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

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Flow The Psychology Of Optimal Experience

Traditional engineering instruction is deductive, beginning with theories and progressing to the applications of those theories Alternative teaching approaches are more inductive Topics are introduced by presenting specific observations, case studies or problems, and theories are taught or the students are helped to discover them only after the need to know them has been established This study reviews several of the most commonly used inductive teaching methods, including inquiry learning, problem-based learning, project-based learning, case-based teaching, discovery learning, and just-in-time teaching The paper defines each method, highlights commonalities and specific differences, and reviews research on the effectiveness of the methods While the strength of the evidence varies from one method to another, inductive methods are consistently found to be at least equal to, and in general more effective than, traditional deductive methods for achieving a broad range of learning outcomes

Inductive Teaching and Learning Methods: Definitions, Comparisons, and Research Bases

I. INTRODUCTION TO EVALUATION. 1. Evaluation's Basic Purpose, Uses, and Conceptual Distinctions. 2. Origins of Modern Program Evaluation. 3. Recent Developments and Trends in Evaluation. II. ALTERNATIVE APPROACHES TO PROGRAM EVALUATION. 4. Alternative Views of Evaluation. 5. Objectives-Oriented Evaluation Approaches. 6. Management-Oriented Evaluation Approaches. 7. Consumer-Oriented Evaluation Approaches. 8. Expertise-Oriented Evaluation Approaches. 9. Adversary-Oriented Evaluation Approaches. 10. Participant-Oriented Evaluation Approaches. 11. Alternative Evaluation Approaches: A Summary and Comparative Analysis. III. PRACTICAL GUIDELINES FOR PLANNING EVALUATION. 12. Clarifying the Evaluation Request and Responsibilities. 13. Setting Boundaries and Analyzing the Evaluation Context. 14. Identifying and Selecting the Evaluative Questions and Criteria. 15. Planning How to Conduct the Evaluation. IV. PRACTICAL GUIDELINES FOR CONDUCTING AND USING EVALUATIONS. 16. Dealing with Political, Ethical, and Interpersonal Aspects of Evaluation. 17. Collecting, Analyzing, and Interpreting Quantitative Information. 18. Collecting, Analyzing, and Interpreting Qualitative Information. 19. Reporting and Using Evaluation Information. 20. Evaluating Evaluations. V. EMERGING AND FUTURE SETTINGS FOR PROGRAM EVALUATION. 21. Conducting Multiple-Site Evaluation Studies. 22. Conducting Evaluations of Organizations Renewal and Training in Corporate and Nonprofit Settings. 23. The Future of Evaluation. Appendix: Some General Areas of Competence Important in Education Evaluation.

http://platform.almanhal.com/Files/?ID=T2-41793-MLA0011322.pdf

تقويم البرنامج : طرق بديلة و إرشادات عملية = Program evaluation Alternative Approaches and Practical Guidelines

Big data may contain big values, but also brings lots of challenges to the computing theory, architecture, framework, knowledge discovery algorithms, and domain specific tools and applications. Beyond the 4-V or 5-V characters of big datasets, the data processing shows the features like inexact, incremental, and inductive manner. This brings new research opportunities to research community across theory, systems, algorithms, and applications. Is there some new "theory" for the big data? How to handle the data computing algorithms in an operatable manner? This report shares some view on new challenges identified, and covers some of the application scenarios such as micro-blog data analysis and data processing in building next generation search engines.

/pdf/scaling-distributed-machine-learning-with-the-parameter-2g1ec41azg.pdf

Scaling Distributed Machine Learning with the Parameter Server

This paper describes the automatically tuned linear algebra software (ATLAS) project, as well as the fundamental principles that underly it. ATLAS is an instantiation of a new paradigm in high performance library production and maintenance, which we term automated empirical optimization of software (AEOS); this style of library management has been created in order to allow software to keep pace with the incredible rate of hardware advancement inherent in Moore's Law. ATLAS is the application of this new paradigm to linear algebra software, with the present emphasis on the basic linear algebra subprograms (BLAS), a widely used, performance-critical, linear algebra kernel library.

/pdf/new-trends-in-high-performance-computing-1f0278f9m2.pdf

New trends in high performance computing

The multidisciplinary nature of computational science and engineering (CSE) and its rela- tion to other disciplines is described. The stages through which CSE education is evolving, from initial recognition in the 1980s to present growth, are discussed. The challenges and benefits of different approaches to CSE education are discussed, as is the emergence of a set of core elements common to different approaches. The content of courses, curricula, and degrees offered in CSE are reviewed, and a survey is made of all undergraduate de- gree programs. The curricula of different programs are examined for the common "tool set" they define and analyzed for their relative weighting of computing, application, and mathematics. A trend toward a standard curriculum is noted.

https://epubs.siam.org/doi/pdf/10.1137/S0036144502408075

Elements of Computational Science and Engineering Education

Teaching with technology still remains as a challenge. Making judicious choices of when, what and how specific tools and pedagogies to use in the teaching of a topic can be improved with the help of curriculum inventories, training, and practices but as new and more capable technologies arrive, such resources and experience do not often transfer to new circumstances. This article presents a case study in which computational modeling and simulation technology (CMST) is used to improve technological pedagogical content knowledge (TPACK) of teachers. We report findings of a summer training program for both preservice and in-service teachers in the Northeastern United States. CMST has shown to be effective on both teaching and learning. Results show that it helps teachers to integrate technology into their teaching in a more permanent, constructive, and tool-independent way. It has also shown to improve student learning in a constructive fashion by first enabling deductive introduction of a topic from a general simplistic framework and then guiding the learner to inductively discover underlying STEM principles through experimentation.

Computational pedagogy: fostering a new method of teaching

Addressing its cognitive essence, universal value, and curricular practices.

Osman Yasar

Papers

New trends in high performance computing

Elements of Computational Science and Engineering Education

Computational pedagogy: fostering a new method of teaching

A new perspective on computational thinking

A new perspective on computational science education