Algorithm-Hardware Co-Design for Performance-driven Embedded Genomics

PhD ThesisGenomics includes development of techniques for diagnosis, prognosis and therapy of over 6000 known genetic disorders. It is a major driver in the transformation of medicine from the reactive form to the personalized, predictive, preventive and participatory (P4) form. The availability of genome is an essential prerequisite to genomics and is obtained from the sequencing and analysis pipelines of the whole genome sequencing (WGS). The advent of second generation sequencing (SGS), significantly, reduced the sequencing costs leading to voluminous research in genomics. SGS technologies, however, generate massive volumes of data in the form of reads, which are fragmentations of the real genome. The performance requirements associated with mapping reads to the reference genome (RG), in order to reassemble the original genome, now, stands disproportionate to the available computational capabilities. Conventionally, the hardware resources used are made of homogeneous many-core architecture employing complex general-purpose CPU cores. Although these cores provide high-performance, a data-centric approach is required to identify alternate hardware systems more suitable for affordable and sustainable genome analysis. Most state-of-the-art genomic tools are performance oriented and do not address the crucial aspect of energy consumption. Although algorithmic innovations have reduced runtime on conventional hardware, the energy consumption has scaled poorly. The associated monetary and environmental costs have made it a major bottleneck to translational genomics. This thesis is concerned with the development and validation of read mappers for embedded genomics paradigm, aiming to provide a portable and energy-efficient hardware solution to the reassembly pipeline. It applies the algorithmhardware co-design approach to bridge the saturation point arrived in algorithmic innovations with emerging low-power/energy heterogeneous embedded platforms. Essential to embedded paradigm is the ability to use heterogeneous hardware resources. Graphical processing units (GPU) are, often, available in most modern devices alongside CPU but, conventionally, state-of-the-art read mappers are not tuned to use both together. The first part of the thesis develops a Cross-platfOrm Read mApper using opencL (CORAL) that can distribute workload on all available devices for high performance. OpenCL framework mitigates the need for designing separate kernels for CPU and GPU. It implements a verification-aware filtration algorithm for rapid pruning and identification of candidate locations for mapping reads to the RG. Mapping reads on embedded platforms decreases performance due to architectural differences such as limited on-chip/off-chip memory, smaller bandwidths and simpler cores. To mitigate performance degradation, in second part of the thesis, we propose a REad maPper for heterogeneoUs sysTEms (REPUTE) which uses an efficient dynamic programming (DP) based filtration methodology. Using algorithm-hardware co-design and kernel level optimizations to reduce its memory footprint, REPUTE demonstrated significant energy savings on HiKey970 embedded platform with acceptable performance. The third part of the thesis concentrates on mapping the whole genome on an embedded platform. We propose a Pyopencl based tooL for gEnomic workloaDs tarGeting Embedded platfoRms (PLEDGER) which includes two novel contributions. The first one proposes a novel preprocessing strategy to generate low-memory footprint (LMF) data structure to fit all human chromosomes at the cost of performance. Second contribution is LMF DP-based filtration method to work in conjunction with the proposed data structures. To mitigate performance degradation, the kernel employs several optimisations including extensive usage of bit-vector operations. Extensive experiments using real human reads were carried out with state-of-the-art read mappers on 5 different platforms for CORAL, REPUTE and PLEDGER. The results show that embedded genomics provides significant energy savings with similar performance compared to conventional CPU-based platforms

Tools for improving performance portability in heterogeneous environments

Programa Oficial de Doutoramento en Investigación en Tecnoloxías da Información. 524V01[Abstract] Parallel computing is currently partially dominated by the availability of heterogeneous devices. These devices differ from each other in aspects such as the instruction set they execute, the number and the type of computing devices that they offer or the structure of their memory systems. In the last years, langnages, libraries and extensions have appeared to allow to write a parallel code once aud run it in a wide variety of devices, OpenCL being the most widespread solution of this kind. However, functional portability does not imply performance portability. This way, one of the probletns that is still open in this field is to achieve automatic performance portability. That is, the ability to automatically tune a given code for any device where it will be execnted so that it ill obtain a good performance. This thesis develops three different solutions to tackle this problem. The three of them are based on typical source-to-sonrce optimizations for heterogeneous devices. Both the set of optimizations to apply and the way they are applied depend on different optimization parameters, whose values have to be tuned for each specific device. The first solution is OCLoptimizer, a source-to-source optimizer that can optimize annotated OpenCL kemels with the help of configuration files that guide the optimization process. The tool optimizes kernels for a specific device, and it is also able to automate the generation of functional host codes when only a single kernel is optimized. The two remaining solutions are built on top of the Heterogeneous Programming Library (HPL), a C++ framework that provides an easy and portable way to exploit heterogeneous computing systexns. The first of these solutions uses the run-time code generation capabilities of HPL to generate a self-optimizing version of a matrix multiplication that can optimize itself at run-time for an spedfic device. The last solutíon is the development of a built-in just-in-time optirnizer for HPL, that can optirnize, at run-tirne, a HPL code for an specific device. While the first two solutions use search processes to find the best values for the optimization parameters, this Iast alternative relies on heuristics bMed on general optirnization strategies.[Resumen] Actualmente la computación paralela se encuentra dominada parcialmente por los múltiples dispositivos heterogéneos disponibles. Estos dispositivos difieren entre sí en características tales como el conjunto de instrucciones que ejecutan, el número y tipo de unidades de computación que incluyen o la estructura de sus sistemas de memoria. Durante los últimos años han aparecido lenguajes, librerías y extensiones que permiten escribir una única vez la versión paralela de un código y ejecutarla en un amplio abanico de dispositivos, siendo de entre todos ellos OpenCL la solución más extendida. Sin embargo, la portabilidad funcional no implica portabilidad de rendimiento. Así, uno de los grandes problemas que sigue abierto en este campo es la automatización de la portabilidad de rendimiento, es decir, la capacidad de adaptar automáticamente un código dado para su ejecución en cualquier dispositivo y obtener un buen rendimiento. Esta tesis aborda este problema planteando tres soluciones diferentes al mismo. Las tres se basan en la aplicación de optimizaciones de código a código usadas habitualmente en dispositivos heterogéneos. Tanto el conjunto de optimizaciones a aplicar como la forma de aplicarlas dependen de varios parámetros de optimización, cuyos valores han de ser ajustados para cada dispositivo concreto. La primera solución planteada es OCLoptirnizer, un optimizador de código a código que a partir de kernels OpenCL anotados y ficheros de configuración como apoyo, obtiene versiones optimizada de dichos kernels para un dispositivo concreto. Además, cuando el kernel a optimizar es único, automatiza la generación de un código de host funcional para ese kernel. Las otras dos soluciones han sido implementadas utilizando Heterogeneous Prograrnming LibranJ (HPL), una librería C++ que permite programar sistemas heterogéneos de forma fácil y portable. La primera de estas soluciones explota las capacidades de generación de código en tiempo de ejecución de HPL para generar versiones de un producto de matrices que se adaptan automáticamente en tiempo de ejecución a las características de un dispositivo concreto. La última solución consiste en el desarrollo e incorporación a HPL de un optimizador al vuelo, de fonna que se puedan obtener en tiempo de ejecución versiones optimizadas de un código HPL para un dispositivo dado. Mientras las dos primeras soluciones usan procesos de búsqueda para encontrar los mejores valores para los parámetros de optimización, esta última altemativa se basa para ello en heurísticas definidas a partir de recomendaciones generales de optimización.[Resumo] Actualmente a computación paralela atópase dominada parcialmente polos múltiples dispositivos heteroxéneos dispoñibles. Estes dispositivos difiren entre si en características tales como o conxunto de instruccións que executan, o número e tipo de unidades de computación que inclúen ou a estrutura dos seus sistemas de mem~ ría. Nos últimos anos apareceron linguaxes, bibliotecas e extensións que permiten escribir unha soa vez a versión paralela dun código e executala nun amplio abano de dispositivos, senda de entre todos eles OpenCL a solución máis extendida. Porén, a portabilidade funcional non implica portabilidade de rendemento. Deste xeito, uns dos grandes problemas que segue aberto neste campo é a automatización da portabilidade de rendemento, isto é, a capacidade de adaptar automaticamente un código dado para a súa execución en calquera dispositivo e obter un bo rendemento. Esta tese aborda este problema propondo tres solucións diferentes. As tres están baseadas na aplicación de optimizacións de código a código usadas habitualmente en disp~ sitivos heteroxéneos. Tanto o conxunto de optimizacións a aplicar como a forma de aplicalas dependen de varios parámetros de optimización para os que é preciso fixar determinados valores en función do dispositivo concreto. A primeira solución pro posta é OCLoptirnizer, un optimizador de código a código que partindo de kemels OpenCL anotados e ficheiros de configuración de apoio, obtén versións optimizadas dos devanditos kernels para un dispositivo concreto. Amais, cando o kernel a optimizaré único, tarnén automatiza a xeración dun código de host funcional para ese kernel. As outras dúas solucións foron implementadas utilizando Heterogeneous Programming Library (HPL), unha biblioteca C++ que permite programar sistemas heteroxéneos de xeito fácil e portable. A primeira destas solucións explota as capacidades de xeración de código en tempo de execución de HPL para xerar versións dun produto de matrices que se adaptan automaticamente ás características dun dispositivo concreto. A última solución consiste no deseuvolvemento e incorporación a HPL dun optimizador capaz de obter en tiempo de execución versións optimizada<; dun código HPL para un dispositivo dado. Mentres as dúas primeiras solucións usan procesos de procura para atopar os mellares valores para os parámetros de optimización, esta última alternativa baséase para iso en heurísticas definidas a partir de recomendacións xerais de optimización

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016)

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.The PhD Symposium was a very good opportunity for the young researchers to share information and knowledge, to present their current research, and to discuss topics with other students in order to look for synergies and common research topics. The idea was very successful and the assessment made by the PhD Student was very good. It also helped to achieve one of the major goals of the NESUS Action: to establish an open European research network targeting sustainable solutions for ultrascale computing aiming at cross fertilization among HPC, large scale distributed systems, and big data management, training, contributing to glue disparate researchers working across different areas and provide a meeting ground for researchers in these separate areas to exchange ideas, to identify synergies, and to pursue common activities in research topics such as sustainable software solutions (applications and system software stack), data management, energy efficiency, and resilience.European Cooperation in Science and Technology. COS

Evolutionary design of deep neural networks

Mención Internacional en el título de doctorFor three decades, neuroevolution has applied evolutionary computation to the optimization of the topology of artificial neural networks, with most works focusing on very simple architectures. However, times have changed, and nowadays convolutional neural networks are the industry and academia standard for solving a variety of problems, many of which remained unsolved before the discovery of this kind of networks. Convolutional neural networks involve complex topologies, and the manual design of these topologies for solving a problem at hand is expensive and inefficient. In this thesis, our aim is to use neuroevolution in order to evolve the architecture of convolutional neural networks. To do so, we have decided to try two different techniques: genetic algorithms and grammatical evolution. We have implemented a niching scheme for preserving the genetic diversity, in order to ease the construction of ensembles of neural networks. These techniques have been validated against the MNIST database for handwritten digit recognition, achieving a test error rate of 0.28%, and the OPPORTUNITY data set for human activity recognition, attaining an F1 score of 0.9275. Both results have proven very competitive when compared with the state of the art. Also, in all cases, ensembles have proven to perform better than individual models. Later, the topologies learned for MNIST were tested on EMNIST, a database recently introduced in 2017, which includes more samples and a set of letters for character recognition. Results have shown that the topologies optimized for MNIST perform well on EMNIST, proving that architectures can be reused across domains with similar characteristics. In summary, neuroevolution is an effective approach for automatically designing topologies for convolutional neural networks. However, it still remains as an unexplored field due to hardware limitations. Current advances, however, should constitute the fuel that empowers the emergence of this field, and further research should start as of today.This Ph.D. dissertation has been partially supported by the Spanish Ministry of Education, Culture and Sports under FPU fellowship with identifier FPU13/03917. This research stay has been partially co-funded by the Spanish Ministry of Education, Culture and Sports under FPU short stay grant with identifier EST15/00260.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: María Araceli Sanchís de Miguel.- Secretario: Francisco Javier Segovia Pérez.- Vocal: Simon Luca

Proceedings of the First International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2014): Porto, Portugal

Proceedings of: First International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2014). Porto (Portugal), August 27-28, 2014

Use of the Smartphone Camera to Monitor Adherence to Inhaled Therapy

Self-management strategies can lead to improved health outcomes, fewer unscheduled treatments, and improved disease control. Compliance with inhaled control drugs is essential to achieve good clinical outcomes in patients with chronic respiratory diseases. However, compliance assessments suffer from the difficulty of achieving a high degree of trustworthiness, as patients often self-report high compliance rates and are considered unreliable. This thesis aims to enable reliable adhesion measurement by developing a mobile application module to objectively verify inhalation usage using image snapshots of the inhalation counter. To achieve this, a mobile application module featuring pre and post processing techniques and a default machine learning framework was built, for inhaler and dosage counter numbers detection. In addition, in an effort to improve the app’s capabilities of text recognition on a worst-performing inhaler, a machine learning model was trained on an inhaler image dataset. Some of the features worked on during this project were incorporated on the current version of the app InspirerMundi, a medication management mobile application, planned to be made available at the PlayStore by the end of 2021. The proposed approach was validated through a series of different inhaler image datasets. The carried-out tests with the default machine learning configuration showed correct detection of dosage counters for 70% of inhaler registration events and 93% for three commonly used inhalers in Portugal. On the other hand, the trained model had an average accuracy of 88 % in recognizing the digits on the dose counter of one of the worst-performing inhaler models. These results show the potential to explore mobile and embedded capabilities to gain additional evidence for inhaler compliance. These systems can help bridge the gap between patients and healthcare professionals. By empowering patients with disease selfmanagement and drug adherence tools and providing additional relevant data, these systems pave the way for informed disease management decisions