Ray tracing is widely used technique for realistic rendering of computer scenes. Its major drawback is time needed to compute the image, therefore it's usually parallelized. This thesis describes parallelization and ray tracing in general. It explains the possibility of how can be ray tracing parallelized as well as it defines the problems which may occur during the process. The result is parallel rendering application which uses selected ray tracing software and measurement of how successful this application is.

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Today's supercomputers gain their performance through a rapidly increasing number of cores per node. To tackle issues arising from those developments new parallelization approaches guided by modern software engineering are inevitable. The concept of task-based parallelization is a promising candidate to overcome many of those challenges. However, for latency-critical applications, like molecular dynamics, available tasking frameworks introduce considerable overheads. In this work a lightweight task engine for latency-critical applications is proposed. The main contributions of this thesis are a static data-flow dispatcher, a type-driven priority scheduler and an extension for communication-enabled tasks. The dispatcher allows a user-configurable mapping of algorithmic dependencies in the task-engine at compile-time. Resolving these dependencies at compile-time reduces the run-time overhead. The scheduler enables the prioritized execution of a critical path of an algorithm. Additionally, the priorities are deduced from the task type at compile-time as well. Furthermore, the aforementioned task engine supports inter-node communication via message passing. The provided communication interface drastically simplifies the user interface of inter-node communication without introducing additional performance penalties. This is only possible by distinguishing two developer roles -- the library developer and the algorithm developer. All proposed components follow a strict guideline to increase the maintainability for library developers and the usability for algorithm developers. To reach this goal a high level of abstraction and encapsulation is required in the software stack. As proof of concept the communication-enabled task engine is utilized to parallelize the FMM for molecular dynamics.

Vid en ekokardiografisk undersökning läggs fokus främst på klaffunktion, hjärtrumsstorlek och vänstersidans systoliska funktion. Vänstersidans diastoliska funktion har dock börjat få större betydelse, men upplevs ofta vara svårbedömd. Höger kammare har en komplex anatomi med ett trabekulerat myokardie och är otillgängligt placerad i bröstkorgen, vilket gör den svårare att bedöma än vänster kammare. 1995 publicerades ett index för bedömning av hjärtats kombinerade systoliska och diastoliska funktion, myocardial performance index (MPI). Syftet med studien var att undersöka om MPI kan vara en användbar och kompletterande metod vid bedömning av systolisk och diastolisk funktion i höger respektive vänster kammare. I den aktuella studien ingick 33 personer i åldrarna 21–80. MPI beräknades med pulsad vävnadsdoppler under en hjärtcykel. MPI jämfördes mot traditionella ekokardiografiska mätmetoder som speglar systolisk och diastolisk funktion för vänster respektive höger kammare. Normalfördelnings-, korrelations- och överrensstämmelseanalyser utfördes. För vänstersidig kammarfunktion sågs en signifikant korrelation mellan MPI och MAPSE. Ingen eller dålig överensstämmelse sågs mellan MPI och samtliga traditionella mätmetoder för systolisk funktion. För högersidig kammarfunktion sågs en starkt signifikant korrelation mellan MPI och FAC samt TAPSE. Mindre god överensstämmelse sågs mellan högersidans MPI och FAC samt TAPSE. För MPI och E/e’ sågs ingen signifikant korrelation hos vare sig vänster eller höger kammare och en sämre överensstämmelse än om klassificeringen hade gjorts rent slumpmässigt. Användbarheten av MPI för bedömning av vänster kammarfunktion anses, baserat på aktuell studie, vara låg. MPI kan vara användbart vid bedömning av höger kammares systoliska funktion, men inte avseende diastolisk funktion.

The Integrated Nested Laplace Approximations (INLA) method has become a commonly used tool for researchers and practitioners to perform approximate Bayesian inference for various fields of applications. It has become essential to incorporate more complex models and expand the method’s capabilities with more features. In this dissertation, we contribute to the INLA method in different aspects. First, we present a new framework, INLA$^+$, based on dense matrices to perform approximate Bayesian inference. An application of the new approach is fitting disease-mapping models for count data with complex interactions. When the precision matrix is dense, the new approach scales better than the existing INLA method and utilizes the power of multiprocessors on shared and distributed memory architectures in today’s computational resources. Second, we propose an adaptive technique to improve gradient estimation for the convex gradient-based optimization framework in INLA. We propose a simple limited-memory technique for improving the accuracy of the numerical gradient of the marginal posterior of the hyperparameter by exploiting a coordinate transformation of the gradient and the history of previously taken descent directions. Third, we extend the commonly utilized Bayesian spatial model in disease mapping, known as the Besag model, into a non-stationary spatial model. This new model considers variations in spatial dependency among a predetermined number of sub-regions. The model incorporates multiple precision parameters, which enable different intensities of spatial dependence in each sub-region. To avoid overfitting and enhance generalization, we derive a joint penalized complexity prior for these parameters. These contributions expand the capabilities of the INLA method, improving its scalability, accuracy, and flexibility for a wider range of applications.

This thesis involves a case study in the use of parallelism to improve the performance of an application for computational research on molecules. The application, VASP, was migrated from a machine with 4 nodes and 16 single-threaded processors to a machine with 60 nodes and 120 dual-threaded processors. When initially migrated, VASP's performance deteriorated after about 17 processing elements (PEs), due to network contention. Subsequent modifications that restrict communication amongst VASP processes, together with additional support for threading, allowed VASP to scale up to 112 PEs, the maximum number that was tested. Other performance-enhancing optimizations that were attempted included replacing old libraries, which produced improvements of about 10%, and prefetching, which degraded, rather than enhanced, VASP performance.

Volumetric rendering is used to visualize volume data from e.g. scientific simulations. Many advanced applications use large gigabyte- or terabyte-sized data sets, which typically means that multiple compute nodes need to partake in the rendering process to achieve interactive frame rates. Load balancing is generally used to optimize the rendering performance. In existing load balancing techniques, nodes generally only render directly-connected data and handle load balancing based on data locality in kd-trees. This approach can result in redundant data transfers and unbalanced data distribution, which affect the frame rate and increase the hardware requirements of all nodes. In this thesis we present a novel load balancing technique for sort-last volume rendering which utilizes a group hierarchy. The technique allows nodes to render data from arbitrary positions in the volume, without inducing a costly image compositing stage. The technique is compared to a static load balancing technique as well as a dynamic kd-tree based load balancing technique. Our testing demonstrated that the presented technique performed better than or equal to the kd-tree based technique while also lowering the worst-case memory usage complexity of all nodes. Utilizing a group hierarchy effectively helped to lower the compositing time of the presented technique. / Volumetrisk rendering används för att visualisera bland annat vetenskapligasimuleringar. Inom avancerade användingsområden används ofta dataset med en storlek på flera gigabyte eller terabyte. Detta medför att flera noder ofta måste användas för att uppnå en interaktiv bildfrekvens. Belastningsutjämning används generellt för att optimera renderingsprestandan. I befintliga tekniker renderar noder vanligtvis endast direkt sammankopplad data och utför belastningsutjämning baserat på datalokalitet i kd-träd. Detta kan resultera i redundanta dataöverföringar och en obalanserad datadistribution, vilket påverkar bildfrekvensen och ökar hårdvarukraven för alla noder. I denna avhandling presenterar vi en ny teknik för belastningsutjämning för sort-last volumetrisk rendering som använder en grupphierarki. Tekniken tillåter noder att rendera data från godtyckliga positioner i volymen utan att förorsaka ett kostsamt steg för bildsammansättning. Tekniken jämförs med en statisk belastningsutjämningsteknik såväl som en dynamisk belastningsutjämningsteknik baserad på kd-träd. Våra tester visar att den presenterade tekniken presterar bättre eller likvärdigt med den kd-trädbaserade tekniken medan den samtidigt sänker minneskomplexiteten för alla noder. Användandet av en grupphierarki sänkte effektivt bildsammansättningstiden för den presenterade tekniken.

Traditional HPC scientific workloads are tightly coupled, while emerging scientific workflows exhibit even more complex patterns, consisting of multiple characteristically different stages that may be IO-intensive, compute-intensive, or memory-intensive. New high-performance computer systems are evolving to adapt to these new requirements and are motivated by the need for performance and efficiency in resource usage. On the other hand, cloud workloads are loosely coupled, and their systems have matured technologies under different constraints from HPC. In this thesis, the use of cloud technologies designed for loosely coupled dynamic and elastic workloads is explored, repurposed, and examined in the landscape of HPC in three major parts. The first part deals with the deployment of HPC workloads in cloud-native environments through the use of containers and analyses the feasibility and trade-offs of elastic scaling. The second part relates to the use of workflow management systems in HPC workflows; in particular, a molecular docking workflow executed through Airflow is discussed. Finally, object storage systems, a cost-effective and scalable solution widely used in the cloud, and their usage in HPC applications through MPI I/O are discussed in the third part of this thesis. / Framväxande vetenskapliga applikationer är mycket datatunga och starkt kopplade. Nya högpresterande datorsystem anpassar sig till dessa nya krav och motiveras av behovet av prestanda och effektivitet i resursanvändningen. Å andra sidan är moln-applikationer löst kopplade och deras system har mogna teknologier som utvecklats under andra begränsningar än HPC. I den här avhandlingen diskuteras användningen av moln-teknologier som har mognat under löst kopplade applikationer i HPC-landskapet i tre huvuddelar. Den första delen handlar om implementeringen av HPC-applikationer i molnmiljöer genom användning av containrar och analyserar genomförbarheten och avvägningarna av elastisk skalning. Den andra delen handlar om användningen av arbetsflödeshanteringsystem i HPC-arbetsflöden; särskilt diskuteras ett molekylär dockningsarbetsflöde som utförs genom Airflow. Objektlagringssystem och deras användning inom HPC, tillsammans med ett gränssnitt mellan S3-standard och MPI I/O, diskuteras i den tredje delen av denna avhandling / <p>QC 20231122</p>

Chronic pain affects nearly 48 million Americans (Haggard, Stowell, Bernstein, & Gatchel, 2008). Established guidelines for pain management encourage the use of personality assessment in chronic pain evaluation (Karlin, Creech, Grimes, Clark, Meagher, & Morey, 2005). In relation to the Big Five personality factors, low Openness relates negatively to treatment success, (Hopwood, Creech, Clark, Meagher, & Morey, 2008), and elevated Neuroticism scores also correlate with increased pain levels among individuals in hospital or rehab settings (Ashgari & Nicholas, 2006; Nitch & Boon, 2004). In contrast to these prior studies, this study identifies correlates in a relatively high-functioning population (college students) to further elucidate the connection between chronic pain and personality. This study compares scores on the NEO-FFI (Costa & McCrae, 1992), the West Haven-Yale Multidimensional Pain Inventory (WHYMPI, Kerns, Turk, & Rudy, 1985), and the Pain Catastrophizing Scale (PCS, American Academy of Orthopaedic Manual Physical Therapists, 2010). Significant correlations were found between Neuroticism, Extraversion, and Agreeableness and select subscales of both the WHYMPI and the PCS. A linear regression of scores showed that Neuroticism was very strongly related to WHYMPI scores. In fact, the WHYMPI scores accounted for 67.9% of variance in Neuroticism. Scores on the WHYMPI also correlated with PCS scores. Helplessness and Overall scores significantly correlated to Life Control and certain positive social support scores. The findings of this study emphasize the need for pain clinicians to incorporate psychological assessments, especially concerning Neuroticism, into their evaluations of chronic pain patients.

Bhom's hydrodynamic formulation (or quantum fluid dynamics) is an attractive approach since, it connects classical and quantum mechanical theories of matter through Hamilton-Jacobi (HJ) theory, and quantum potential. Lopreore and Wyatt derived and implemented one-dimensional quantum trajectory method (QTM), a new wave-packet approach, for solving hydrodynamic equations of motion on serial computing environment. Brook et al. parallelized the QTM on shared memory computing environment using a partially implicit method, and conducted accuracy study of a free particle. These studies exhibited a strange behavior of the relative error for the probability density referred to as the transient effect. In the present work, numerical experiments of Brook et al. were repeated with a view to identify the physical origin of the transient effect and its resolution. The present work used the QTM implemented on a distributed memory computing environment using MPI. The simulation is guided by an explicit scheme.