily …