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

Matrix Factorization Techniques for Recommender Systems

Confluent Hypergeometric Functions.

To assist in the development of nuclear fusion as a viable commercial power source, preparation is underway for the fusion demonstration reactor (DEMO), which will build on the work of ITER, the international experimental fusion reactor. Like other advanced nuclear energy systems, DEMO must satisfy several goals including a high level of public and worker safety, low environmental impact, high reactor availability, a closed fuel cycle and the potential to be economically competitive. Yet there are still large scientific and technological safety gaps between the on-going ITER project and DEMO that will need to be addressed. Here we review international fusion safety research and development relevant to DEMO, following the lessons learned so far from ITER. We identify the main scientific and technological safety gaps, drawing on knowledge from the development of fission energy, in particular Generation IV (Gen-IV) fission reactors. From this survey, we discuss the corresponding implications for the design and operation of DEMO. Following on from ITER, a fusion demonstration reactor will be needed to showcase the commercial viability of fusion energy. To guide preliminary design considerations, this Review explores safety gaps that must be filled between current fission reactors, ITER and future fusion reactors.

Identification of safety gaps for fusion demonstration reactors

Cloud spot markets offer virtual machines (VMs) for a dynamic price that is much lower than the fixed price of on-demand VMs. In exchange, spot VMs expose applications to multiple forms of risk, including price risk, or the risk that a VM's price will increase relative to others. Since spot prices vary continuously across hundreds of different types of VMs, flexible applications can mitigate price risk by moving to the VM that currently offers the lowest cost. To enable this flexibility, we present HotSpot, a resource container that "hops" VMs---by dynamically selecting and self-migrating to new VMs---as spot prices change. HotSpot containers define a migration policy that lowers cost by determining when to hop VMs based on the transaction costs (from vacating a VM early and briefly double paying for it) and benefits (the expected cost savings). As a side effect of migrating to minimize cost, HotSpot is also able to reduce the number of revocations without degrading performance. HotSpot is simple and transparent: since it operates at the systems-level on each host VM, users need only run an HotSpot-enabled VM image to use it. We implement a HotSpot prototype on EC2, and evaluate it using job traces from a production Google cluster. We then compare HotSpot to using on-demand VMs and spot VMs (with and without fault-tolerance) in EC2, and show that it is able to lower cost and reduce the number of revocations without degrading performance.

HotSpot: automated server hopping in cloud spot markets

Datacenters are under-utilized, primarily due to unused resources on over-provisioned nodes of latency-critical jobs. Such idle resources can be used to run batch data analytic jobs to increase datacenter utilization, but these transient resources must be evicted whenever latency-critical jobs require them again. Resource evictions often lead to cascading recomputations, which is usually handled by checkpointing intermediate results on stable storages of eviction-free reserved resources. However, checkpointing has major shortcomings in its substantial overhead of transferring data back and forth. In this work, we step away from such approaches and focus on observing the job structure and the relationships between computations of the job. We carefully mark the computations that are most likely to cause a large number of recomputations upon evictions, to run them reliably using reserved resources. This lets us retain corresponding intermediate results effortlessly without any additional checkpointing. We design Pado, a general data processing engine, which carries out our idea with several optimizations that minimize the number of additional reserved nodes. Evaluation results show that Pado outperforms Spark 2.0.0 by up to 5.1×, and checkpoint-enabled Spark by up to 3.8×.

Pado: A Data Processing Engine for Harnessing Transient Resources in Datacenters

Enhanced self-healing performance of graphene oxide/vitrimer nanocomposites: A molecular dynamics simulations study

In this paper, we study the valuation of Exchange option with credit risk. Since the over-the-counter (OTC) markets have grown rapidly in size, the counterparty default risk is very important and should be considered for the valuation of options. For modeling of credit risk, we use the structural model of Klein [13]. We derive the closed-form pricing formula for the price of the Exchange option with credit risk via the Mellin transform and provide the experiment results to illustrate the important properties of option with numerical graphs.

Closed-form pricing formula for exchange option with credit risk

Recently, weighted nuclear norm minimization (WNNM), which regularizes singular values of an input matrix with different strengths according to given weights, has demonstrated impressive results in low-level vision tasks such as additive Gaussian noise removal, deblurring and image inpainting [14, 15, 33]. In this study, we apply WNNM to remove additive Cauchy noise in images. A variational model is adopted based on maximum a posteriori estimate, which contains a data fidelity term that is appropriate for noise following the Cauchy distribution. Weighted nuclear norm is used as a regularizer in the proposed algorithm, and we utilized similar patches in the image by nonlocal similarity. We adopted the nonconvex alternating direction method of multiplier to solve the problem iteratively. Numerical experiments are presented to demonstrate the superior denoising performance of our algorithm compared with other existing methods in terms of quantitative measure and visual quality.

Cauchy Noise Removal by Weighted Nuclear Norm Minimization

Pepper (Capsicum spp.) fruit-related traits are critical determinants of quality. These traits are controlled by quantitatively inherited genes for which marker-assisted selection (MAS) has proven insufficiently effective. Here, we evaluated the potential of genomic selection, in which genotype and phenotype data for a training population are used to predict phenotypes of a test population with only genotype data, for predicting fruit-related traits in pepper. We measured five fruit traits (fruit length, fruit shape, fruit width, fruit weight, and pericarp thickness) in 351 accessions from the pepper core collection, including 229 Capsicum annuum, 48 Capsicum baccatum, 48 Capsicum chinense, 25 Capsicum frutescens, and 1 Capsicum chacoense in 4 years at two different locations and genotyped these accessions using genotyping-by-sequencing. Among the whole core collection, considering its genetic distance and sexual incompatibility, we only included 302 C. annum complex (229 C. annuum, 48 C. chinense, and 25 C. frutescens) into further analysis. We used phenotypic and genotypic data to investigate genomic prediction models, marker density, and effects of population structure. Among 10 genomic prediction methods tested, Reproducing Kernel Hilbert Space (RKHS) produced the highest prediction accuracies (measured as correlation between predicted values and observed values) across the traits, with accuracies of 0.75, 0.73, 0.84, 0.83, and 0.82 for fruit length, fruit shape, fruit width, fruit weight, and pericarp thickness, respectively. Overall, prediction accuracies were positively correlated with the number of markers for fruit traits. We tested our genomic selection models in a separate population of recombinant inbred lines derived from two parental lines from the core collection. Despite the large difference in genetic diversity between the training population and the test population, we obtained moderate prediction accuracies of 0.32, 0.34, 0.50, and 0.48 for fruit length, fruit shape, fruit width, and fruit weight, respectively. This use of genomic selection for fruit-related traits demonstrates the potential use of core collections and genomic selection as tools for crop improvement.

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Geon-Woo Kim

Papers

Pado: A Data Processing Engine for Harnessing Transient Resources in Datacenters

Enhanced self-healing performance of graphene oxide/vitrimer nanocomposites: A molecular dynamics simulations study

Closed-form pricing formula for exchange option with credit risk

Cauchy Noise Removal by Weighted Nuclear Norm Minimization

Genomic Selection for Prediction of Fruit-Related Traits in Pepper (Capsicum spp.).