Grids with multiple batch systems for performance enhancement of multi-component and parameter sweep parallel applications

In this work, we evaluate the benefits of using Grids with multiple batch systems to improve the performance of multi-component and parameter sweep parallel applications by reduction in queue waiting times. Using different job traces of different loads, job distributions and queue waiting times corresponding to three different queuing policies(FCFS, conservative and EASY backfilling), we conducted a large number of experiments using simulators of two important classes of applications. The first simulator models Community Climate System Model (CCSM), a prominent multi-component application and the second simulator models parameter sweep applications. We compare the performance of the applications when executed on multiple batch systems and on a single batch system for different system and application configurations. We show that there are a large number of configurations for which application execution using multiple batch systems can give improved performance over execution on a single system.

Recognition and alignment of homologous DNA sequences between minichromosomes and single-stranded DNA promoted by RecA protein

The incorporation of DNA into nucleosomes and higher-order forms of chromatin in vivo creates difficulties with respect to its accessibility for cellular functions such as transcription, replication, repair and recombination. To understand the role of chromatin structure in the process of homologous recombination, we have studied the interaction of nucleoprotein filaments, comprised of RecA protein and ssDNA, with minichromosomes. Using this paradigm, we have addressed how chromatin structure affects the search for homologous DNA sequences, and attempted to distinguish between two mutually exclusive models of DNA-DNA pairing mechanisms. Paradoxically, we found that the search for homologous sequences, as monitored by unwinding of homologous or heterologous duplex DNA, was facilitated by nucleosomes, with no discernible effect on homologous pairing. More importantly, unwinding of minichromosomes required the interaction of nucleoprotein filaments and led to the accumulation of circular duplex DNA sensitive to nuclease P1. Competition experiments indicated that chromatin templates and naked DNA served as equally efficient targets for homologous pairing. These and other findings suggest that nucleosomes do not impede but rather facilitate the search for homologous sequences and establish, in accordance with one proposed model, that unwinding of duplex DNA precedes alignment of homologous sequences at the level of chromatin. The potential application of this model to investigate the role of chromosomal proteins in the alignment of homologous sequences in the context of cellular recombination is considered.

Tailoring texture of γ-Y2Si2O7 by strong magnetic field alignment and two-step sintering

In this study, a well-dispersed γ-Y2Si2O 7 ethanol-based suspension with 30 vol% solid loading was prepared by adding 1 dwb% polyethylene imine dispersant, which allows feeble magnetic γ-Y2Si2O7 particles with anisotropic magnetic susceptibility to rotate in a 12 T strong magnetic field during slip casting, resulting in the development of a strong texture in green bodies. Pressureless sintering gives rise to more pronounced grain growth in the textured sample than in the untextured sample prepared without the magnetic field due to the rapid migration of the grain boundaries of the well-oriented grains, which was revealed by constant-heating-rate sintering kinetics. It was found that the use of two-step sintering is very efficient not only for inhibiting the grain growth but also for enhancing the texture. This implies that controlled grain growth is crucial for enhancing texture development in γ-Y2Si2O7.

Pareto-optimal solutions for multi-objective optimization of fed-batch bioreactors using nondominated sorting genetic algorithm

Many optimal control problems are characterized by their multiple performance measures that are often noncommensurable and competing with each other. The presence of multiple objectives in a problem usually give rise to a set of optimal solutions, largely known as Pareto-optimal solutions. Evolutionary algorithms have been recognized to be well suited for multi-objective optimization because of their capability to evolve a set of nondominated solutions distributed along the Pareto front. This has led to the development of many evolutionary multi-objective optimization algorithms among which Nondominated Sorting Genetic Algorithm (NSGA and its enhanced version NSGA-II) has been found effective in solving a wide variety of problems. Recently, we reported a genetic algorithm based technique for solving dynamic single-objective optimization problems, with single as well as multiple control variables, that appear in fed-batch bioreactor applications. The purpose of this study is to extend this methodology for solution of multi-objective optimal control problems under the framework of NSGA-II. The applicability of the technique is illustrated by solving two optimal control problems, taken from literature, which have usually been solved by several methods as single-objective dynamic optimization problems. (C) 2004 Elsevier Ltd. All rights reserved.

Alignment of the ALICE Inner Tracking System with cosmic-ray tracks

ALICE (A Large Ion Collider Experiment) is the LHC (Large Hadron Collider) experiment devoted to investigating the strongly interacting matter created in nucleus-nucleus collisions at the LHC energies. The ALICE ITS, Inner Tracking System, consists of six cylindrical layers of silicon detectors with three different technologies; in the outward direction: two layers of pixel detectors, two layers each of drift, and strip detectors. The number of parameters to be determined in the spatial alignment of the 2198 sensor modules of the ITS is about 13,000. The target alignment precision is well below 10 micron in some cases (pixels). The sources of alignment information include survey measurements, and the reconstructed tracks from cosmic rays and from proton-proton collisions. The main track-based alignment method uses the Millepede global approach. An iterative local method was developed and used as well. We present the results obtained for the ITS alignment using about 10^5 charged tracks from cosmic rays that have been collected during summer 2008, with the ALICE solenoidal magnet switched off.

Minimizing total weighted tardiness on a batch-processing machine with non-agreeable release times and due dates

This study considers the scheduling problem observed in the burn-in operation of semiconductor final testing, where jobs are associated with release times, due dates, processing times, sizes, and non-agreeable release times and due dates. The burn-in oven is modeled as a batch-processing machine which can process a batch of several jobs as long as the total sizes of the jobs do not exceed the machine capacity and the processing time of a batch is equal to the longest time among all the jobs in the batch. Due to the importance of on-time delivery in semiconductor manufacturing, the objective measure of this problem is to minimize total weighted tardiness. We have formulated the scheduling problem into an integer linear programming model and empirically show its computational intractability. Due to the computational intractability, we propose a few simple greedy heuristic algorithms and meta-heuristic algorithm, simulated annealing (SA). A series of computational experiments are conducted to evaluate the performance of the proposed heuristic algorithms in comparison with exact solution on various small-size problem instances and in comparison with estimated optimal solution on various real-life large size problem instances. The computational results show that the SA algorithm, with initial solution obtained using our own proposed greedy heuristic algorithm, consistently finds a robust solution in a reasonable amount of computation time.

Optimal design of multiproduct batch chemical plant using NSGA-II

The optimal design of a multiproduct batch chemical plant is formulated as a multiobjective optimization problem, and the resulting constrained mixed-integer nonlinear program (MINLP) is solved by the nondominated sorting genetic algorithm approach (NSGA-II). By putting bounds on the objective function values, the constrained MINLP problem can be solved efficiently by NSGA-II to generate a set of feasible nondominated solutions in the range desired by the decision-maker in a single run of the algorithm. The evolution of the entire set of nondominated solutions helps the decision-maker to make a better choice of the appropriate design from among several alternatives. The large set of solutions also provides a rich source of excellent initial guesses for solution of the same problem by alternative approaches to achieve any specific target for the objective functions

Scheduling parallel batch processors with incompatible job families using ant colony optimization

The present work concerns with the static scheduling of jobs to parallel identical batch processors with incompatible job families for minimizing the total weighted tardiness. This scheduling problem is applicable in burn-in operations and wafer fabrication in semiconductor manufacturing. We decompose the problem into two stages: batch formation and batch scheduling, as in the literature. The Ant Colony Optimization (ACO) based algorithm called ATC-BACO algorithm is developed in which ACO is used to solve the batch scheduling problems. Our computational experimentation shows that the proposed ATC-BACO algorithm performs better than the available best traditional dispatching rule called ATC-BATC rule.

Performance comparison of batch and continuous flow surface aeration systems

The oxygen transfer rate and the corresponding power requirement to operate the rotor are vital for design and scale-up of surface aerators. The aeration process can be analyzed in two ways such as batch and continuous systems. The process behaviors of batch and continuous flow systems are different from each other. The experimental and numerical results obtained through the batch systems cannot be relied on and applied for the designing of the continuous aeration tank. Based on the experimentation on batch and continuous type systems, the present work compares the performance of both the batch and continuous surface aeration systems in terms of their oxygen transfer capacity and power consumption. A simulation equation developed through experimentation has shown that continuous flow surface aeration systems are taking more energy than the batch systems. It has been found that batch systems are economical and better for the field application but not feasible where large quantity of wastewater is produced.

Minimizing total completion time on a batch processing machine with job families

We consider the problem of minimizing the total completion time on a single batch processing machine. The set of jobs to be scheduled can be partitioned into a number of families, where all jobs in the same family have the same processing time. The machine can process at most B jobs simultaneously as a batch, and the processing time of a batch is equal to the processing time of the longest job in the batch. We analyze that properties of an optimal schedule and develop a dynamic programming algorithm of polynomial time complexity when the number of job families is fixed. The research is motivated by the problem of scheduling burn-in ovens in the semiconductor industry

Minimizing total completion time on batch processing machines

We study the problem of minimizing total completion time on single and parallel batch processing machines. A batch processing machine is one which can process up to B jobs simultaneously. The processing time of a batch is equal to the largest processing time among all jobs in the batch. This problem is motivated by burn-in operations in the final testing stage of semiconductor manufacturing and is expected to occur in other production environments. We provide an exact solution procedure for the single-machine problem and heuristic algorithms for both single and parallel machine problems. While the exact algorithms have limited applicability due to high computational requirements, extensive experiments show that the heuristics are capable of consistently obtaining near-optimal solutions in very reasonable CPU times.