566 research outputs found
Smith-Waterman Acceleration in Multi-GPUs: A Performance per Watt Analysis
Artículo publicado en el libro de actas del congreso.We present a performance per watt analysis of CUDAlign 4.0, a parallel strategy to obtain the optimal alignment of huge DNA se- quences in multi-GPU platforms using the exact Smith-Waterman method. Speed-up factors and energy consumption are monitored on different stages of the algorithm with the goal of identifying advantageous sce- narios to maximize acceleration and minimize power consumption. Ex- perimental results using CUDA on a set of GeForce GTX 980 GPUs illustrate their capabilities as high-performance and low-power devices, with a energy cost to be more attractive when increasing the number of GPUs. Overall, our results demonstrate a good correlation between the performance attained and the extra energy required, even in scenarios where multi-GPUs do not show great scalability.Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech
Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications
With the advent of dedicated Deep Learning (DL) accelerators and neuromorphic
processors, new opportunities are emerging for applying deep and Spiking Neural
Network (SNN) algorithms to healthcare and biomedical applications at the edge.
This can facilitate the advancement of the medical Internet of Things (IoT)
systems and Point of Care (PoC) devices. In this paper, we provide a tutorial
describing how various technologies ranging from emerging memristive devices,
to established Field Programmable Gate Arrays (FPGAs), and mature Complementary
Metal Oxide Semiconductor (CMOS) technology can be used to develop efficient DL
accelerators to solve a wide variety of diagnostic, pattern recognition, and
signal processing problems in healthcare. Furthermore, we explore how spiking
neuromorphic processors can complement their DL counterparts for processing
biomedical signals. After providing the required background, we unify the
sparsely distributed research on neural network and neuromorphic hardware
implementations as applied to the healthcare domain. In addition, we benchmark
various hardware platforms by performing a biomedical electromyography (EMG)
signal processing task and drawing comparisons among them in terms of inference
delay and energy. Finally, we provide our analysis of the field and share a
perspective on the advantages, disadvantages, challenges, and opportunities
that different accelerators and neuromorphic processors introduce to healthcare
and biomedical domains. This paper can serve a large audience, ranging from
nanoelectronics researchers, to biomedical and healthcare practitioners in
grasping the fundamental interplay between hardware, algorithms, and clinical
adoption of these tools, as we shed light on the future of deep networks and
spiking neuromorphic processing systems as proponents for driving biomedical
circuits and systems forward.Comment: Submitted to IEEE Transactions on Biomedical Circuits and Systems (21
pages, 10 figures, 5 tables
Services and support for IU School of Medicine and Clinical Affairs Schools by the UITS/PTI Advanced Biomedical Information Technology Core and Research Technologies Division in FY 2013 - Extended Version
The report presents information on services delivered in FY 2013 by ABITC and RT to the IU School of Medicine and the other Clinical Affairs schools that include the Schools of Nursing, Dentistry, Health and Rehabilitation Sciences, and Optometry; the Fairbanks School of Public Health at IUPUI; the School of Public Health at IU Bloomington; and the School of Social Work
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Grid-based semantic integration of heterogeneous data resources: Implementation on a HealthGrid
This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.The semantic integration of geographically distributed and heterogeneous data
resources still remains a key challenge in Grid infrastructures. Today's
mainstream Grid technologies hold the promise to meet this challenge in a
systematic manner, making data applications more scalable and manageable. The
thesis conducts a thorough investigation of the problem, the state of the art, and
the related technologies, and proposes an Architecture for Semantic Integration of
Data Sources (ASIDS) addressing the semantic heterogeneity issue. It defines a
simple mechanism for the interoperability of heterogeneous data sources in order
to extract or discover information regardless of their different semantics. The
constituent technologies of this architecture include Globus Toolkit (GT4) and
OGSA-DAI (Open Grid Service Architecture Data Integration and Access)
alongside other web services technologies such as XML (Extensive Markup
Language). To show this, the ASIDS architecture was implemented and tested in a
realistic setting by building an exemplar application prototype on a HealthGrid
(pilot implementation).
The study followed an empirical research methodology and was informed by
extensive literature surveys and a critical analysis of the relevant technologies and
their synergies. The two literature reviews, together with the analysis of the
technology background, have provided a good overview of the current Grid and
HealthGrid landscape, produced some valuable taxonomies, explored new paths
by integrating technologies, and more importantly illuminated the problem and
guided the research process towards a promising solution. Yet the primary
contribution of this research is an approach that uses contemporary Grid
technologies for integrating heterogeneous data resources that have semantically
different. data fields (attributes). It has been practically demonstrated (using a
prototype HealthGrid) that discovery in semantically integrated distributed data
sources can be feasible by using mainstream Grid technologies, which have been
shown to have some Significant advantages over non-Grid based approaches
An evaluation of galaxy and ruffus-scripting workflows system for DNA-seq analysis
>Magister Scientiae - MScFunctional genomics determines the biological functions of genes on a global scale by
using large volumes of data obtained through techniques including next-generation
sequencing (NGS). The application of NGS in biomedical research is gaining in
momentum, and with its adoption becoming more widespread, there is an increasing
need for access to customizable computational workflows that can simplify, and offer
access to, computer intensive analyses of genomic data. In this study, the Galaxy and
Ruffus frameworks were designed and implemented with a view to address the
challenges faced in biomedical research. Galaxy, a graphical web-based framework,
allows researchers to build a graphical NGS data analysis pipeline for accessible,
reproducible, and collaborative data-sharing. Ruffus, a UNIX command-line framework
used by bioinformaticians as Python library to write scripts in object-oriented style,
allows for building a workflow in terms of task dependencies and execution logic. In
this study, a dual data analysis technique was explored which focuses on a comparative
evaluation of Galaxy and Ruffus frameworks that are used in composing analysis
pipelines. To this end, we developed an analysis pipeline in Galaxy, and Ruffus, for the
analysis of Mycobacterium tuberculosis sequence data. Furthermore, this study aimed
to compare the Galaxy framework to Ruffus with preliminary analysis revealing that the
analysis pipeline in Galaxy displayed a higher percentage of load and store instructions.
In comparison, pipelines in Ruffus tended to be CPU bound and memory intensive. The
CPU usage, memory utilization, and runtime execution are graphically represented in
this study. Our evaluation suggests that workflow frameworks have distinctly different
features from ease of use, flexibility, and portability, to architectural designs
Science 2.0 : sharing scientific data on the Grid
Mestrado em Engenharia de Computadores e TelemáticaA computação assumese
cada vez mais como um recurso essencial em
ciência, levando ao surgimento do termo eCiência
para designar a utilização
de tecnologias de computação avançada para suportar a realização de
experiências científicas, a preservação e partilha do conhecimento.
Uma das áreas de aplicação do conceito de eCiência
é o tratamento e análise
de imagens médicas. Os processos que lidam com imagem médica, tanto ao
nível clínico como de investigação, são exigentes em relação ao suporte
computacional, devido aos algoritmos de processamento de imagem que
requerem e à elevada capacidade de armazenamento relacionada com volume
das imagens geradas.
As políticas públicas e os avanços tecnológicos recentes orientados para a eCiência,
têm vindo a apoiar o desenvolvimento da computação em Grid, tanto
a nível dos middlewares como da instalação de capacidade de produção, como
um sistema de computação avançado que permite a partilha de recursos,
instrumentos científicos e boas práticas em comunidades virtuais.
Este trabalho tem como objectivo desenvolver uma estratégia e um protótipo
para o armazenamento de dados médicos na Grid, visando a sua utilização em
investigação. Uma preocupação diferenciadora prendese
com o objectivo de
colocar as potencialidades da Grid ao serviço de utilizadores não técnicos (e.g.
médicos, investigadores), que acedem a serviços de processamento e de
armazenamento e catalogação de dados de forma transparente, através de um
portal Web.
O protótipo desenvolvido permite a investigadores na área das neurociências,
sem conhecimentos específicos da tecnologia Grid, armazenar imagens e
analisálas
em Grids de produção existentes, baseadas no middleware gLite.
ABSTRACT: Computing has become an essential tool in modern science, leading to the
appearance of the term eScience
to designate the usage of advanced
computing technologies to support the execution of scientific experiments, and
the preservation and sharing of knowledge.
One of eScience
domain areas is the medical imaging analysis. The processes
that deal with medical images, both at clinical and investigation level, are very
demanding in terms of computational support, due to the analysis algorithms
that involve large volumes of generated images, requiring high storage
capabilities.
The recent public policies and technological advances are eScience
oriented,
and have been supporting the development of Grid computing, both at the
middleware level and at the installation of production capabilities in an
advanced computing system, that allows the sharing of resources, scientific
instrumentation and good practices among virtual communities.
The main objective of this work is to develop a strategy and a prototype to allow
the storage of medical data on the Grid, targeting a research environment. The
differentiating concern of this work is the ability to provide the nonexperts
users (e.g: doctors, researchers) access to the Grid services, like storage and
processing, through a friendly Web interface.
The developed prototype allows researchers in the field of neuroscience,
without any specific knowledge of Grid technology, to store images and analyse
them in production Grid infrastructures, based on the gLite middleware
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