The programming model of ASSIST, an environment for parallel and distributed portable applications

A software development system based upon integrated skeleton technology (ASSIST) is a proposal of a new programming environment oriented to the development of parallel and distributed high-performance applications according to a unified approach. The main goals are: high-level programmability and software productivity for complex multidisciplinary applications, including data-intensive and interactive software; performance portability across different platforms, in particular large-scale platforms and grids; effective reuse of parallel software; efficient evolution of applications through versions that scale according to the underlying technologies.

The purpose of this paper is to show the principles of the proposed approach in terms of the programming model (successive papers will deal with the environment implementation and with performance evaluation). The features and the characteristics of the ASSIST programming model are described according to an operational semantics style and using examples to drive the presentation, to show the expressive power and to discuss the research issues.

According to our previous experience in structured parallel programming, in ASSIST we wish to overcome some limitations of the classical skeletons approach to improve generality and flexibility, expressive power and efficiency for irregular, dynamic and interactive applications, as well as for complex combinations of task and data parallelism. A new paradigm, called “parallel module” (parmod), is defined which, in addition to expressing the semantics of several skeletons as particular cases, is able to express more general parallel and distributed program structures, including both data-flow and nondeterministic reactive computations. ASSIST allows the programmer to design the applications in the form of generic graphs of parallel components. Another distinguishing feature is that ASSIST modules are able to utilize external objects, including shared data structures and abstract objects (e.g. CORBA), with standard interfacing mechanisms. In turn, an ASSIST application can be reused and exported as a component for other applications, possibly expressed in different formalisms.     

Quality prediction and analysis for large-scale processes based on multi-level principal component modeling strategy

This paper proposed a multi-level principal component regression (PCR) modeling strategy for quality prediction and analysis of large-scale processes. Based on decomposition of the large data matrix, the first level PCR model divides the process into different sub-blocks through uncorrelated principal component directions, with a related index defined for determination of variables in each sub-block. In the second level, a PCR model is developed for local quality prediction in each sub-block. Subsequently, the third level PCR model is constructed to combine the local prediction results in different sub-blocks. For process analysis, a sub-block contribution index is defined to identify the critical-to-quality sub-blocks, based on which an inside sub-block contribution index is further defined for determination of the key variables in each sub-block. As a result, correlations between process variables and quality variables can be successfully constructed. A case study on Tennessee Eastman (TE) benchmark process is provided for performance evaluation.     

Fast, large-scale model predictive control by partial enumeration

Partial enumeration (PE) is presented as a method for treating large, linear model predictive control applications that are out of reach with available MPC methods. PE uses both a table storage method and online optimization to achieve this goal. Versions of PE are shown to be closed-loop stable. PE is applied to an industrial example with more than 250 states, 32 inputs, and a 25-sample control horizon. The performance is less than 0.01% suboptimal, with average speedup factors in the range of 80-220, and worst-case speedups in the range of 4.9-39.2, compared to an existing MPC method. Small tables with only 25-200 entries were used to obtain this performance, while full enumeration is intractable for this example.     

Decentralised variable gain robust controller design for a class of large-scale interconnected systems with mismatched uncertainties

In this paper, we propose an LMI-based design method of a decentralised variable gain robust controller for large-scale interconnected systems with mismatched uncertainties. The mismatched uncertainties under consideration are composed of the matched part and the mismatched one, and the proposed decentralised robust controller consists of a state feedback with a fixed gain and one with a variable gain tuned by parameter adjustment laws. Sufficient conditions for the existence of the proposed decentralised variable gain robust controller are given in terms of linear matrix inequalities (LMIs). Finally, a numerical example is illustrated to validate the proposed design procedure.     

大型活动安全风险评估系统建设研究

随着我国经济文化逐步发展,大型活动数量越来越多,规模也越来越大,其举办过程存在的安全问题也日益凸显,引起人们重视。因此有必要建立有效的安全风险评估方法事先对举办单位进行甄选,并在此基础上,利用网络通讯和应用管理平台,研究开发以接待评估活动举办过程中存在风险因素可能性和严重性的信息管理系统,供评估机构进行遴选。本文以大型活动安全评估为基本研究对象,通过阅读文献提出假设,通过调查问卷和德尔菲法分析大型活动主要风险因素,阐述建立大型活动风险因素指标体系以及信息化系统建设主要内容,为相关平台构建提供参考。     

关于数控机床机座的铸造模式的探讨

文章主要阐述了数控机床利用消失模铸造工艺生产,中压机床机座铸件的生产过程,对冲压机床机座铸件,从浇注位置的选择及浇注工艺的设计,模样组合,涂料涂挂,装箱浇注和过程控制等方面进行了深入探索和实践。     

Decentralised sampled-data control for large-scale systems with nonlinear interconnections

This paper presents a decentralised sampled-data control technique for a class of large-scale systems, which are considered to consist of linear subsystems and nonlinear interconnections. The decentralised sampled-data controller design problem is established using a closed-loop subsystem. Based on the controller design problem, the stability condition is derived for a closed-loop large-scale system, and the maximum interconnection bound is guaranteed to satisfy the stability condition. Also, its sufficient condition is formulated in terms of linear matrix inequalities. Finally, the effectiveness of the proposed technique is verified by using an example of the multi-machine power system.     

A global piecewise smooth Newton method for fast large-scale model predictive control

In this paper, the strictly convex quadratic program (QP) arising in model predictive control (MPC) for constrained linear systems is reformulated as a system of piecewise affine equations. A regularized piecewise smooth Newton method with exact line search on a convex, differentiable, piecewise-quadratic merit function is proposed for the solution of the reformulated problem. The algorithm has considerable merits when applied to MPC over standard active set or interior point algorithms. Its performance is tested and compared against state-of-the-art QP solvers on a series of benchmark problems. The proposed algorithm is orders of magnitudes faster, especially for large-scale problems and long horizons. For example, for the challenging crude distillation unit model of Pannocchia, Rawlings, and Wright (2007) with 252 states, 32 inputs, and 90 outputs, the average running time of the proposed approach is 1.57 ms.     

On the conjunction practical stability and controllability of large-scale impulsive control systems

We studied the conjunction practical stability and controllability of large-scale impulsive control systems by using the comparison systems and vector Lyapunov functions. Then the less conservative sufficient conditions fo r conjunction practical stability and controllability of large-scale impulsive control system were obtained.