Accelerating Large-Scale Graph Processing with FPGAs: Lesson Learned and Future Directions

Processing graphs on a large scale presents a range of difficulties, including irregular memory access patterns, device memory limitations, and the need for effective partitioning in distributed systems, all of which can lead to performance problems on traditional architectures such as CPUs and GPUs. To address these challenges, recent...

Graph Greenifier: Towards Sustainable and Energy-Aware Massive Graph Processing in the Computing Continuum

Our society is increasingly digital, and its processes are increasingly digitalized. As an emerging technology for the digital society, graphs provide a universal abstraction to represent concepts and objects, and the relationships between them. However, processing graphs at a massive scale raises numerous sustainability challenges; becoming...

Distributed large-scale graph processing on FPGAs

Processing large-scale graphs is challenging due to the nature of the computation that causes irregular memory access patterns. Managing such irregular accesses may cause significant performance degradation on both CPUs and GPUs. Thus, recent research trends propose graph processing acceleration with Field-Programmable Gate Arrays (FPGA)....

Observing and tracking bandlimited graph processes from sampled measurements

A critical challenge in graph signal processing is the sampling of bandlimited graph signals; signals that are sparse in a well-defined graph Fourier domain. Current works focused on sampling time-invariant graph signals and ignored their temporal evolution. However, time can bring new insights on sampling since sensor, biological, and...

Controllability of bandlimited graph processes over random time varying graphs

Controllability of complex networks arises in many technological problems involving social, financial, road, communication, and smart grid networks. In many practical situations, the underlying topology might change randomly with time, due to link failures such as changing friendships, road blocks or sensor malfunctions. Thus, it leads to...

Elasticity in Graph Analytics?: A Benchmarking Framework for Elastic Graph Processing

Graphs are a natural fit for modeling concepts used in solving diverse problems in science, commerce, engineering, and governance. Responding to the diversity of graph data and algorithms, many parallel and distributed graph-processing systems exist. However, until now these platforms use a static model of deployment: they only run on a pre...

Exploring HPC and Big Data Convergence: A Graph Processing Study on Intel Knights Landing

The question 'Can big data and HPC infrastructure converge?' has important implications for many operators and clients of modern computing. However, answering it is challenging. The hardware is currently different, and fast evolving: big data uses machines with modest numbers of fat cores per socket, large caches, and much memory, whereas HPC...

Statistical Graph Signal Processing: Stationarity and Spectral Estimation

Stationarity is a cornerstone property that facilitates the analysis and processing of random signals in the time domain. Although time-varying signals are abundant in nature, in many contemporary applications the information of interest resides in more irregular domains that can be conveniently represented using a graph. This chapter reviews...

Graphalytics Global Competition: A Competition Platform to Compare Different Graph Processing Platforms

Graphs are becoming more popular day by day. This has lead to the development of different graph-processing platforms, such as Hadoop, Apache Giraph and GraphLab. With this wide variety of graph processing platforms, which one should we choose for a specific use case? Which platform has the best performance? To solve this issue, we can benchmark...

Design and Experimental Evaluation of Distributed Heterogeneous Graph-Processing Systems

Graph processing is increasingly used in a variety of domains, from engineering to logistics and from scientific computing to online gaming. To process graphs efficiently, GPU-enabled graph-processing systems such as TOTEM and Medusa exploit the GPU or the combined CPU+GPU capabilities of a single machine. Unlike scalable distributed CPU-based...

Distributed heterogeneous systems for large-scale graph processing

Graph processing is increasingly popular in a variety of scientific and engineering domains. Consequently, graphs and graph-processing algorithms have become increasingly more diverse. Following the big data trend in every computer-related domain, graphs have also become increasingly larger. Processing graphs is requiring more sophisticated...

Fine-grained Performance Evaluation of Large-scale Graph Processing Systems

In the age of information our society generates data at an increasing and already alarming rate. To keep up with the rapid increase in the amount of available data, the academics have shown strong interests in the emerging research field of Big Data Processing (BDP), which explores technologies aiming at efficient processing of enormous amounts...

An empirical study of large-scale graph processing with Pregel

In this age of information, data gathering has become a new growing trend. Social networking sites, Internet banking, online communities, all gather and store data concerning their users preferences, interactions or activities. This data is strongly relational and can be represented in the form of graphs where a Vertex represents the subject of...