Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable

There has been significant recent interest in parallel graph processing due to the need to quickly analyze the large graphs available today. Many graph codes have been designed for distributed memory or external memory. However, today even the largest publicly-available real-world graph (the Hyperlink Web graph with over 3.5 billion vertices and 128 billion edges) can fit in the memory of a single commodity multicore server. Nevertheless, most experimental work in the literature report results on much smaller graphs, and the ones for the Hyperlink graph use distributed or external memory. Therefore, it is natural to ask whether we can efficiently solve a broad class of graph problems on this graph in memory. This paper shows that theoretically-efficient parallel graph algorithms can scale to the largest publicly-available graphs using a single machine with a terabyte of RAM, processing them in minutes. We give implementations of theoretically-efficient parallel algorithms for 20 important graph problems. We also present the optimizations and techniques that we used in our implementations, which were crucial in enabling us to process these large graphs quickly. We show that the running times of our implementations outperform existing state-of-the-art implementations on the largest real-world graphs. For many of the problems that we consider, this is the first time they have been solved on graphs at this scale. We have made the implementations developed in this work publicly-available as the Graph-Based Benchmark Suite (GBBS).Comment: This is the full version of the paper appearing in the ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), 201

Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network

The Internet of Medical Things (IoMT) workflow applications have been rapidly growing in practice. These internet-based applications can run on the distributed healthcare sensing system, which combines mobile computing, edge computing and cloud computing. Offloading and scheduling are the required methods in the distributed network. However, a security issue exists and it is hard to run different types of tasks (e.g., security, delay-sensitive, and delay-tolerant tasks) of IoMT applications on heterogeneous computing nodes. This work proposes a new healthcare architecture for workflow applications based on heterogeneous computing nodes layers: an application layer, management layer, and resource layer. The goal is to minimize the makespan of all applications. Based on these layers, the work proposes a secure offloading-efficient task scheduling (SEOS) algorithm framework, which includes the deadline division method, task sequencing rules, homomorphic security scheme, initial scheduling, and the variable neighbourhood searching method. The performance evaluation results show that the proposed plans outperform all existing baseline approaches for healthcare applications in terms of makespan

Automated Test Generation Based on an Applicational Model

Context: As testing is an extremely costly and time-consuming process, tools to automatically generate test cases have been proposed throughout the literature. OutSystems provides a software development environment where with the aid of the visual OutSystems language, developers can create their applications in an agile form, thus improving their productivity. Problem: As OutSystems aims at accelerating software development, automating the test case generation activity would bring great value to their clients. Objectives: The main objectives of this work are to: develop an algorithm that generates, automatically, test cases for OutSystems applications and evaluates the coverage they provide to the code, according to a set of criteria. Methods: The OutSystems language is represented as a graph to which developers can then add pieces of code by dragging nodes to the screen and connecting them to the graph. The methodology applied in this work consists in traversing these graphs with depth and breadth-first search algorithms, employing a boundary-value analysis to identify the test inputs and a cause-effect graphing to reduce the number of redundant inputs generated. To evaluate these test inputs, coverage criteria regarding the control flow of data are analysed according to node, branch, condition, modified condition-decision and multiple condition coverage. Results: This tool is able to generate test inputs that cover 100% of reachable code and the methodologies employed help greatly in reducing the inputs generated, as well as displaying a minimum set of test inputs with which the developer is already able to cover all traversable code. Usability tests also yield very optimistic feedback from users. Conclusions: This work’s objectives were fully met, seen as we have a running tool able to act upon a subset of the OutSystems applicational model. This work provides crucial information for assessing the quality of OutSystems applications, with value for OutSystems developers, in the form of efficiency and visibility

High-Performance and Power-Aware Graph Processing on GPUs

Graphs are a common representation in many problem domains, including engineering, finance, medicine, and scientific applications. Different problems map to very large graphs, often involving millions of vertices. Even though very efficient sequential implementations of graph algorithms exist, they become impractical when applied on such actual very large graphs. On the other hand, graphics processing units (GPUs) have become widespread architectures as they provide massive parallelism at low cost. Parallel execution on GPUs may achieve speedup up to three orders of magnitude with respect to the sequential counterparts. Nevertheless, accelerating efficient and optimized sequential algorithms and porting (i.e., parallelizing) their implementation to such many-core architectures is a very challenging task. The task is made even harder since energy and power consumption are becoming constraints in addition, or in same case as an alternative, to performance. This work aims at developing a platform that provides (I) a library of parallel, efficient, and tunable implementations of the most important graph algorithms for GPUs, and (II) an advanced profiling model to analyze both performance and power consumption of the algorithm implementations. The platform goal is twofold. Through the library, it aims at saving developing effort in the parallelization task through a primitive-based approach. Through the profiling framework, it aims at customizing such primitives by considering both the architectural details and the target efficiency metrics (i.e., performance or power)

Book of Abstracts of the Sixth SIAM Workshop on Combinatorial Scientific Computing

Book of Abstracts of CSC14 edited by Bora UçarInternational audienceThe Sixth SIAM Workshop on Combinatorial Scientific Computing, CSC14, was organized at the Ecole Normale Supérieure de Lyon, France on 21st to 23rd July, 2014. This two and a half day event marked the sixth in a series that started ten years ago in San Francisco, USA. The CSC14 Workshop's focus was on combinatorial mathematics and algorithms in high performance computing, broadly interpreted. The workshop featured three invited talks, 27 contributed talks and eight poster presentations. All three invited talks were focused on two interesting fields of research specifically: randomized algorithms for numerical linear algebra and network analysis. The contributed talks and the posters targeted modeling, analysis, bisection, clustering, and partitioning of graphs, applied in the context of networks, sparse matrix factorizations, iterative solvers, fast multi-pole methods, automatic differentiation, high-performance computing, and linear programming. The workshop was held at the premises of the LIP laboratory of ENS Lyon and was generously supported by the LABEX MILYON (ANR-10-LABX-0070, Université de Lyon, within the program ''Investissements d'Avenir'' ANR-11-IDEX-0007 operated by the French National Research Agency), and by SIAM

High Performance Computing for DNA Sequence Alignment and Assembly

Recent advances in DNA sequencing technology have dramatically increased the scale and scope of DNA sequencing. These data are used for a wide variety of important biological analyzes, including genome sequencing, comparative genomics, transcriptome analysis, and personalized medicine but are complicated by the volume and complexity of the data involved. Given the massive size of these datasets, computational biology must draw on the advances of high performance computing. Two fundamental computations in computational biology are read alignment and genome assembly. Read alignment maps short DNA sequences to a reference genome to discover conserved and polymorphic regions of the genome. Genome assembly computes the sequence of a genome from many short DNA sequences. Both computations benefit from recent advances in high performance computing to efficiently process the huge datasets involved, including using highly parallel graphics processing units (GPUs) as high performance desktop processors, and using the MapReduce framework coupled with cloud computing to parallelize computation to large compute grids. This dissertation demonstrates how these technologies can be used to accelerate these computations by orders of magnitude, and have the potential to make otherwise infeasible computations practical

Novel Bioinformatic Approaches for Analyzing Next-Generation Sequencing Data

In general, DNA reconstruction is deemed as the key of molecular biology since it makes people realize how genotype affects phenotypes. The DNA sequencing technology emerged exactly towards this and has greatly promoted molecular biology’s development. The traditional method, Sanger, is effective but extremely expensive on a cost-per-base basis. This shortcoming of Sanger method leads to the rapid development of next-generation sequencing technologies. The NGS technologies are widely used by virtue of their low-cost, high-throughput, and fast nature. However, they still face major drawbacks such as huge amounts of data as well as relatively short read length compared with traditional methods. The scope of the research mainly focuses upon a quick preliminary analysis of NGS data, identification of genome-wide structural variations (SVs), and microRNA prediction. In terms of preliminary NGS data analysis, the author developed a toolkit named SeqAssist to evaluate genomic library coverage and estimate the redundancy between different sequencing runs. Regarding the genome-wide SV detection, a one-stop pipeline was proposed to identify SVs, which integrates the components of preprocessing, alignment, SV detection, breakpoints revision, and annotation. This pipeline not only detects SVs at the individual sample level, but also identifies consensus SVs at the population and cross-population levels. At last, miRDisc, a pipeline for microRNA discovery, was developed for the identification of three categories of miRNAs, i.e., known, conserved, and novel microRNAs

Scripts in a Frame: A Framework for Archiving Deferred Representations

Web archives provide a view of the Web as seen by Web crawlers. Because of rapid advancements and adoption of client-side technologies like JavaScript and Ajax, coupled with the inability of crawlers to execute these technologies effectively, Web resources become harder to archive as they become more interactive. At Web scale, we cannot capture client-side representations using the current state-of-the art toolsets because of the migration from Web pages to Web applications. Web applications increasingly rely on JavaScript and other client-side programming languages to load embedded resources and change client-side state. We demonstrate that Web crawlers and other automatic archival tools are unable to archive the resulting JavaScript-dependent representations (what we term deferred representations), resulting in missing or incorrect content in the archives and the general inability to replay the archived resource as it existed at the time of capture. Building on prior studies on Web archiving, client-side monitoring of events and embedded resources, and studies of the Web, we establish an understanding of the trends contributing to the increasing unarchivability of deferred representations. We show that JavaScript leads to lower-quality mementos (archived Web resources) due to the archival difficulties it introduces. We measure the historical impact of JavaScript on mementos, demonstrating that the increased adoption of JavaScript and Ajax correlates with the increase in missing embedded resources. To measure memento and archive quality, we propose and evaluate a metric to assess memento quality closer to Web users’ perception. We propose a two-tiered crawling approach that enables crawlers to capture embedded resources dependent upon JavaScript. Measuring the performance benefits between crawl approaches, we propose a classification method that mitigates the performance impacts of the two-tiered crawling approach, and we measure the frontier size improvements observed with the two-tiered approach. Using the two-tiered crawling approach, we measure the number of client-side states associated with each URI-R and propose a mechanism for storing the mementos of deferred representations. In short, this dissertation details a body of work that explores the following: why JavaScript and deferred representations are difficult to archive (establishing the term deferred representation to describe JavaScript dependent representations); the extent to which JavaScript impacts archivability along with its impact on current archival tools; a metric for measuring the quality of mementos, which we use to describe the impact of JavaScript on archival quality; the performance trade-offs between traditional archival tools and technologies that better archive JavaScript; and a two-tiered crawling approach for discovering and archiving currently unarchivable descendants (representations generated by client-side user events) of deferred representations to mitigate the impact of JavaScript on our archives. In summary, what we archive is increasingly different from what we as interactive users experience. Using the approaches detailed in this dissertation, archives can create mementos closer to what users experience rather than archiving the crawlers’ experiences on the Web

Algorithm Engineering for Realistic Journey Planning in Transportation Networks

Diese Dissertation beschäftigt sich mit der Routenplanung in Transportnetzen. Es werden neue, effiziente algorithmische Ansätze zur Berechnung optimaler Verbindungen in öffentlichen Verkehrsnetzen, Straßennetzen und multimodalen Netzen, die verschiedene Transportmodi miteinander verknüpfen, eingeführt. Im Fokus der Arbeit steht dabei die Praktikabilität der Ansätze, was durch eine ausführliche experimentelle Evaluation belegt wird