批加工设备调度是半导体生产线调度的重要组成部分,对半导体生产线性能有重要影响。在综述批加工设备调度研究成果的基础上,提出了同时考虑即将到来工件与下游设备负载情况的半导体生产线批加工设备调度规则(Scheduling Rule for Batch ...批加工设备调度是半导体生产线调度的重要组成部分,对半导体生产线性能有重要影响。在综述批加工设备调度研究成果的基础上,提出了同时考虑即将到来工件与下游设备负载情况的半导体生产线批加工设备调度规则(Scheduling Rule for Batch Processing Machines in Semiconductor Wafer Fabrication Facilities,SRB)。基于三种不同规模的半导体生产线模型,在非批加工设备使用不同的调度规则的情况下,对提出的SRB进行了仿真验证。仿真结果表明,与目前常用的固定加工批量调度规则相比,SRB能够更好的改善半导体生产线性能,获得较高的工件移动步数、产量和准时交货率,以及较低的加工周期。展开更多
In distributed training,increasing batch size can improve parallelism,but it can also bring many difficulties to the training process and cause training errors.In this work,we investigate the occurrence of training er...In distributed training,increasing batch size can improve parallelism,but it can also bring many difficulties to the training process and cause training errors.In this work,we investigate the occurrence of training errors in theory and train ResNet-50 on CIFAR-10 by using Stochastic Gradient Descent(SGD) and Adaptive moment estimation(Adam) while keeping the total batch size in the parameter server constant and lowering the batch size on each Graphics Processing Unit(GPU).A new method that considers momentum to eliminate training errors in distributed training is proposed.We define a Momentum-like Factor(MF) to represent the influence of former gradients on parameter updates in each iteration.Then,we modify the MF values and conduct experiments to explore how different MF values influence the training performance based on SGD,Adam,and Nesterov accelerated gradient.Experimental results reveal that increasing MFs is a reliable method for reducing training errors in distributed training.The analysis of convergent conditions in distributed training with consideration of a large batch size and multiple GPUs is presented in this paper.展开更多
Citric acid is an important organic substance whose marketing concerns various fields. Nevertheless, until 1997 the scientific literature reported little information about the process of crystallization by cooling thr...Citric acid is an important organic substance whose marketing concerns various fields. Nevertheless, until 1997 the scientific literature reported little information about the process of crystallization by cooling through which the commercial product is obtained. In particular, the available studies were aimed to investigate only the kinetics of nucleation and crystal growth neglecting some effective aspects of the industrial crystallization in mechanically stirred apparatus. In order to fill that sci-tech gap, the Department of Chemical Engineering at the University ＂La Sapienza＂ of Rome decided to lead a long and meticulous experimental research on the crystallization in discontinuous （batch） of CAM （citric acid monohydrate） in the allotropic form that is stable at room temperature. Due to the number of people involved in that pioneering work, carried out in the historic laboratories of＂La Sapienza＂ （Faculty of Engineering）, and motivated by the publication of related M.Sc. dissertations and research papers, such collective effort was called ＂School of Industrial Crystallization＂. Among the graduate students in Chemical Engineering that 17 years ago participated in that fruitful experience there was also the author who, under the supervision of Prof. Barbara Mazzarotta, had the specific task of assessing the effects on CAM of changing the crystallization operating conditions until their optimization; the achievements are briefly illustrated in this paper.展开更多
基金partially supported by the Major State Research Development Program (No. 2016YFB0201305)the National Key R&D Program of China (No.2018YFB2101100)the National Natural Science Foundation of China (Nos. 61806216, 61702533,61932001, and 61872376)。
文摘In distributed training,increasing batch size can improve parallelism,but it can also bring many difficulties to the training process and cause training errors.In this work,we investigate the occurrence of training errors in theory and train ResNet-50 on CIFAR-10 by using Stochastic Gradient Descent(SGD) and Adaptive moment estimation(Adam) while keeping the total batch size in the parameter server constant and lowering the batch size on each Graphics Processing Unit(GPU).A new method that considers momentum to eliminate training errors in distributed training is proposed.We define a Momentum-like Factor(MF) to represent the influence of former gradients on parameter updates in each iteration.Then,we modify the MF values and conduct experiments to explore how different MF values influence the training performance based on SGD,Adam,and Nesterov accelerated gradient.Experimental results reveal that increasing MFs is a reliable method for reducing training errors in distributed training.The analysis of convergent conditions in distributed training with consideration of a large batch size and multiple GPUs is presented in this paper.
文摘Citric acid is an important organic substance whose marketing concerns various fields. Nevertheless, until 1997 the scientific literature reported little information about the process of crystallization by cooling through which the commercial product is obtained. In particular, the available studies were aimed to investigate only the kinetics of nucleation and crystal growth neglecting some effective aspects of the industrial crystallization in mechanically stirred apparatus. In order to fill that sci-tech gap, the Department of Chemical Engineering at the University ＂La Sapienza＂ of Rome decided to lead a long and meticulous experimental research on the crystallization in discontinuous （batch） of CAM （citric acid monohydrate） in the allotropic form that is stable at room temperature. Due to the number of people involved in that pioneering work, carried out in the historic laboratories of＂La Sapienza＂ （Faculty of Engineering）, and motivated by the publication of related M.Sc. dissertations and research papers, such collective effort was called ＂School of Industrial Crystallization＂. Among the graduate students in Chemical Engineering that 17 years ago participated in that fruitful experience there was also the author who, under the supervision of Prof. Barbara Mazzarotta, had the specific task of assessing the effects on CAM of changing the crystallization operating conditions until their optimization; the achievements are briefly illustrated in this paper.
