Breadth-first search for social network graphs on heterogenous platforms

Breadth-First Search (BFS) is the core of many graph analysis algorithms, and it is useful in many problems including social network, computer network analysis, and data organization, but, due to its irregular behav- ior, its parallel implementation is very challenging. There are several approaches that implement efficient algorithms for BFS in multicore architectures and in Graphics Processors, but an efficient implementation of BFS for heterogeneous systems is even more complicated, as the task of distributing the work among the main cores and the accelerators becomes a big challenge. As part of this work, we have assessed different heterogenous shared-memory architectures (from high- end processors to embedded mobile processors, both composed by a multi-core CPU and an integrated GPU) and implemented different approaches to perform BFS. This work introduces three heterogeneous approaches for BFS: Selective, Concurrent, and Async. Contributions of this work includes both the analysis of BFS performance on Heterogenous platforms, as well as in depth analysis of social network graphs and its implications on the BFS algorithm. The results show that BFS is very input dependent, and that the structure of the graph is one of the prime factors to analyze in order to develop good and scalable algorithms. The results also show that heterogenous platforms can provide acceleration to even irregular algorithms, reaching speed-ups of 2.2x in the best case. It is also shown how the different system configurations and capabilities impact the performance and how the shared-memory system can reach bandwidth limitations that prevent performance improvements despite having higher utilization of the resources

Graph Algorithms on GPUs

This chapter introduces the topic of graph algorithms on GPUs. It starts by presenting and comparing the main important data structures and techniques applied for representing and analysing graphs on GPUs at the state of the art.It then presents the theory and an updated review of the most efficient implementations of graph algorithms for GPUs. In particular, the chapter focuses on graph traversal algorithms (breadth-first search), single-source shortest path(Djikstra, Bellman-Ford, delta stepping, hybrids), and all-pair shortest path (Floyd-Warshall). By the end of the chapter, load balancing and memory access techniques are discussed through an overview of their main issues and management techniques

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

筑波大学計算科学研究センター 平成25年度 年次報告書

1 平成25 年度重点施策および改善目標の達成状況 ...... 22 自己評価と課題 ...... 83 各研究部門の報告 ...... 10I. 素粒子物理研究部門 ...... 10II. 宇宙・原子核物理研究部門 ...... 32II-1. 宇宙物理理論グループ ...... 32II-2. 原子核分野 ...... 56III. 量子物性研究部門 ...... 69IV. 生命科学研究部門 ...... 83IV-1. 生命機能情報分野 ...... 83IV-2. 分子進化分野 ...... 93V. 地球環境研究部門 ....... 104VI. 高性能計算システム研究部門 ...... 118VII. 計算情報学研究部門 ...... 148VII-1. データ基盤分野 ...... 148VII-2. 計算メディア分野 ...... 16