Failure analysis and reliability -aware resource allocation of parallel applications in High Performance Computing systems

The demand for more computational power to solve complex scientific problems has been driving the physical size of High Performance Computing (HPC) systems to hundreds and thousands of nodes. Uninterrupted execution of large scale parallel applications naturally becomes a major challenge because a single node failure interrupts the entire application, and the reliability of a job completion decreases with increasing the number of nodes. Accurate reliability knowledge of a HPC system enables runtime systems such as resource management and applications to minimize performance loss due to random failures while also providing better Quality Of Service (QOS) for computational users. This dissertation makes three major contributions for reliability evaluation and resource management in HPC systems. First we study the failure properties of HPC systems and observe that Times To Failure (TTF\u27s) of individual compute nodes follow a time-varying failure rate based distribution like Weibull distribution. We then propose a model for the TTF distribution of a system of k independent nodes when individual nodes exhibit time varying failure rates. Based on the reliability of the proposed TTF model, we develop reliability-aware resource allocation algorithms and evaluated them on actual parallel workloads and failure data of a HPC system. Our observations indicate that applying time varying failure rate-based reliability function combined with some heuristics reduce the performance loss due to unexpected failures by as much as 30 to 53 percent. Finally, we also study the effect of reliability with respect to the number of nodes and propose reliability-aware optimal k node allocation algorithm for large scale parallel applications. Our simulation results of comparing the optimal k node algorithm indicate that choosing the number of nodes for large scale parallel applications based on the reliability of compute nodes can reduce the overall completion time and waste time when the k may be smaller than the total number of nodes in the system

Palladium catalyzed carbon-carbon bond formation under reductive, oxidative and redox neutral conditions

De organische chemie houdt zich bezig met reacties en eigenschappen van koolstof-gebaseerde verbindingen. Het is verantwoordelijk voor veel van de wonderen van de chemie die we waarnemen in ons dagelijkse leven zoals geneesmiddelen, plastics etc. Centraal binnen de studie van organische chemie staan reacties die de formatie van koolstof-koolstof bindingen mogelijk maken. In de afgelopen jaren, is het metaal palladium verrezen als een uitstekende katalysator voor de formatie van deze koolstof-koolstof bindingen. Dit proefschrift is toegewijd aan het onderzoek naar twee van zulke reacties, de Heck reactie en de geconjugeerde additie. De Heck reactie is erkend met de Nobel prijs in 2010. Dit proefschrift verkent beide reacties onder verschillende condities en de relatie tussen beide. Deze kennis staat ons toe om verschillende industrieel relevante reacties, die plaatsvinden met de formatie van grote hoeveelheden met metaal vervuild afval, te vervangen met schonere en goedkopere alternatieven. Daarnaast is het onderzoek in dit proefschrift gericht op de vorming van "benzylische quaternaire stereocentrums" via palladium-gekatalyseerde geconjugeerde additie reacties, een voorheen onbekende toepassing van deze reactie. Belangrijke eigenschappen van deze reactie werden ontdekt. Verschillende uitdagingen werden geïdentificeerd en hun oplossingen aangedragen. De voordelen van deze reactie is dat het reacties vervangt die drastische condities nodig hebben (gecontroleerde reactie-atmosfeer en erg lage temperaturen) of reacties die erg dure metal nodig hebben zoals rhodium. Een toepassing van deze nieuwe ontwikkeling is de synthese van het natuurproduct (–) - α - cuparenon in slecht 2 stappen, een molecuul dat voorheen tussen de 5 en 17 stappen vereiste

Deep Multi-view Learning to Rank

We study the problem of learning to rank from multiple information sources. Though multi-view learning and learning to rank have been studied extensively leading to a wide range of applications, multi-view learning to rank as a synergy of both topics has received little attention. The aim of the paper is to propose a composite ranking method while keeping a close correlation with the individual rankings simultaneously. We present a generic framework for multi-view subspace learning to rank (MvSL2R), and two novel solutions are introduced under the framework. The first solution captures information of feature mappings from within each view as well as across views using autoencoder-like networks. Novel feature embedding methods are formulated in the optimization of multi-view unsupervised and discriminant autoencoders. Moreover, we introduce an end-to-end solution to learning towards both the joint ranking objective and the individual rankings. The proposed solution enhances the joint ranking with minimum view-specific ranking loss, so that it can achieve the maximum global view agreements in a single optimization process. The proposed method is evaluated on three different ranking problems, i.e. university ranking, multi-view lingual text ranking and image data ranking, providing superior results compared to related methods.Comment: Published at IEEE TKD

One-Class Classification for Intrusion Detection on Vehicular Networks

Controller Area Network bus systems within vehicular networks are not equipped with the tools necessary to ward off and protect themselves from modern cyber-security threats. Work has been done on using machine learning methods to detect and report these attacks, but common methods are not robust towards unknown attacks. These methods usually rely on there being a sufficient representation of attack data, which may not be available due to there either not being enough data present to adequately represent its distribution or the distribution itself is too diverse in nature for there to be a sufficient representation of it. With the use of one-class classification methods, this issue can be mitigated as only normal data is required to train a model for the detection of anomalous instances. Research has been done on the efficacy of these methods, most notably One-Class Support Vector Machine and Support Vector Data Description, but many new extensions of these works have been proposed and have yet to be tested for injection attacks in vehicular networks. In this paper, we investigate the performance of various state-of-the-art one-class classification methods for detecting injection attacks on Controller Area Network bus traffic. We investigate the effectiveness of these techniques on attacks launched on Controller Area Network buses from two different vehicles during normal operation and while being attacked. We observe that the Subspace Support Vector Data Description method outperformed all other tested methods with a Gmean of about 85%.Comment: 7 pages, 2 figures, 4 tables. Accepted at IEEE Symposium Series on Computational Intelligence 202