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

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

流体力学杂志“Journal of Fluid Mechanics”由剑桥大学教授George Batchelor在1956年5月创办，在国际流体力学界享有很高的学术声望，被公认为是流体力学最著名的学术刊物之一，2005年的影响因子为2．061，雄居同类期刊之首．在它创刊50周年之际，2006年5月JFM出版了第554卷的纪念特刊，其中刊登了现任主编（美国西北大学S．H．Davis教授和英国剑桥大学T．J．Pedley教授）合写的述评：“Editorial：JFM at50”，以JFM为背景，从独特的视角对近50年来流体力学的发展进行了简明的回顾和展望，并归纳了一系列非常有启发性的有趣统计数字．2006年7月21日在剑桥大学应用数学和理论物理研究所（DAMTP）举行了创刊50周年的庆祝会．下午2点，JFM的新老编辑和来宾会聚一堂，Pedley教授致开幕词，其后是5个精彩的报告：The mysterious rattleback and its fluid counterpart（Keith Moffatt），Developments in shear instabilities（Patrick Huerre），Falling clouds（Elisabeth Guazzelli），Ecotectural fluid mechanics（Paul Linden），The success of JFM（Herbert Huppert），最后由Davis教授致闭幕词．

Journal of Fluid Mechanics创刊50周年

AutoDock Vina is arguably one of the fastest and most widely used open-source programs for molecular docking. However, compared to other programs in the AutoDock Suite, it lacks support for modeling specific features such as macrocycles or explicit water molecules. Here, we describe the implementation of this functionality in AutoDock Vina 1.2.0. Additionally, AutoDock Vina 1.2.0 supports the AutoDock4.2 scoring function, simultaneous docking of multiple ligands, and a batch mode for docking a large number of ligands. Furthermore, we implemented Python bindings to facilitate scripting and the development of docking workflows. This work is an effort toward the unification of the features of the AutoDock4 and AutoDock Vina programs. The source code is available at https://github.com/ccsb-scripps/AutoDock-Vina.

/pdf/autodock-vina-1-2-0-new-docking-methods-expanded-force-field-5qyon7yms3.pdf

AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings.

Molecular sieving can lead to ultrahigh selectivity and low regeneration energy because it completely excludes all larger molecules via a size restriction mechanism. However, it allows adsorption of all molecules smaller than the pore aperture and so separations of complicated mixtures can be hindered. Here, we report an intermediate-sized molecular sieving (iSMS) effect in a metal-organic framework (MAF-41) designed with restricted flexibility, which also exhibits superhydrophobicity and ultrahigh thermal/chemical stabilities. Single-component isotherms and computational simulations show adsorption of styrene but complete exclusion of the larger analogue ethylbenzene (because it exceeds the maximal aperture size) and smaller toluene/benzene molecules that have insufficient adsorption energy to open the cavity. Mixture adsorption experiments show a high styrene selectivity of 1,250 for an ethylbenzene/styrene mixture and 3,300 for an ethylbenzene/styrene/toluene/benzene mixture (orders of magnitude higher than previous reports). This produces styrene with a purity of 99.9%+ in a single adsorption-desorption cycle. Controlling/restricting flexibility is the key for iSMS and can be a promising strategy for discovering other exceptional properties.

Intermediate-sized molecular sieving of styrene from larger and smaller analogues.

This study explores the effect of learning styles and online participation on students' self-reported enjoyment levels in distributed learning environments. One hundred and sixty-nine entering students, who were enrolled in their first course totally via Internet, were chosen from masters' students in the School of Library and Information Sciences at the University of North Texas. The students were asked to complete Kolb's Learning-Style Inventory during a face-to-face training session on web-based learning. Subjects also reported their performance and enjoyment level in the course near the end of the term. Web Course Tool (WebCT) courseware automatically recorded every student's participation in terms of pages accessed, pages read, and total postings made. Multiple regression analysis found learning style significantly impacts students' enjoyment level but class participation does not. Learning styles and class participation together explain students' enjoyment level with an effect size of R2 = .125 (p < .01). Learning styles also statistically impact student participation in terms of "hits" (p = .028) and "posts" (p = .038) in this web-based course.

https://www.jstor.org/stable/pdfplus/40323899.pdf

Effects of learning styles and class participation on students enjoyment level in distributed learning environments

In this paper, we describe our experiment developing an implementation of the Linpack benchmark for TianHe-1, a petascale CPU/GPU supercomputer system, the largest GPU-accelerated system ever attempted before. An adaptive optimization framework is presented to balance the workload distribution across the GPUs and CPUs with the negligible runtime overhead, resulting in the better performance than the static or the training partitioning methods. The CPU-GPU communication overhead is effectively hidden by a software pipelining technique, which is particularly useful for large memory-bound applications. Combined with other traditional optimizations, the Linpack we optimized using the adaptive optimization framework achieved 196.7 GFLOPS on a single compute element of TianHe-1. This result is 70.1% of the peak compute capability and 3.3 times faster than the result using the vendor’s library. On the full configuration of TianHe-1 our optimizations resulted in a Linpack performance of 0.563PFLOPS, which made TianHe-1 the 5th fastest supercomputer on the Top500 list released in November 2009.

Adaptive Optimization for Petascale Heterogeneous CPU/GPU Computing

This study explores the effect of learning styles and online participation on students' self-reported enjoyment levels in distributed learning environments. One hundred and sixty-nine entering students, who were enrolled in their first course totally via Internet, were chosen from masters' students in the School of Library and Information Sciences at the University of North Texas. The students were asked to complete Kolb's Learning-Style Inventory during a face-to-face training session on web-based learning. Subjects also reported their performance and enjoyment level in the course near the end of the term. Web Course Tool (WebCT) courseware automatically recorded every student's participation in terms of pages accessed, pages read, and total postings made. Multiple regression analysis found learning style significantly impacts students' enjoyment level but class participation does not. Learning styles and class participation together explain students' enjoyment level with an effect size of R 2 = .125 (p <.01). Learning styles also statistically impact student participation in terms of hits (p =.028) and posts (p =.038) in this web-based course.

Effects of Learning Styles and Class Participation on Students' Enjoyment Level in Distributed Learning Environments

Biogenic compounds are important materials for drug discovery and chemical biology. In this work, we report a quasi-biogenic molecule generator (QBMG) to compose virtual quasi-biogenic compound libraries by means of gated recurrent unit recurrent neural networks. The library includes stereo-chemical properties, which are crucial features of natural products. QMBG can reproduce the property distribution of the underlying training set, while being able to generate realistic, novel molecules outside of the training set. Furthermore, these compounds are associated with known bioactivities. A focused compound library based on a given chemotype/scaffold can also be generated by this approach combining transfer learning technology. This approach can be used to generate virtual compound libraries for pharmaceutical lead identification and optimization.

/pdf/qbmg-quasi-biogenic-molecule-generator-with-deep-recurrent-2ufu5ju29y.pdf

Yunfei Du

Papers

Intermediate-sized molecular sieving of styrene from larger and smaller analogues.

Effects of learning styles and class participation on students enjoyment level in distributed learning environments

Adaptive Optimization for Petascale Heterogeneous CPU/GPU Computing

Effects of Learning Styles and Class Participation on Students' Enjoyment Level in Distributed Learning Environments

QBMG: quasi-biogenic molecule generator with deep recurrent neural network.