Towards a Pythonic framework for control and analysis of magnetic levitation systems

openLa levitazione magnetica rappresenta una tecnologia affascinante con applicazioni in diversi settori, dalla ricerca scientifica all’industria. Questa tesi si concentra sulla creazione di un software in Python per il controllo e l’analisi della levitazione magnetica. Verranno presentati i fondamenti matematici che governano la levitazione magnetica e verr`a fornita un’implementazione Python open-source disponibile su GitHub. Saranno discussi i principali ostacoli nell’implementazione del software in Python, evidenziando le differenze rispetto all’uso di MATLAB. Verranno eseguite comparazioni tra le simulazioni effettuate con il software e i risultati ottenuti con MATLAB, con un’analisi delle differenze di velocit`a tra le due piattaforme. Uno dei principali risultati emersi dalla ricerca `e che Python risulta pi`u lento rispetto a MATLAB nell’esecuzione delle simulazioni di levitazione magnetica. Questo problema sar`a esaminato in dettaglio e saranno proposte soluzioni per migliorare le prestazioni del software Python. In particolare, verranno considerate le tecniche di compilazione Just-in-time (JIT) per accelerare l’esecuzione del codice. Questa tesi analizzer`a diverse approssimazioni per le funzioni ellittiche ellipk ed ellipe utilizzate nel software e ne comparer`a l’accuratezza e le prestazioni. Infine sono state eseguite simulazioni del controllore LQR in MATLAB e in Python. L’obiettivo principale di questo lavoro `e costruire un framework completo in Python per il controllo e l’analisi della levitazione magnetica, con l’ambizione di contribuire al progresso della ricerca in questo campo.Magnetic levitation represents a fascinating technology with applications in various fields, from scientific research to industry. This thesis focuses on the development of a Python software framework for the control and analysis of magnetic levitation systems. We will present the mathematical foundations governing magnetic levitation and provide an open-source Python implementation available on GitHub. We will discuss the major challenges in implementing the software in Python, highlighting the differences compared to MATLAB. Comparisons will be made between simulations performed using the software and results obtained with MATLAB, with an analysis of the speed differences between the two platforms. One of the key findings of this research is that Python is fundamentally slower than MATLAB in executing magnetic levitation simulations. This issue will be examined in detail, and solutions will be proposed to enhance the performance of the Python software. In particular, Just-in-time (JIT) compilation techniques will be considered to accelerate code execution. This thesis will analyze different approximations for the elliptic functions ellipk and ellipe used in the software, and compare their accuracy and performance. In the end, the LQR controller was simulated using MATLAB and Python. The primary objective of this work is to construct a comprehensive Python framework for the control and analysis of magnetic levitation, with the ambition of contributing to the advancement of research in this field

ALPyNA: Acceleration of Loops in Python for Novel Architectures

We present ALPyNA, an automatic loop parallelization framework for Python, which analyzes data dependences within nested loops and dynamically generates CUDA kernels for GPU execution. The ALPyNA system applies classical dependence analysis techniques to discover and exploit potential parallelism. The skeletal structure of the dependence graph is determined statically (if possible) or at runtime; this is combined with type and bounds information discovered at runtime, to auto-generate high-performance kernels for offload to GPU. We demonstrate speedups of up to 1000x relative to the native CPython interpreter across four array-intensive numerical Python benchmarks. Performance improvement is related to both iteration domain size and dependence graph complexity. Nevertheless, this approach promises to bring the benefits of manycore parallelism to application developers

Integrals over Gaussians under Linear Domain Constraints

Integrals of linearly constrained multivariate Gaussian densities are a frequent problem in machine learning and statistics, arising in tasks like generalized linear models and Bayesian optimization. Yet they are notoriously hard to compute, and to further complicate matters, the numerical values of such integrals may be very small. We present an efficient black-box algorithm that exploits geometry for the estimation of integrals over a small, truncated Gaussian volume, and to simulate therefrom. Our algorithm uses the Holmes-Diaconis-Ross (HDR) method combined with an analytic version of elliptical slice sampling (ESS). Adapted to the linear setting, ESS allows for rejection-free sampling, because intersections of ellipses and domain boundaries have closed-form solutions. The key idea of HDR is to decompose the integral into easier-to-compute conditional probabilities by using a sequence of nested domains. Remarkably, it allows for direct computation of the logarithm of the integral value and thus enables the computation of extremely small probability masses. We demonstrate the effectiveness of our tailored combination of HDR and ESS on high-dimensional integrals and on entropy search for Bayesian optimization

High-speed processing of X-ray wavefront marking data with the Unified Modulated Pattern Analysis (UMPA) model

Wavefront-marking X-ray imaging techniques use e.g., sandpaper or a grating to generate intensity fluctuations, and analyze their distortion by the sample in order to retrieve attenuation, phase-contrast, and dark-field information. Phase contrast yields an improved visibility of soft-tissue specimens, while dark-field reveals small-angle scatter from sub-resolution structures. Both have found many biomedical and engineering applications. The previously developed Unified Modulated Pattern Analysis (UMPA) model extracts these modalities from wavefront-marking data. We here present a new UMPA implementation, capable of rapidly processing large datasets and featuring capabilities to greatly extend the field of view. We also discuss possible artifacts and additional new features.Comment: 18 pages, 7 figures, submitted to Optics Expres

PULSEE: A software for the quantum simulation of an extensive set of magnetic resonance observables

We present an open-source software for the simulation of observables in magnetic resonance experiments, including nuclear magnetic/quadrupole resonance NMR/NQR and electron spin resonance (ESR), developed to assist experimental research in the design of new strategies for the investigation of fundamental quantum properties of materials, as inspired by magnetic resonance protocols that emerged in the context of quantum information science (QIS). The package introduced here enables the simulation of both standard NMR spectroscopic observables and the time-evolution of an interacting single-spin system subject to complex pulse sequences, i.e. quantum gates. The main purpose of this software is to facilitate in the development of much needed novel NMR-based probes of emergent quantum orders, which can be elusive to standard experimental probes. The software is based on a quantum mechanical description of nuclear spin dynamics in NMR/NQR experiments and has been widely tested on available theoretical and experimental results. Moreover, the structure of the software allows for basic experiments to easily be generalized to more sophisticated ones, as it includes all the libraries required for the numerical simulation of generic spin systems. In order to make the program easily accessible to a large user base, we developed a user-friendly graphical interface, Jupyter notebooks, and fully-detailed documentation. Lastly, we portray several examples of the execution of the code that illustrate the potential of a novel NMR paradigm, inspired by QIS, for efficient investigation of emergent phases in strongly correlated materials.Comment: 51 page

Truly Sparse Neural Networks at Scale

Recently, sparse training methods have started to be established as a de facto approach for training and inference efficiency in artificial neural networks. Yet, this efficiency is just in theory. In practice, everyone uses a binary mask to simulate sparsity since the typical deep learning software and hardware are optimized for dense matrix operations. In this paper, we take an orthogonal approach, and we show that we can train truly sparse neural networks to harvest their full potential. To achieve this goal, we introduce three novel contributions, specially designed for sparse neural networks: (1) a parallel training algorithm and its corresponding sparse implementation from scratch, (2) an activation function with non-trainable parameters to favour the gradient flow, and (3) a hidden neurons importance metric to eliminate redundancies. All in one, we are able to break the record and to train the largest neural network ever trained in terms of representational power -- reaching the bat brain size. The results show that our approach has state-of-the-art performance while opening the path for an environmentally friendly artificial intelligence era.Comment: 30 pages, 17 figure

Programação paralela do método dos elementos de contorno

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2020.O presente trabalho explora técnicas de programação de alto desempenho para aperfeiçoar um código de método dos elementos de contorno (MEC). Serão discutidas as vantagens da implementação da programação em paralelo, passando por uma revisão teórica sobre vetorização, multi-threading e programação paralela em clusters. A implementação do MEC foi escrita em Python devido a facilidade de programação que reduz o tempo gasto na etapa de desenvolvimento. Serão avaliadas as partes que mais consomem tempo de processamento para uma otimização utilizando a biblioteca Cython, que transforma trechos do programa em extensões em C. Foi explorado o uso de Softwares livres, Open Source, para soluções de geração de CAD (FreeCad, Designspark Mechanical), geração de malha (GMSH), leitura e conversão de malha (Meshio), visualização e pós-processamento (ParaView). Por fim, foi analisado o impacto da implementação de paralelismo, com memória compartilhada, em um dos trechos críticos da rotina de cálculo e foram discutidos os aspectos práticos e dificuldades técnicas, deste método, com o intuito de fomentar o uso de novas práticas mais eficientes de programação. A paralelização da rotina de integração, existente no código, aumentou a velocidade por um fator igual ao número de processadores utilizados.The present work explore high performance programming techniques to enhance a boundary element method (BEM) code. It was discussed the advantages of implementing parallel programming, throughout the concept of vectorization, multi-threading and parallel programming on clusters. The BEM implementation has been written in Python due to the ease of programming that reduces the time spent in the development stage. The most time consuming parts will be evaluated for a optimization utilizing the Cython library, that turns program snippets into C extensions. Free Open Source Softwares will be explored for solutions of CAD generation (FreeCad, Designspark Mechanical), Mesh generation (GMSH), Mesh reading and conversion (Meshio), visualization and post processing (ParaView).Finally, it was analised the impact of a parallel implementation, with shared memory, on a critical section of the calculation routine and it was discussed the practical aspects and technical dificulties of such method, with the intention of fomenting the use of new and more efficient programming practices. The paralelization of the integration routine, that exist in the code, enhanced the speed by a factor equal to the number of processors utilized