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Understanding the mechanisms that generate shared morphologies across closely related taxa is important when identifying distinct evolutionary lineages using morphological characters. Desmognathus salamanders are an ideal group for testing hypotheses concerning the correlation between morphological similarity and genetic exchange within and among nominal species due to a pattern of high discordance between the two. Phylogeographic hypotheses are tested for populations of the D. quadramaculatus species complex throughout southern Appalachia by combining phylogenetic and population genetic methods with geographical information. Phylogenetic and phylogeographic inferences are then assessed in conjunction with morphological characteristics that have traditionally diagnosed taxonomic entities to understand the genetic basis of shared morphology in this complex, and to assess species boundaries. A history of fragmentation followed by range expansion is suggested as a recurrent pattern that has shaped the current population structure within this complex. The current taxonomy is found to unite populations that share similar morphologies due to parallel evolution rather than ancestry. We suggest revisions in taxonomy that will better reflect the evolutionary history of these lineages. Appreciation of the hidden genetic variation and homoplasious morphological variation often present in and among salamander species can foster the implementation of more appropriate methods for detecting and recognizing the complex history of these organisms.

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Deep learning models' prediction accuracy tends to improve with the size of the model. The implications being that the amount of computational power needed to train models is continuously increasing. Distributed deep learning training tries to address this issue by spreading the computational load onto several devices. In theory, distributing computation onto N devices should give a performance improvement of xN. Yet, in reality the performance improvement is rarely xN, due to communication and other overheads. This thesis will study the communication overhead incurred when distributing deep learning training. Hopsworks is a platform designed for data science. The purpose of this work is to explore a feasible way of deploying distributed deep learning training on a shared cluster and analyzing the performance of different distributed deep learning algorithms to be used on this platform. The findings of this study show that bandwidth-optimal communication algorithms like ring all-reduce scales better than many-to-one communication algorithms like parameter server, but were less fault tolerant. Furthermore, system usage statistics collected revealed a network bottleneck when training is distributed on multiple machines. This work also shows that it is possible to run MPI on a hadoop cluster by building a prototype that orchestrates resource allocation, deployment, and monitoring of MPI based training jobs. Even though the experiments did not cover different cluster configurations, the results are still relevant in showing what considerations need to be made when distributing deep learning training. / Träffsäkerheten hos djupinlärningsmodeller tenderar att förbättras i relation med storleken på modellen. Implikationen blir att mängden beräkningskraft som krävs för att träna modeller ökar kontinuerligt.Distribuerad djupinlärning försöker lösa detta problem genom att distribuera beräkningsbelastning på flera enheter. Att distribuera beräkningarna på N enheter skulle i teorin innebär en linjär skalbarhet (xN). I verkligenheten stämmer sällan detta på grund av overhead från nätverkskommunikation eller I/O. Hopsworks är en dataanalys och maskininlärningsplattform. Syftetmed detta arbeta är att utforska ett möjligt sätt att utföra distribueraddjupinlärningträning på ett delat datorkluster, samt analysera prestandan hos olika algoritmer för distribuerad djupinlärning att använda i plattformen. Resultaten i denna studie visar att nätverksoptimala algoritmer såsom ring all-reduce skalar bättre för distribuerad djupinlärning änmånga-till-en kommunikationsalgoritmer såsom parameter server, men är inte lika feltoleranta. Insamlad data från experimenten visade på en flaskhals i nätverket vid träning på flera maskiner. Detta arbete visar även att det är möjligt att exekvera MPI program på ett hadoopkluster genom att bygga en prototyp som orkestrerar resursallokering, distribution och övervakning av exekvering. Trots att experimenten inte täcker olika klusterkonfigurationer så visar resultaten på vilka faktorer som bör tas hänsyn till vid distribuerad träning av djupinlärningsmodeller.

Simulating light is a very computationally expensive proposition. There are a wide variety of global illumination algorithms that are implemented and used by major motion picture companies to render interesting and believable scenes. Every algorithm strives to find a balance between speed and accuracy. The Point Based Approximate Color Bleeding algorithm is one of the most widely used algorithms in the field today. The Point Based Approximate Color Bleeding(PBACB) global illumination algorithm is based on the central idea that the geometry and direct illumination of the scene can be approximated by using a point cloud representation. This point cloud representation can then be used to generate the indirect illumination. The most basic unit of the point cloud is a surfel. A surfel is a two dimensional circle in space that contains the direct illumination for that section of space. The surfels are gathered in a tree structure and approximations are generated for the different levels of the tree. This tree is then used to calculate the appropriate color bleeding effect to apply to the surfaces in a rendered image. The main goal of this project was to explore the possibility of applying CUDA to the PBACB global illumination algorithm. CUDA is an extension of the C/C++ programing languages which allows for GPU parallel programming. In this paper, we present our GPU based implementation of the PBACB algorithm. The PBACB algorithm involves three central steps, creation of a surfel point cloud, generation of the spherical harmonics approximations for the point cloud, and using the surfel point cloud to generate an approximation for global illumi- nation. For this project, CUDA was applied to two of the steps of the PBACB algorithm, the generation of the spherical harmonic representations and the ap- plication of the surfel point cloud to generate indirect illumination. Our final GPU algorithm was able to obtain a 4.0 times speedup over our CPU version. We also discuss future work which could include the use of CUDA’s Dynamic Parallelism and a stack free implementation which could increase the speedups seen by our algorithm.

Datorer med flera kärnor som delar på ett gemensamt minne (SMP) har blivit normen sedan Moore's lag har slutat gälla. För att utnyttja den prestanda flera kärnor erbjuder så behöver mjukvaruingenjören skriva programmen så att de explicit utnyttjar flera kärnor. För mindre projekt är det lätt att detta bortses från vilket skapar program som endast utnyttjar en kärna. Detta gör att det i sådana fall finns stora vinningar genom att parallellisera koden. Det här examensarbetet har förbättrat prestandan på ett beräkningstungt simuleringsprogram, skrivit att utnyttja endast en kärna, genom att hitta områden i koden som är lämpliga att parallellisera. Dessa områden har identifierats med Intel's Vtune Amplifier och utförts med OpenMP. Arbetet har också bytt ut en speciell beräkningsrutin som var särskilt krävande, speciellt för större problem. Slutresultatet är ett beräkningsprogram som ger samma resultat som det ursprungliga programmet men betydligt snabbare och med mindre datorresurser. Programmet kommer att användas i framtida forskningsprojekt. / Multi-core Shared Memory Parallel (SMP) systems became the norm ever since the performance trend prophesied by Moore’s law ended. Correctly utilizing the performance benefits these systems offer usually requires a conscious effort from the software developer’s side to enforce concurrency in the program. This is easy to disregard in small software projects and can lead to great amounts of unused potential parallelism in the produced code. This thesis attempted to improve the perfor- mance of a computationally demanding Thermal Elastohydrodynamic Lubrication (TEHL) simula- tion written in Fortran by finding such parallelism. The parallelization effort focused on the most demanding parts of the program identified using Intel’s VTune Amplifier and was implemented using OpenMP. The thesis also documents an algorithm change that led to further improvements in terms of execution time and scalability with respect to problem size. The end result is a faster, lighter and more efficient TEHL simulator that can further support the research in its domain.

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