On orbital allotments for geostationary satellites

The following satellite synthesis problem is addressed: communication satellites are to be allotted positions on the geostationary arc so that interference does not exceed a given acceptable level by enforcing conservative pairwise satellite separation. A desired location is specified for each satellite, and the objective is to minimize the sum of the deviations between the satellites' prescribed and desired locations. Two mixed integer programming models for the satellite synthesis problem are presented. Four solution strategies, branch-and-bound, Benders' decomposition, linear programming with restricted basis entry, and a switching heuristic, are used to find solutions to example synthesis problems. Computational results indicate the switching algorithm yields solutions of good quality in reasonable execution times when compared to the other solution methods. It is demonstrated that the switching algorithm can be applied to synthesis problems with the objective of minimizing the largest deviation between a prescribed location and the corresponding desired location. Furthermore, it is shown that the switching heuristic can use no conservative, location-dependent satellite separations in order to satisfy interference criteria

Minimizing the Message Waiting Time in Single-Hop Multichannel Systems

In this paper, we examine the problem of packet scheduling in a single-hop multichannel systems, with the goal of minimizing the average message waiting time. Such an objective function represents the delay incurred by the users before receiving the desired data. We show that the problem of finding a schedule with minimum message waiting time, is NP-complete, by means of polynomial time reduction of the time table design problem to our problem. We present also several heuristics which result in outcomes very close to the optimal ones. We compare these heuristics by means of extensive simulations

Theory and Engineering of Scheduling Parallel Jobs

Scheduling is very important for an efficient utilization of modern parallel computing systems. In this thesis, four main research areas for scheduling are investigated: the interplay and distribution of decision makers, the efficient schedule computation, efficient scheduling for the memory hierarchy and energy-efficiency. The main result is a provably fast and efficient scheduling algorithm for malleable jobs. Experiments show the importance and possibilities of scheduling considering the memory hierarchy

Minimum Message Waiting Time Scheduling in Distributed Systems

In this paper, we examine the problem of packet scheduling in a single-hop multichannel system, with the goal ofminimizing the average message waiting time. Such an objective function represents the delay incurred by the users before receivingthe desired data. We show that the problem of finding a schedule with minimum message waiting time is NP-complete, by means ofpolynomial time reduction of the time table design problem to our problem. We present also several heuristics that result in outcomesvery close to the optimal ones. We compare these heuristics by means of extensive simulations

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

Grid-job scheduling with reservations and preemption

Computational grids make it possible to exploit grid resources across multiple clusters when grid jobs are deconstructed into tasks and allocated across clusters. Grid-job tasks are often scheduled in the form of workflows which require synchronization, and advance reservation makes it easy to guarantee predictable resource provisioning for these jobs. However, advance reservation for grid jobs creates roadblocks and fragmentation which adversely affects the system utilization and response times for local jobs. We provide a solution which incorporates relaxed reservations and uses a modified version of the standard grid-scheduling algorithm, HEFT, to obtain flexibility in placing reservations for workflow grid jobs. Furthermore, we deploy the relaxed reservation with modified HEFT as an extension of the preemption based job scheduling framework, SCOJO-PECT job scheduler. In SCOJO-PECT, relaxed reservations serve the additional purpose of permitting scheduler optimizations which shift the overall schedule forward. Furthermore, a propagation heuristics algorithm is used to alleviate the workflow job makespan extension caused by the slack of relaxed reservation. Our solution aims at decreasing the fragmentation caused by grid jobs, so that local jobs and system utilization are not compromised, and at the same time grid jobs also have reasonable response times

Gain scheduling for geometrically nonlinear flexible space structures

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, February 2002.Includes bibliographical references (p. 181-185).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.A gain-scheduling approach for the control of geometrically nonlinear structures is developed. The objective is to improve performance over current linear design techniques that are applied to the same control problem. The approach is applicable to a variety of structures that have complex dynamics with slow variations such as flexible robotic arms and space structures with gimballing solar arrays. The modeling approach is motivated by the lack of in situ test data available for design of 0-g controllers. A Linear Fractional form allows the nonlinear and uncertain aspects of the structure to be modeled independently. The geometric nonlinearity is modeled using a feedback description of structural coupling. The uncertainty model is based on a physical parameter description, so that an experimentally identified 1-g parametric uncertainty model can be extrapolated to 0-g. The control approach is motivated by the success of linear control design synthesis and analysis techniques for space structures. Graphical heuristics for linear control design using Linear Quadratic Gaussian (LQG) and Sensitivity Weighted LQG techniques are introduced. A procedure to realize reduced-order gain-scheduled controllers from a family of linear state-space controllers is developed. A nonlinear analysis framework suitable for the slow variations of geometrically nonlinear structures is also presented. The realization procedure and nonlinear analysis is combined with the graphical linear design heuristics to form an iterative gain scheduled design process. The complete gain scheduling approach is applied to the MIT/MACE-II experiment flown on the International Space Station. Gain scheduled controller designs are shown to provide improved performance and robustness over a Multiple Model linear controllerdesign.by Jeremy Hoyt Yung.Ph.D

Improving programmability and performance for scientific applications

With modern advancements in hardware and software technology scaling towards new limits, our compute machines are reaching new potentials to tackle more challenging problems. While the size and complexity of both the problems and solutions increases, the programming methodologies must remain at a level that can be understood by programmers and scientists alike. In our work, this problem is encountered when developing an optimized framework to best exploit the semantic properties of a finite-element solver. In efforts to address this problem, we explore programming and runtime models which decouple algorithmic complexity, parallelism concerns, and hardware mapping. We build upon these frameworks to exploit domain-specific semantics using high-level transformations and modifications to obtain performance through algorithmic and runtime optimizations. We first discusses optimizations performed on a computational mechanics solver using a novel coupling technique for multi-time scale methods for discrete finite element domains. We exploit domain semantics using a high-level dynamic runtime scheme to reorder and balance workloads to greatly improve runtime performance. The framework presented automatically chooses a near-optimal coupling solution and runs a work-stealing parallel executor to run effectively on multi-core systems. In my latter work, I focus on the parallel programming model, Concurrent Collections (CnC), to seamlessly bridge the gap between performance and programmability. Because challenging problems in various domains, not limited to computation mechanics, requires both domain expertise and programming prowess, there is a need for ways to separate those concerns. This thesis describes methods and techniques to obtain scalable performance using CnC programming while limiting the burden of programming. These high level techniques are presented for two high-performance applications corresponding to hydrodynamics and multi-grid solvers