Background: Despite marked recent improvements in long-read sequencing technology, the assembly of diploid genomes remains a difficult task. A major obstacle is distinguishing between alternative contigs that represent highly heterozygous regions. If primary and secondary contigs are not properly identified, the primary assembly will overrepresent both the size and complexity of the genome, which complicates downstream analysis such as scaffolding.Results: Here we illustrate a new method, which we call HapSolo, that identifies secondary contigs and defines a primary assembly based on multiple pairwise contig alignment metrics. HapSolo evaluates candidate primary assemblies using BUSCO scores and then distinguishes among candidate assemblies using a cost function. The cost function can be defined by the user but by default considers the number of missing, duplicated and single BUSCO genes within the assembly. HapSolo performs hill climbing to minimize cost over thousands of candidate assemblies. We illustrate the performance of HapSolo on genome data from three species: the Chardonnay grape (Vitis vinifera), with a genome of 490 Mb, a mosquito (Anopheles funestus; 200 Mb) and the Thorny Skate (Amblyraja radiata; 2650 Mb). Conclusions:HapSolo rapidly identified candidate assemblies that yield improvements in assembly metrics, including decreased genome size and improved N50 scores. Contig N50 scores improved by 35%, 9% and 9% for Chardonnay, mosquito and the thorny skate, respectively, relative to unreduced primary assemblies. The benefits of HapSolo were amplified by down-stream analyses, which we illustrated by scaffolding with Hi-C data. We found, for example, that prior to the application of HapSolo, only 52% of the Chardonnay genome was captured in the largest 19 scaffolds, corresponding to the number of chromosomes. After the application of HapSolo, this value increased to ~ 84%. The improvements for the mosquito's largest three scaffolds, representing the number of chromosomes, were from 61 to 86%, and the improvement was even more pronounced for thorny skate. We compared the scaffolding results to assemblies that were based on PurgeDups for identifying secondary contigs, with generally superior results for HapSolo.

We report on a new 3D numerical code designed to solve the Einstein equations for general vacuum spacetimes. This code is based on the standard 3+1 approach using Cartesian coordinates. We discuss the numerical techniques used in developing this code, and its performance on massively parallel and vector supercomputers. As a test case, we present evolutions for the first 3D black hole spacetimes. We identify a number of difficulties in evolving 3D black holes and suggest approaches to overcome them. We show how special treatment of the conformal factor can lead to more accurate evolution, and discuss techniques we developed to handle black hole spacetimes in the absence of symmetries. Many different slicing conditions are tested, including geodesic, maximal, and various algebraic conditions on the lapse. With current resolutions, limited by computer memory sizes, we show that with certain lapse conditions we can evolve the black hole to about t = 50M , where M is the black hole mass. Comparisons are made with results obtained by evolving spherical initial black hole data sets with a 1D spherically symmetric code. We also demonstrate that an "apparent horizon locking shift" can be used to prevent the development of large gradients in the metric functions that result from singularity avoiding time slicings. We compute the mass of the apparent horizon in these spacetimes, and find that in many cases it can be conserved to within about 5% throughout the evolution with our techniques and current 1 resolution.

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Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics.

The XDMoD auditing tool provides, for the first time, a comprehensive tool to measure both utilization and performance of high-end cyberinfrastructure (CI), with initial focus on XSEDE. Here, we demonstrate, through several case studies, its utility for providing important metrics regarding resource utilization and performance of TeraGrid/XSEDE that can be used for detailed analysis and planning as well as improving operational efficiency and performance. Measuring the utilization of high-end cyberinfrastructure such as XSEDE helps provide a detailed understanding of how a given CI resource is being utilized and can lead to improved performance of the resource in terms of job throughput or any number of desired job characteristics. In the case studies considered here, a detailed historical analysis of XSEDE usage data using XDMoD clearly demonstrates the tremendous growth in the number of users, overall usage, and scale of the simulations routinely carried out. Not surprisingly, physics, chemistry, and the engineering disciplines are shown to be heavy users of the resources. However, as the data clearly show, molecular biosciences are now a significant and growing user of XSEDE resources, accounting for more than 20 percent of all SUs consumed in 2012. XDMoD shows that the resources required by the various scientific disciplines are very different. Physics, Astronomical sciences, and Atmospheric sciences tend to solve large problems requiring many cores. Molecular biosciences applications on the other hand, require many cycles but do not employ core counts that are as large. Such distinctions are important in guiding future cyberinfrastructure design decisions. XDMoD's implementation of a novel application kernel-based auditing system to measure overall CI system performance and quality of service is shown, through several examples, to provide a useful means to automatically detect under performing hardware and software. This capability is especially critical given the complex composition of today's advanced CI. Examples include an application kernel based on a widely used quantum chemistry program that uncovered a software bug in the I/O stack of a commercial parallel file system, which was subsequently fixed by the vendor in the form of a software patch that is now part of their standard release. This error, which resulted in dramatically increased execution times as well as outright job failure, would likely have gone unnoticed for sometime and was only uncovered as a result of implementation of XDMoD's suite of application kernels.

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Abstract:The TeraGrid is an advanced, integrated, nationally-distributed, open, user-driven, US cyberinfrastructure that enables and supports leading edge scientific discovery and promotes science and technology education. It comprises supercomputing resources, storage systems, visualization resources, data collections, software, and science gateways, integrated by software systems and high bandwidth networks, coordinated through common policies and operations, and supported by technology experts. This paper discusses the TeraGrid itself, examples of the science that is occurring on the TeraGrid today, and applications that are being developed to perform science in the future. Key words: computational science applications, high performance computing, grid computing, production grid infrastructure *Steven Manos was a Research Fellow at University College London while this work was conducted CMST SI(1) 81-97 (2010 82effort to build and deploy the world's largest, fastest, most comprehensive, distributed infrastructure for general scientific research. The initial TeraGrid was homogeneous and very "griddy", with users foreseen to be running on multiple systems, both because their codes could run "anywhere", and because in some cases, multiple systems would be needed to support the large runs that were desired. The software that made up the TeraGrid was a set of identical packages on all systems. The TeraGrid has since expanded in capability and number of resource providers. This introduced heterogeneity and thus added complexity to the grid ideals of the initial DTF, as the common software no longer could be completely identical. This led to the concept of common interfaces, with potentially different software underneath the interfaces. Additionally, the users of national center supercomputers were merged into TeraGrid, which led to TeraGrid increasing its focus on supporting these users and their traditional parallel/batch usage modes. The TeraGrid is freely available for US researchers, with allocations determined by a peer-review mechanism. The TeraGrid resources are generally at least 50% dedicated to TeraGrid. Roughly similar to the TeraGrid in terms of a continental-scale set of resources with a focus on HPC users is DEISA/PRACE in Europe, which is a set of national resources with some fractions provided to a set of European users, again determined through a review process. An alternative type of infrastructure is one that is more focused on high throughput computing (HTC), initially in high-energy physics, but increasingly in other domains as well. Filling this role in the US is Open Science Grid (OSG), and in Europe, Enabling Grids for E-sciencE (EGEE), which has recently been transformed into the European Grid Infrastructure (EGI). This paper discusses the TeraGrid itself (Section II), examples of the interesting current uses of the TeraGrid cyberinfrastructure for science (Section III), and examples of applications that are being developed to perform science on the TeraGrid in the future (Section IV) 1 . ...