Multicore processors are now the standard CPU architecture, and parallel and distributed computing (PDC) is in the CS2013 core curriculum [9]. It is now the task of CS educators to begin creating pedagogical materials that will help their students understand PDC concepts. In this paper, we present TSGL (the thread-safe graphics library), a C++11 library that safely supports multithreaded graphics. We also present several multithreaded visualizations that illustrate how TSGL can be used to visualize the Parallel Loop design pattern, and present evidence that TSGL can improve student mastery of this parallel abstraction.

It is well known that active learning strategies, like Peer Instruction and Cooperative Learning promote deep learning of material in the classroom. These techniques are typically accomplished by having students use cell phones or laptops. However, research has also shown that use by students of these technologies detract from learning. In this poster we examine our low-tech solution: the use of inexpensive, handheld whiteboards in our classrooms. We provide examples of activities which make use of the boards, in contexts ranging from introductory programming to upper-level networking.

UNLABELLED: PhamDB is a web application which creates databases of bacteriophage genes, grouped by gene similarity. It is backwards compatible with the existing Phamerator desktop software while providing an improved database creation workflow. Key features include a graphical user interface, validation of uploaded GenBank files, and abilities to import phages from existing databases, modify existing databases and queue multiple jobs. AVAILABILITY AND IMPLEMENTATION: Source code and installation instructions for Linux, Windows and Mac OSX are freely available at https://github.com/jglamine/phage PhamDB is also distributed as a docker image which can be managed via Kitematic. This docker image contains the application and all third party software dependencies as a pre-configured system, and is freely available via the installation instructions provided. CONTACT: [email protected].

MOTIVATION: While phylogenetic analyses of datasets containing 1000-5000 sequences are challenging for existing methods, the estimation of substantially larger phylogenies poses a problem of much greater complexity and scale. METHODS: We present DACTAL, a method for phylogeny estimation that produces trees from unaligned sequence datasets without ever needing to estimate an alignment on the entire dataset. DACTAL combines iteration with a novel divide-and-conquer approach, so that each iteration begins with a tree produced in the prior iteration, decomposes the taxon set into overlapping subsets, estimates trees on each subset, and then combines the smaller trees into a tree on the full taxon set using a new supertree method. We prove that DACTAL is guaranteed to produce the true tree under certain conditions. We compare DACTAL to SATé and maximum likelihood trees on estimated alignments using simulated and real datasets with 1000-27 643 taxa. RESULTS: Our studies show that on average DACTAL yields more accurate trees than the two-phase methods we studied on very large datasets that are difficult to align, and has approximately the same accuracy on the easier datasets. The comparison to SATé shows that both have the same accuracy, but that DACTAL achieves this accuracy in a fraction of the time. Furthermore, DACTAL can analyze larger datasets than SATé, including a dataset with almost 28 000 sequences. AVAILABILITY: DACTAL source code and results of dataset analyses are available at www.cs.utexas.edu/users/phylo/software/dactal.

The current technique for estimating phylogenies from sequence data uses two phases: first, the sequences are aligned, and then the tree is estimated using the obtained alignment. More recently, however, several computational methods have been developed for simultaneous estimation of the alignment and the tree, of which POY (a heuristic for the NP-hard "minimum treelength" problem, which extends maximum parsimony (MP) so that gaps contribute to the cost) is the most popular. In a 2007 paper published in Systematic Biology, Ogden and Rosenberg reported on a simulation study in which they compared POY to the very simple two-phase method of estimating the alignment using ClustalW and then analyzing the resultant alignment using MP. They found that in the overwhelming majority of the cases, ClustalW + MP outperformed POY with respect to alignment and phylogenetic tree accuracy, and they concluded that simultaneous estimation techniques (collectively referred to as "Direct Optimization") are not competitive with two-phase techniques. Our paper presents a simulation study in which we take a closer look at the points raised by Ogden and Rosenberg. Instead of focusing specifically on POY, we focus on the NP-hard optimization problem that POY addresses: minimizing treelength. Since this optimization depends upon the specific edit distance criterion used to score a tree, our study considers the impact of the gap penalty (in particular, affine versus simple) on the accuracy of the resultant alignment and tree that optimizes the treelength for that gap penalty function. Our study suggests that the poor performance observed for POY by Ogden and Rosenberg is due to the simple gap penalties they used to score alignment/tree pairs, but also suggests the intriguing possibility that optimizing under an affine gap penalty might produce alignments that are not only better than ClustalW alignments, but competitive with (or perhaps better than) those produced by the best current alignment methods. This study also shows that optimizing under this affine gap penalty produces trees whose topological accuracy is better than ClustalW + MP, and competitive with the current best two-phase methods.