Gunrock: GPU Graph Analytics

For large-scale graph analytics on the GPU, the irregularity of data access and control flow, and the complexity of programming GPUs, have presented two significant challenges to developing a programmable high-performance graph library. "Gunrock", our graph-processing system designed specifically for the GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on operations on a vertex or edge frontier. Gunrock achieves a balance between performance and expressiveness by coupling high performance GPU computing primitives and optimization strategies with a high-level programming model that allows programmers to quickly develop new graph primitives with small code size and minimal GPU programming knowledge. We characterize the performance of various optimization strategies and evaluate Gunrock's overall performance on different GPU architectures on a wide range of graph primitives that span from traversal-based algorithms and ranking algorithms, to triangle counting and bipartite-graph-based algorithms. The results show that on a single GPU, Gunrock has on average at least an order of magnitude speedup over Boost and PowerGraph, comparable performance to the fastest GPU hardwired primitives and CPU shared-memory graph libraries such as Ligra and Galois, and better performance than any other GPU high-level graph library.Comment: 52 pages, invited paper to ACM Transactions on Parallel Computing (TOPC), an extended version of PPoPP'16 paper "Gunrock: A High-Performance Graph Processing Library on the GPU

Scaling Expected Force: Efficient Identification of Key Nodes in Network-based Epidemic Models

Centrality measures are fundamental tools of network analysis as they highlight the key actors within the network. This study focuses on a newly proposed centrality measure, Expected Force (EF), and its use in identifying spreaders in network-based epidemic models. We found that EF effectively predicts the spreading power of nodes and identifies key nodes and immunization targets. However, its high computational cost presents a challenge for its use in large networks. To overcome this limitation, we propose two parallel scalable algorithms for computing EF scores: the first algorithm is based on the original formulation, while the second one focuses on a cluster-centric approach to improve efficiency and scalability. Our implementations significantly reduce computation time, allowing for the detection of key nodes at large scales. Performance analysis on synthetic and real-world networks demonstrates that the GPU implementation of our algorithm can efficiently scale to networks with up to 44 million edges by exploiting modern parallel architectures, achieving speed-ups of up to 300x, and 50x on average, compared to the simple parallel solution

Efficient load balancing techniques for graph traversal applications on GPUs

Efficiently implementing a load balancing technique in graph traversal applications for GPUs is a critical task. It is a key feature of GPU applications as it can sensibly impact on the overall application performance. Different strategies have been proposed to deal with such an issue. Nevertheless, the efficiency of each of them strongly depends on the graph characteristics and no one is the best solution for any graph. This paper presents three different balancing techniques and how they have been implemented to fully exploit the GPU architecture. It also proposes a set of support strategies that can be modularly applied to the main balancing techniques to better address the graph characteristics. The paper presents an analysis and a comparison of the three techniques and support strategies with the best solutions at the state of the art over a large dataset of representative graphs. The analysis allows statically identifying, given graph characteristics and for each of the proposed techniques, the best combination of supports, and that such a solution is more efficient than the techniques at the state of the art

CO-expression Analysis of RNA-sequence Data from Parkinson's Disease Patients

Parkinson’s disease is known as a progressive neurological disease characterized by motor symptoms. The motor symptoms are caused by neurodegeneration that causes dysfunctionalities in molecular functions crucial for movement. Network analysis contributes to identifying new biomarkers of diseases by considering the interactions between the disease-specific genes and proteins. This study focuses on a differential weighted gene co-expression network analysis of transcriptomics data, comparing data from healthy persons with Parkinson’s diseased patients. This analysis method constructs networks and identifies modules that can be compared with different evaluation and analysis methods, to identify dysregulated pathways and causative genes of Parkinson’s disease. This disease is a complex disease by multiple variations of symptoms with each individual. This study contributes to the predictive part of personalized medicine that enables improved treatments.Masteroppgave i informatikkINF399MAMN-INFMAMN-PRO

Graph analytics on modern massively parallel systems

Graphs provide a very flexible abstraction for understanding and modeling complex systems in many fields such as physics, biology, neuroscience, engineering, and social science. Only in the last two decades, with the advent of Big Data era, supercomputers equipped by accelerators –i.e., Graphics Processing Unit (GPUs)–, advanced networking, and highly parallel file systems have been used to analyze graph properties such as reachability, diameter, connected components, centrality, and clustering coefficient. Today graphs of interest may be composed by millions, sometimes billions, of nodes and edges and exhibit a highly irregular structure. As a consequence, the design of efficient and scalable graph algorithms is an extraordinary challenge due to irregular communication and memory access patterns, high synchronization costs, and lack of data locality. In the present dissertation, we start off with a brief and gentle introduction for the reader to graph analytics and massively parallel systems. In particular, we present the intersection between graph analytics and parallel architectures in the current state-of-the-art and discuss the challenges encountered when solving such problems on large-scale graphs on these architectures (Chapter 1). In Chapter 2, some preliminary definitions and graph-theoretical notions are provided together with a description of the synthetic graphs used in the literature to model real-world networks. In Chapters 3-5, we present and tackle three different relevant problems in graph analysis: reachability (Chapter 3), Betweenness Centrality (Chapter 4), and clustering coefficient (Chapter 5). In detail, Chapter 3 tackles reachability problems by providing two scalable algorithms and implementations which efficiently solve st-connectivity problems on very large-scale graphs Chapter 4 considers the problem of identifying most relevant nodes in a network which plays a crucial role in several applications, including transportation and communication networks, social network analysis, and biological networks. In particular, we focus on a well-known centrality metrics, namely Betweenness Centrality (BC), and present two different distributed algorithms for the BC computation on unweighted and weighted graphs. For unweighted graphs, we present a new communication-efficient algorithm based on the combination of bi-dimensional (2D) decomposition and multi-level parallelism. Furthermore, new algorithms which exploit the underlying graph topology to reduce the time and space usage of betweenness centrality computations are described as well. Concerning weighted graphs, we provide a scalable algorithm based on an algebraic formulation of the problem. Finally, thorough comprehensive experimental results on synthetic and real- world large-scale graphs, we show that the proposed techniques are effective in practice and achieve significant speedups against state-of-the-art solutions. Chapter 5 considers clustering coefficients problem. Similarly to Betweenness Centrality, it is a fundamental tool in network analysis, as it specifically measures how nodes tend to cluster together in a network. In the chapter, we first extend caching techniques to Remote Memory Access (RMA) operations on distributed-memory system. The caching layer is mainly designed to avoid inter-node communications in order to achieve similar benefits for irregular applications as communication-avoiding algorithms. We also show how cached RMA is able to improve the performance of a new distributed asynchronous algorithm for the computation of local clustering coefficients. Finally, Chapter 6 contains a brief summary of the key contributions described in the dissertation and presents potential future directions of the work

A Social Network Analysis of Active Transportation Policy Networks

Background: In an effort to increase physical activity, communities are recognizing the importance of policy and environmental changes to facilitate active transportation. However, evidence on the policy partnerships and processes to achieve such policy and environmental changes, particularly in non-health sectors, is lacking. Methods: An online social network survey was administered in Fall 2015 to organizations engaged in active transportation policies in six cities across the United States. In addition to individual and organizational characteristics, relationships between organizations were assessed, including: level of collaboration around active transportation policies, frequency of contact, resource sharing to support active transportation, and perceived decisional power of partnering organizations. Descriptive and inferential network analyses were conducted. Results: An average of 25 individuals at 22 organizations in each city participated in the online survey, with a total of 149 respondents. Organization types represented in the full sample included: advocacy/nonprofit, local government, local transit agencies, metropolitan planning organizations, planning/engineering firms, public health, state and federal transportation organizations, and academic institutions. In all six cities, the likelihood of active transportation policy collaboration increased when organizations communicated at least quarterly. In half of the cities, the probability of active transportation policy collaboration increased when resources were exchanged between two agencies. In half of the cities, active transportation policy collaboration was more likely to occur when organizations were perceived as having decisional authority around active transportation policies. Conclusion: Information on the policy partnerships that exist around active transportation policies can help researchers, practitioners, policymakers, and advocates more effectively work together across diverse sectors to support active transportation