Ett spel för akustiska gitarrer som liknar Activisions Guitar Hero har skrivits i programmeringsspråket Python. Spelet har ett grafiskt användargränssnitt och ett flertal låtar finns att välja emellan. I spelet visas de ackord eller enskilda strängar som ska spelas och när man ska spela dessa. Poäng delas ut om man träffar rätt ackord eller sträng. Spelet fungerar bra i ett normaltyst rum om gitarren spelas ganska rent. Förbättringsmöjligheterna är goda.

How can Python users be empowered with the robust simulation, compilation and scripting abilities of a non-proprietary object-oriented, equation based modeling language such as Modelica? The immediate objective of this thesis work is to develop an application programming interface for the OpenModelica modeling and simulation environment that would bridge the gap between the two agile programming languages Python and Modelica. The Python interface to OpenModelica OMPython, is both a tool and a functional library that allows Python users to realize the full capabilities of Open- Modelica’s scripting and simulation environment requiring minimal setup actions. OMPython is designed to combine both simulation and model building. Thus domain experts (people writing the models) and computational engineers (people writing the solver code) can work on one unified tool that is industrially viable for optimization of Modelica models, while offering a flexible platform for algorithm development and research.

The two well-known approaches to designing combinatorial generation algorithms are the recursive approach and the iterative approach. In this thesis a third design approach using coroutines, introduced by Knuth and Ruskey, is explored further. An introduction to coroutines and their implementation in modern languages (in particular Python) is provided, and the coroutine-based approach is introduced using an example, and contrasted with the recursive and iterative approaches. The coroutine sum, coroutine product, and coroutine symmetric sum constructs are defined to create an algebra of coroutines, and used to give concise definitions of coroutine-based algorithms for generating ideals of chain and forest posets. Afterwards, new coroutine-based variations of several algorithms, including the Steinhaus-Johnson-Trotter algorithm for generating permutations in Gray order, the Varol-Rotem algorithm for generating linear extensions in Gray order, and the Pruesse-Ruskey algorithm for generating signed linear extensions of a poset in Gray order, are given. / Graduate / 0984 / saba@uvic.ca

Heterogeneous computing platforms that use GPUs and CPUs in tandem for computation have become an important choice to build low-cost high-performance computing platforms. The computing ability of modern GPUs surpasses that of CPUs can offer for certain classes of applications. GPUs can deliver several Tera-Flops in peak performance. However, programmers must adopt a more complicated and more difficult new programming paradigm. To alleviate the burden of programming for heterogeneous systems, Garg and Amaral developed a Python compiling framework that combines an ahead-of-time compiler called unPython with a just-in-time compiler called jit4GPU. This compilation framework generates code for systems with AMD GPUs. We extend the framework to retarget it to generate OpenCL code, an industry standard that is implemented for most GPUs. Therefore, by generating OpenCL code, this new compiler, called jit4OpenCL, enables the execution of the same program in a wider selection of heterogeneous platforms. To further improve the target-code performance on nVidia GPUs, we developed an array-access analysis tool that helps to exploit the data reusability by utilizing the shared (local) memory space hierarchy in OpenCL. The thesis presents an experimental performance evaluation indicating that, in comparison with jit4GPU, jit4OpenCL has performance degradation because of the current performance of implementations of OpenCL, and also because of the extra time needed for the additional just-in-time compilation. However, the portable code generated by jit4OpenCL still have performance gains in some applications compared to highly optimized CPU code.

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Scientists are trending towards usage of high-level programming languages such as Python. The convenience of these languages often have a performance cost. As the amount of data being processed increases this can make using these languages unfeasible. Parallelism is a means to achieve better performance, but many users are unaware of it, or find it difficult to work with. This thesis presents ParForPy, a means for loop-parallelization to to simplify usage of parallelism in Python for users. Discussion is included for determining when parallelism matches well with the problem. Results are given that indicate that ParForPy is both capable of improving program execution time and perceived to be a simpler construct to understand than other techniques for parallelism in Python.

Ziel dieser Arbeit war die Beschleunigung des von Elena-Ruxandra Cojocaru und Sébastien Bérujon in Python implementierten Wellenfrontrekonstruktionsalgorithmus. Dieser berechnet aus zwei Bildern einer Probe pixelweise die Fronten der elektromagnetischen Welle eines Röntgenlasers. Die Bilder werden hierbei von zwei hochempfindlichen Röntgen-CCD-Sensoren aufgenommen, welche in einem festen Abstand zueinander und zur Probe positioniert sind. Treffen Strahlen des Röntgenlasers auf diese, so lässt sich aus den so aufgenommenen Streubildern die Wellenfront rekonstruieren, was Rückschlüsse auf die Struktur der Probe zulässt. Auf Basis von Performance-Analysen der Python-Implementierung wurden Optimierungen und Parallelisierungsmöglichkeiten für die kritischen Programmabschnitte ermittelt, implementiert und evaluiert. Die schnellste vorgestellte Lösung basiert auf der Verteilung der Bildpaare auf mehrere Rechenkerngruppen und der Parallelisierung der Berechnung der Bildpaare auf diesen, was eine Skalierung über mehrere Knoten erlaubt. Kombiniert mit der Nutzung optimierter Bibliotheken und dem Übersetzen des Python-Codes wurde eine Beschleunigung von bis zu vier gegenüber der Referenzimplementierung mit gleicher Kernanzahl erreicht. Wurden 120 Kerne verwendet, so war eine Beschleunigung auf das bis zu 133-fache gegenüber der Referenz auf einem Kern möglich. Die Referenzdaten hierfür wurden an der Beamline BM05 der European Synchrotron Radiation Facility aufgenommen. / The goal of this thesis was the acceleration of the wavefront reconstruction algorithm which was developed in Python by Elena-Ruxandra Cojocaru and Sébastien Bérujon. This algorithms calculates the electromagnetic wavefront of an X-ray laser from two images of a target pixelwise. The images were captured by two highly sensitive X-ray CCD sensors, which were positioned in a fixed distance to each other and the target. When the refracted X-ray beam hits these detectors a distortion image is generated from which the wavefront can be reconstructed. The result can be used to draw conclusions about the structure of the target. On the basis of performance measurements of the Python implementation optimization and parallelization possibilities for critical sections were determined, implemented and evaluated. The fastest proposed solution is based on the distribution of the image pairs onto CPU core groups and the parallelization of the calculation of the image pairs on these which allows scaling the problem over multiple nodes. This combined with the use of optimized libraries and the compilation of the Python code resulted in a speedup of up to four towards the reference implementation without the use of more cores. When using 120 cores a speedup of up to 133 towards the reference implementation running on a single core could be achieved. The here used datasets were recorded at Beamline BM05 of the European Synchrotron Radiation Facility.

Η παρούσα διπλωματική εργασία αφορά την ανάπτυξη βιβλιοθήκης συναρτήσεων για κβαντικό υπολογισμό και τη δημιουργία γραφικού περιβάλλοντος το οποίο χρησιμοποιεί τη συγκεκριμένη βιβλιοθήκη. Αρχικά μελετήθηκαν: πρώτον, οι βασικές έννοιες που διέπουν τους κβαντικούς υπολογιστές και δεύτερον, η χρήση και οι δυνατότητες της γλώσσας python. Εν συνέχεια, ύστερα από εμβάθυνση στα ανωτέρω πεδία πραγματοποιήθηκε η συγγραφή της κβαντικής βιβλιοθήκης καθώς και η δημιουργία του γραφικού περιβάλλοντος που χρησιμοποιεί τη κβαντική αυτή βιβλιοθήκη. Επίσης δημιουργήθηκε και μία γραφική διεπαφή για την παρουσίαση των γραφικών παραστάσεων που παρουσιάζουν τα αποτελέσματα του κβαντικού υπολογισμού. Η ανάπτυξη του προγράμματος πραγματοποιήθηκε σε γλώσσα Python. / This diploma dissertation presents the development of a library for quantum calculation and the implementation of a graphic interface that uses this library. Initially, the quantum computers and the programming language python were studied. After the comprehension on the two above fields the quantum library and the graphic interface which uses that library were created. Another graphical interface also created for the presentation of the graphs that showing the results of the quantum computation. The program is written in Python language.