A hybrid CPU-GPU parallelization scheme of variable neighborhood search for inventory optimization problems

In this paper, we study various parallelization schemes for the Variable Neighborhood Search (VNS) metaheuristic on a CPU-GPU system via OpenMP and OpenACC. A hybrid parallel VNS method is applied to recent benchmark problem instances for the multi-product dynamic lot sizing problem with product returns and recovery, which appears in reverse logistics and is known to be NP-hard. We report our findings regarding these parallelization approaches and present promising computational results.Comment: 8 pages, 1 figur

Dynamic FTSS in Asynchronous Systems: the Case of Unison

Distributed fault-tolerance can mask the effect of a limited number of permanent faults, while self-stabilization provides forward recovery after an arbitrary number of transient fault hit the system. FTSS protocols combine the best of both worlds since they are simultaneously fault-tolerant and self-stabilizing. To date, FTSS solutions either consider static (i.e. fixed point) tasks, or assume synchronous scheduling of the system components. In this paper, we present the first study of dynamic tasks in asynchronous systems, considering the unison problem as a benchmark. Unison can be seen as a local clock synchronization problem as neighbors must maintain digital clocks at most one time unit away from each other, and increment their own clock value infinitely often. We present many impossibility results for this difficult problem and propose a FTSS solution when the problem is solvable that exhibits optimal fault containment

User-guided free-form asset modelling

In this paper a new system for piecewise primitive surface recovery on point clouds is presented, which allows a novice user to sketch areas of interest in order to guide the fitting process. The algorithm is demonstrated against a benchmark technique for autonomous surface fitting, and, contrasted against existing literature in user guided surface recovery, with empirical evidence. It is concluded that the system is an improvement to the current documented literature for its visual quality when modelling objects which are composed of piecewise primitive shapes, and, in its ability to fill large holes on occluded surfaces using free-form input

Benchmarking tests on recovery oriented computing

textBenchmarks have played a very important role in guiding the progress of computer science systems in various ways. Specifically, in Autonomous environments it has a major role to play. System crashes and software failures are a basic part of a software system’s life-cycle and to overcome or rather make it as less vulnerable as possible is the main purpose of recovery oriented computing. This is usually done by trying to reduce the downtime by automatically and efficiently recovering from a broad class of transient software failures without having to modify applications. There have been various types of benchmarks for recovering from a failure, but in this paper we intend to create a benchmark framework called the warning benchmarks to measure and evaluate the recovery oriented systems. It consists of the known and the unknown failures and few benchmark techniques which the warning benchmarks handle with the help of various other techniques in software fault analysis.Electrical and Computer Engineerin

Privacy and Fairness in Recommender Systems via Adversarial Training of User Representations

Latent factor models for recommender systems represent users and items as low dimensional vectors. Privacy risks of such systems have previously been studied mostly in the context of recovery of personal information in the form of usage records from the training data. However, the user representations themselves may be used together with external data to recover private user information such as gender and age. In this paper we show that user vectors calculated by a common recommender system can be exploited in this way. We propose the privacy-adversarial framework to eliminate such leakage of private information, and study the trade-off between recommender performance and leakage both theoretically and empirically using a benchmark dataset. An advantage of the proposed method is that it also helps guarantee fairness of results, since all implicit knowledge of a set of attributes is scrubbed from the representations used by the model, and thus can't enter into the decision making. We discuss further applications of this method towards the generation of deeper and more insightful recommendations.Comment: International Conference on Pattern Recognition and Method

S-Store: Streaming Meets Transaction Processing

Stream processing addresses the needs of real-time applications. Transaction processing addresses the coordination and safety of short atomic computations. Heretofore, these two modes of operation existed in separate, stove-piped systems. In this work, we attempt to fuse the two computational paradigms in a single system called S-Store. In this way, S-Store can simultaneously accommodate OLTP and streaming applications. We present a simple transaction model for streams that integrates seamlessly with a traditional OLTP system. We chose to build S-Store as an extension of H-Store, an open-source, in-memory, distributed OLTP database system. By implementing S-Store in this way, we can make use of the transaction processing facilities that H-Store already supports, and we can concentrate on the additional implementation features that are needed to support streaming. Similar implementations could be done using other main-memory OLTP platforms. We show that we can actually achieve higher throughput for streaming workloads in S-Store than an equivalent deployment in H-Store alone. We also show how this can be achieved within H-Store with the addition of a modest amount of new functionality. Furthermore, we compare S-Store to two state-of-the-art streaming systems, Spark Streaming and Storm, and show how S-Store matches and sometimes exceeds their performance while providing stronger transactional guarantees