Visualizing the performance of parallel programs

Bibliography: pages 110-115.The performance analysis of parallel programs is a complex task, particularly if the program has to be efficient over a wide range of parallel machines. We have designed a performance analysis system called Chiron that uses scientific visualization techniques to guide and help the user in performance analysis activities. The aim of Chiron is to give the user full control over what section of the data he/she wants to investigate in detail. Chiron uses interactive three-dimensional graphics techniques to display large amounts of data in a compact and easy to understand/ conceptualize way. The system assists in the tracking of performance bottlenecks by showing data in 10 different views and allowing the user to interact with the data. In this thesis the design and implementation of Chiron are described, and its effectiveness illustrated by means of three case studies

Visualizing the memory performance of parallel programs with Chiron

Bibliography: leaves 78-81.This thesis describes Chiron, visualization system which helps programmers detect memory system bottlenecks in their shared-memory parallel applications. Chiron is different from most other performance debugging tools in that it uses three-dimensional graphics techniques to display vast amounts of memory-performance data. Both code-and data-oriented information can be presented in several views. These views have been designed to help the user detect problems which cause coherence interference or replacement interference. Chiron’s interactive user-interface enables the user to manipulate the views and home in on features which indicate memory system bottlenecks. The visualized data can be augmented with more detailed numerical and correlations between the separate views can be displayed. The effectiveness of Chiron is illustrated in this thesis by means of three case studies

The visual uncertainty paradigm for controlling screen-space information in visualization

The information visualization pipeline serves as a lossy communication channel for presentation of data on a screen-space of limited resolution. The lossy communication is not just a machine-only phenomenon due to information loss caused by translation of data, but also a reflection of the degree to which the human user can comprehend visual information. The common entity in both aspects is the uncertainty associated with the visual representation. However, in the current linear model of the visualization pipeline, visual representation is mostly considered as the ends rather than the means for facilitating the analysis process. While the perceptual side of visualization is also being studied, little attention is paid to the way the visualization appears on the display. Thus, we believe there is a need to study the appearance of the visualization on a limited-resolution screen in order to understand its own properties and how they influence the way they represent the data. I argue that the visual uncertainty paradigm for controlling screen-space information will enable us in achieving user-centric optimization of a visualization in different application scenarios. Conceptualization of visual uncertainty enables us to integrate the encoding and decoding aspects of visual representation into a holistic framework facilitating the definition of metrics that serve as a bridge between the last stages of the visualization pipeline and the user's perceptual system. The goal of this dissertation is three-fold: i) conceptualize a visual uncertainty taxonomy in the context of pixel-based, multi-dimensional visualization techniques that helps systematic definition of screen-space metrics, ii) apply the taxonomy for identifying sources of useful visual uncertainty that helps in protecting privacy of sensitive data and also for identifying the types of uncertainty that can be reduced through interaction techniques, and iii) application of the metrics for designing information-assisted models that help in visualization of high-dimensional, temporal data

Analysing and Reducing Costs of Deep Learning Compiler Auto-tuning

Deep Learning (DL) is significantly impacting many industries, including automotive, retail and medicine, enabling autonomous driving, recommender systems and genomics modelling, amongst other applications. At the same time, demand for complex and fast DL models is continually growing. The most capable models tend to exhibit highest operational costs, primarily due to their large computational resource footprint and inefficient utilisation of computational resources employed by DL systems. In an attempt to tackle these problems, DL compilers and auto-tuners emerged, automating the traditionally manual task of DL model performance optimisation. While auto-tuning improves model inference speed, it is a costly process, which limits its wider adoption within DL deployment pipelines. The high operational costs associated with DL auto-tuning have multiple causes. During operation, DL auto-tuners explore large search spaces consisting of billions of tensor programs, to propose potential candidates that improve DL model inference latency. Subsequently, DL auto-tuners measure candidate performance in isolation on the target-device, which constitutes the majority of auto-tuning compute-time. Suboptimal candidate proposals, combined with their serial measurement in an isolated target-device lead to prolonged optimisation time and reduced resource availability, ultimately reducing cost-efficiency of the process. In this thesis, we investigate the reasons behind prolonged DL auto-tuning and quantify their impact on the optimisation costs, revealing directions for improved DL auto-tuner design. Based on these insights, we propose two complementary systems: Trimmer and DOPpler. Trimmer improves tensor program search efficacy by filtering out poorly performing candidates, and controls end-to-end auto-tuning using cost objectives, monitoring optimisation cost. Simultaneously, DOPpler breaks long-held assumptions about the serial candidate measurements by successfully parallelising them intra-device, with minimal penalty to optimisation quality. Through extensive experimental evaluation of both systems, we demonstrate that they significantly improve cost-efficiency of autotuning (up to 50.5%) across a plethora of tensor operators, DL models, auto-tuners and target-devices

Leveraging elasticity theory to calculate cell forces: From analytical insights to machine learning

Living cells possess capabilities to detect and respond to mechanical features of their surroundings. In traction force microscopy, the traction of cells on an elastic substrate is made visible by observing substrate deformation as measured by the movement of embedded marker beads. Describing the substrates by means of elasticity theory, we can calculate the adhesive forces, improving our understanding of cellular function and behavior. In this dissertation, I combine analytical solutions with numerical methods and machine learning techniques to improve traction prediction in a range of experimental applications. I describe how to include the normal traction component in regularization-based Fourier approaches, which I apply to experimental data. I compare the dominant strategies for traction reconstruction, the direct method and inverse, regularization-based approaches and find, that the latter are more precise while the former is more stress resilient to noise. I find that a point-force based reconstruction can be used to study the force balance evolution in response to microneedle pulling showing a transition from a dipolar into a monopolar force arrangement. Finally, I show how a conditional invertible neural network not only reconstructs adhesive areas more localized, but also reveals spatial correlations and variations in reliability of traction reconstructions

Predictive Articulatory speech synthesis Utilizing Lexical Embeddings (PAULE)

Das Predictive Articulatory speech synthesis Utilizing Lexical Embeddings (PAULE) Modell ist ein neues Modell zur Kontrolle des artikulatorischen Sprachsynthesizers VocalTractLab (VTL) [15] . Mit PAULE lassen sich deutsche Wörter synthetisieren. Die Wortsynthese kann entweder mit Hilfe eines semantischen Vektors, der die Wortbedeu- tung kodiert, und der gewünschten Dauer der Wortsynthese gestartet werden oder es kann eine Resynthese von einer Audiodatei gemacht werden. Die Audiodatei kann beliebige Aufnahmen von Sprecher:innen enthalten, wobei die Resynthese immer über den Standardsprecher des VTL erfolgt. Abhängig von der Wortbedeutung und der Audiodatei variiert die Synthesequalität. Neu an PAULE ist, dass es einen prädiktiven Ansatz verwendet, indem es aus der geplanten Artikulation die dazugehörige perzeptuelle Akustik vorhersagt und daraus die Wortbedeutung ableitet. Sowohl die Akustik als auch die Wortbedeutung sind als metrische Vektorräume implementiert. Dadurch lässt sich ein Fehler zu einer gewünschten Zielakustik und Zielbedeutung berechnen und minimieren. Bei dem minimierten Fehler handelt es sich nicht um den tatsächlichen Fehler, der aus der Synthese mit dem VTL entsteht, sondern um den Fehler, der aus den Vorhersagen eines prädiktiven Modells generiert wird. Obwohl es nicht der tatsächliche Fehler ist, kann dieser Fehler genutzt werden, um die tatsächliche Artikulation zu verbessern. Um das prädiktive Modell mit der tatsächlichen Akustik in Einklang zu bringen, hört sich PAULE selbst zu. Ein in der Sprachsynthese zentrales Eins-Zu-Viele-Problem ist, dass eine Akustik durch viele verschiedene Artikulationen erzeugt werden kann. Dieses Eins-Zu-Viele-Problem wird durch die Vorhersagefehlerminimierung in PAULE aufgelöst, zusammen mit der Bedingung, dass die Artikulation möglichst stationär und mit möglichst konstanter Kraft ausgeführt wird. PAULE funktioniert ohne jegliche symbolische Repräsentation in der Akustik (Phoneme) und in der Artikulation (motorische Gesten oder Ziele). Damit zeigt PAULE, dass sich gesprochene Wörter ohne symbolische Beschreibungsebene model- lieren lassen. Der gesprochenen Sprache könnte daher im Vergleich zur geschriebenen Sprache eine fundamental andere Verarbeitungsebene zugrunde liegen. PAULE integriert Erfahrungswissen sukzessive. Damit findet PAULE nicht die global beste Artikulation sondern lokal gute Artikulationen. Intern setzt PAULE auf künstliche neuronale Netze und die damit verbundenen Gradienten, die zur Fehlerkorrektur verwendet werden. PAULE kann weder ganze Sätze synthetisieren noch wird somatosensorisches Feedback berücksichtigt. Zu Beidem gibt es Vorarbeiten, die in zukünftige Versionen integriert werden sollen.The Predictive Articulatory speech synthesis Utilizing Lexical Embeddings (PAULE) model is a new control model for the VocalTractLab (VTL) [15] speech synthesizer, a simulator of the human speech system. It is capable of synthesizing single words in the German language. The speech synthesis can be based on a target semantic vector or on target acoustics, i.e., a recorded word token. VTL is controlled by 30 parameters. These parameters have to be estimated for each time point during the production of a word, which is roughly every 2.5 milliseconds. The time-series of these 30 control parameters (cps) of the VTL are the control parameter trajectories (cp-trajectories). The high dimensionality of the cp-trajectories in combination with non-linear interactions leads to a many-to-one mapping problem, where many sets of cp-trajectories produce highly similar synthesized audio. PAULE solves this many-to-one mapping problem by anticipating the effects of cp- trajectories and minimizing a semantic and acoustic error between this nticipation and a targeted meaning and acoustics. The quality of the anticipation is improved by an outer loop, where PAULE listens to itself. PAULE has three central design features that distinguish it from other control models: First, PAULE does not use any symbolic units, neither motor primitives, articulatory targets, or gestural scores on the movement side, nor any phone or syllable representation on the acoustic side. Second, PAULE is a learning model that accumulates experience with articulated words. As a consequence, PAULE will not find a global optimum for the inverse kinematic optimization task it has to solve. Instead, it finds a local optimum that is conditioned on its past experience. Third, PAULE uses gradient-based internal prediction errors of a predictive forward model to plan cp-trajectories for a given semantic or acoustic target. Thus, PAULE is an error-driven model that takes its previous experiences into account. Pilot study results indicate that PAULE is able to minimize an acoustic semantic and acoustic error in the resynthesized audio. This allows PAULE to find cp-trajectories that are correctly classified by a classification model as the correct word with an accuracy of 60 %, which is close to the accuracy for human recordings of 63 %. Furthermore, PAULE seems to model vowel-to-vowel anticipatory coarticulation in terms of formant shifts correctly and can be compared to human electromagnetic articulography (EMA) recordings in a straightforward way. Furthermore, with PAULE it is possible to condition on already executed past cp-trajectories and to smoothly continue the cp-trajectories from the current state. As a side-effect of developing PAULE, it is possible to create large amounts of training data for the VTL through an automated segment-based approach. Next steps, in the development of PAULE, include adding a somatosensory feedback channel, extending PAULE from producing single words to the articulation of small utterances and adding a thorough evaluation

MC 2019 Berlin Microscopy Conference - Abstracts

Das Dokument enthält die Kurzfassungen der Beiträge aller Teilnehmer an der Mikroskopiekonferenz "MC 2019", die vom 01. bis 05.09.2019, in Berlin stattfand

Proceedings of the 19th Sound and Music Computing Conference

Proceedings of the 19th Sound and Music Computing Conference - June 5-12, 2022 - Saint-Étienne (France). https://smc22.grame.f

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.