Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Course Description In this course, we will explore the question of the actual and potential connections between democracy and education. Our focus of attention will be placed on a critical examination of democratic theory and its implications for the civic education roles and contributions of teachers, adult educators, community development practitioners, and community organizers. We will survey and deal critically with a range of competing conceptions of democracy, variously described as classical, republican, liberal, radical, marxist, neomarxist, pragmatist, feminist, populist, pluralist, postmodern, and/or participatory. Using narrative inquiry as a means for illuminating and interpreting contemporary practice, we will analyze the implications of different conceptions of democracy for the practical work of civic education.

Democracy and education.

物件導向軟體之架構(Object-Oriented Software Construction)探討

語用論(Pragmatics)を考える

Blocks-based programming has become the lingua franca for introductory coding. Studies have found that experience with blocks-based programming can help beginners learn more traditional text-based languages. We explore how blocks environments improve learnability for novices by 1) favoring recognition over recall, 2) reducing cognitive load, and 3) preventing errors. Increased usability of blocks programming has led to widespread adoption within introductory programming contexts across a range of ages. Ongoing work explores further reducing barriers to programming, supporting novice programmers in expanding their programming skills, and transitioning to textual programming. New blocks frameworks are making it easier to access a variety of APIs through blocks environments, opening the doors to a greater diversity of programming domains and supporting greater experimentation for novices and professionals alike.

Learnable Programming: Blocks and Beyond

New blocks frameworks open doors to greater experimentation for novices and professionals alike

Learnable programming: blocks and beyond

Model differentiation techniques, which provide the capability to identify mappings and differences between models, are essential to many model development and management practices. There has been initial research toward model differentiation applied to Unified Modeling Language (UML) diagrams, but differentiation of domain-specific models has not been explored deeply in the modeling community. Traditional modeling practice using the UML relies on a single fixed general-purpose language (i.e., all UML diagrams conform to a single metamodel). In contrast, Domain-Specific Modeling (DSM) is an emerging model-driven paradigm in which multiple metamodels are used to define various modeling languages that represent the key concepts and abstractions for particular domains. Therefore, domain-specific models may conform to various metamodels, which requires model differentiation algorithms be metamodel-independent and able to apply to multiple domain-specific modeling languages. This paper presents metamodel-independent algorithms and associated tools for detecting mappings and differences between domain-specific models, with facilities for graphical visualization of the detected differences.

http://disi.unitn.it/~p2p/RelatedWork/Matching/ejis-2007.pdf

DSMDiff: a differentiation tool for domain-specific models

An Aspect-Oriented (AO) approach can be beneficial at different stages of the software lifecycle and at various levels of abstraction. Whenever the description of a software artifact exhibits crosscutting structure, the principles of modularity espoused by AO offer a powerful technology for supporting separation of concerns. We have found this to be true especially in the area of domain-specific modeling [3].

/pdf/handling-crosscutting-constraints-in-domain-specific-42tl9yxoxs.pdf

Handling crosscutting constraints in domain-specific modeling

The Generic Modeling Environment (GME) is a configurable tool suite that facilitates the rapid creation of domain-specific model-integrated program synthesis environments. There are three characteristics of the GME that make it a valuable tool for the construction of domain-specific modeling environments. First, the GME provides generic modeling primitives that assist an environment designer in the specification of new graphical modeling environments. Second, these generic primitives are specialized to create the domain-specific modeling concepts through meta-modeling. The meta-models explicitly support composition enabling the creation of composite modeling languages supporting multiple paradigms. Third, several ideas from prototype-based programming languages have been integrated with the inherent model containment hierarchy, which gives the domain expert the ability to clone graphical models. This paper explores the details of these three ideas and their implications.

/pdf/composition-and-cloning-in-modeling-and-meta-modeling-scgq1lyueo.pdf

Jeff Gray

Papers

Learnable Programming: Blocks and Beyond

Learnable programming: blocks and beyond

DSMDiff: a differentiation tool for domain-specific models

Handling crosscutting constraints in domain-specific modeling

Composition and cloning in modeling and meta-modeling