Information Cost Tradeoffs for Augmented Index and Streaming Language Recognition

This paper makes three main contributions to the theory of communication complexity and stream computation. First, we present new bounds on the information complexity of AUGMENTED-INDEX. In contrast to analogous results for INDEX by Jain, Radhakrishnan and Sen [J. ACM, 2009], we have to overcome the significant technical challenge that protocols for AUGMENTED-INDEX may violate the "rectangle property" due to the inherent input sharing. Second, we use these bounds to resolve an open problem of Magniez, Mathieu and Nayak [STOC, 2010] that asked about the multi-pass complexity of recognizing Dyck languages. This results in a natural separation between the standard multi-pass model and the multi-pass model that permits reverse passes. Third, we present the first passive memory checkers that verify the interaction transcripts of priority queues, stacks, and double-ended queues. We obtain tight upper and lower bounds for these problems, thereby addressing an important sub-class of the memory checking framework of Blum et al. [Algorithmica, 1994]

Multiairport capacity management: genetic algorithm with receding horizon

The inability of airport capacity to meet the growing air traffic demand is a major cause of congestion and costly delays. Airport capacity management (ACM) in a dynamic environment is crucial for the optimal operation of an airport. This paper reports on a novel method to attack this dynamic problem by integrating the concept of receding horizon control (RHC) into a genetic algorithm (GA). A mathematical model is set up for the dynamic ACM problem in a multiairport system where flights can be redirected between airports. A GA is then designed from an RHC point of view. Special attention is paid on how to choose those parameters related to the receding horizon and terminal penalty. A simulation study shows that the new RHC-based GA proposed in this paper is effective and efficient to solve the ACM problem in a dynamic multiairport environment

Exploring optimization for the random-field Ising model

The push-relabel algorithm can be used to calculate rapidly the exact ground states for a given sample with a random-field Ising model (RFIM) Hamiltonian. Although the algorithm is guaranteed to terminate after a time polynomial in the number of spins, implementation details are important for practical performance. Empirical results for the timing in dimensions d=1,2, and 3 are used to determine the fastest among several implementations. Direct visualization of the auxiliary fields used by the algorithm provides insight into its operation and suggests how to optimize the algorithm. Recommendations are given for further study of the RFIM.Comment: 10 page

BAG : Managing GPU as buffer cache in operating systems

This paper presents the design, implementation and evaluation of BAG, a system that manages GPU as the buffer cache in operating systems. Unlike previous uses of GPUs, which have focused on the computational capabilities of GPUs, BAG is designed to explore a new dimension in managing GPUs in heterogeneous systems where the GPU memory is an exploitable but always ignored resource. With the carefully designed data structures and algorithms, such as concurrent hashtable, log-structured data store for the management of GPU memory, and highly-parallel GPU kernels for garbage collection, BAG achieves good performance under various workloads. In addition, leveraging the existing abstraction of the operating system not only makes the implementation of BAG non-intrusive, but also facilitates the system deployment

Scheduling and Tuning Kernels for High-performance on Heterogeneous Processor Systems

Accelerated parallel computing techniques using devices such as GPUs and Xeon Phis (along with CPUs) have proposed promising solutions of extending the cutting edge of high-performance computer systems. A significant performance improvement can be achieved when suitable workloads are handled by the accelerator. Traditional CPUs can handle those workloads not well suited for accelerators. Combination of multiple types of processors in a single computer system is referred to as a heterogeneous system. This dissertation addresses tuning and scheduling issues in heterogeneous systems. The first section presents work on tuning scientific workloads on three different types of processors: multi-core CPU, Xeon Phi massively parallel processor, and NVIDIA GPU; common tuning methods and platform-specific tuning techniques are presented. Then, analysis is done to demonstrate the performance characteristics of the heterogeneous system on different input data. This section of the dissertation is part of the GeauxDock project, which prototyped a few state-of-art bioinformatics algorithms, and delivered a fast molecular docking program. The second section of this work studies the performance model of the GeauxDock computing kernel. Specifically, the work presents an extraction of features from the input data set and the target systems, and then uses various regression models to calculate the perspective computation time. This helps understand why a certain processor is faster for certain sets of tasks. It also provides the essential information for scheduling on heterogeneous systems. In addition, this dissertation investigates a high-level task scheduling framework for heterogeneous processor systems in which, the pros and cons of using different heterogeneous processors can complement each other. Thus a higher performance can be achieve on heterogeneous computing systems. A new scheduling algorithm with four innovations is presented: Ranked Opportunistic Balancing (ROB), Multi-subject Ranking (MR), Multi-subject Relative Ranking (MRR), and Automatic Small Tasks Rearranging (ASTR). The new algorithm consistently outperforms previously proposed algorithms with better scheduling results, lower computational complexity, and more consistent results over a range of performance prediction errors. Finally, this work extends the heterogeneous task scheduling algorithm to handle power capping feature. It demonstrates that a power-aware scheduler significantly improves the power efficiencies and saves the energy consumption. This suggests that, in addition to performance benefits, heterogeneous systems may have certain advantages on overall power efficiency

Automaattisen varastointi- ja keruujärjestelmän ohjausperiaatteet

Automated storage and retrieval systems (AS/RS) are popular for storing semi-fast moving items in distribution centers. They are costly systems whose design involves many critical decisions, which affect the overall performance of the system. This work is focused on the crane control policies of a double-deep dual-shuttle AS/RS. The goal of the thesis is to find out how operating performance of a crane can be improved with storage location assignment, dwell-point positioning and request sequencing. Alternative control rules were developed based on AS/RS literature and tested against those implemented by the supplier of the system. The comparison was carried out with a discrete-event simulation tool, which was built as a part of the thesis. The system was simulated under two different workload scenarios and rack fill levels. The simulation results indicate that sequencing and cycle formation algorithms can have a significant effect on system throughput in periods of high utilization. The effect was found larger with the lower 70 % fill level. The linear programming sequencing algorithm developed in this thesis was found to reduce the average cycle time by 5.3 % compared to the algorithm used by the supplier. In the on-shift scenario, the optimal dwell point strategy could reduce the average crane response time by 10 % compared to the policy used by the supplier. However, this difference was not noticeable when the average request turnover time was used as a measure.Jakelukeskuksissa yleiset hyllystöhissijärjestelmät ovat kalliita investointeja. Niiden keräilytehoa voidaan osaltaan parantaa tehostamalla varastohissin ohjausmenetelmiä. Tämän diplomityön tutkimuksen kohteena on hyllystöhissijärjestelmä, jossa on tuplasyvät hyllyt sekä hissi, jolla on kahden laatikon kantokapasiteetti. Työn tavoitteena oli selvittää, miten järjestelmän suorituskykyä voidaan parantaa hyllypaikkojen allokoinnin, hissin odotuspaikan valinnan, sekä hissitehtävien sekvensoinnin avulla. Järjestelmätoimittajan ohjausperiaatteiden hyvyyttä arvioitiin vertaamalla niitä kirjallisuuden pohjalta kehitettyihin ohjausmenetelmiin. Ohjausten vertailu tehtiin simulointityökalulla, joka rakennettiin työn aikana. Testiskenaarioissa simuloitiin järjestelmää kahdessa eri kuormitustilanteessa ja kahdella eri täyttöasteella. Simulointitulosten perusteella sekvensointi- ja syklinmuodostusalgoritmeilla huomattiin olevan merkittävä vaikutus tilanteissa, joissa hissillä on tehtäväjonoja. Vaikutus oli suurempi matalammalla 70 % täyttöasteella. Työssä kehitetty sekalukuoptimointiin perustuva sekvensointialgoritmi lyhensi keskimääräistä sykliaikaa 5,3 % toimittajan käyttämään algoritmiin verrattuna. Matalan käyttöasteen skenaariossa optimaalisen odotuspaikan valinta lyhensi hissin liikkumisaikaa seuraavan tehtävään 10 %, mutta ero oli käytännössä olematon, kun mittarina käytettiin keskimääräistä aikaa tehtävän saapumisesta sen suorittamiseen

A Comprehensive Analysis of Swarming-based Live Streaming to Leverage Client Heterogeneity

Due to missing IP multicast support on an Internet scale, over-the-top media streams are delivered with the help of overlays as used by content delivery networks and their peer-to-peer (P2P) extensions. In this context, mesh/pull-based swarming plays an important role either as pure streaming approach or in combination with tree/push mechanisms. However, the impact of realistic client populations with heterogeneous resources is not yet fully understood. In this technical report, we contribute to closing this gap by mathematically analysing the most basic scheduling mechanisms latest deadline first (LDF) and earliest deadline first (EDF) in a continuous time Markov chain framework and combining them into a simple, yet powerful, mixed strategy to leverage inherent differences in client resources. The main contributions are twofold: (1) a mathematical framework for swarming on random graphs is proposed with a focus on LDF and EDF strategies in heterogeneous scenarios; (2) a mixed strategy, named SchedMix, is proposed that leverages peer heterogeneity. The proposed strategy, SchedMix is shown to outperform the other two strategies using different abstractions: a mean-field theoretic analysis of buffer probabilities, simulations of a stochastic model on random graphs, and a full-stack implementation of a P2P streaming system.Comment: Technical report and supplementary material to http://ieeexplore.ieee.org/document/7497234