Experimental analysis of vectorized instructions impact on energy and power consumption under thermal design power constraints

International audienceVectorized instructions were introduced to improve the performance of applications. However, they come with an increase in the power consumption cost. As a consequence, processors are designed to limit the frequency of the processors when such instructions are used in order to maintain the thermal design power.In this paper, we study and compare the impact of thermal design power and SIMD instructions on performance, power and energy consumption of processors and memory. The study is performed on three different architectures providing different characteristics and four applications with different profiles (including one application with different phases, each phase having a different profile).The study shows that, because of processor frequency, performance and power consumption are strongly related under thermal design power. It also shows that AVX512 has unexpected behavior regarding processor power consumption, while DRAM power consumption is impacted by SIMD instructions because of the generated memory throughput

Load curve data cleansing and imputation via sparsity and low rank

The smart grid vision is to build an intelligent power network with an unprecedented level of situational awareness and controllability over its services and infrastructure. This paper advocates statistical inference methods to robustify power monitoring tasks against the outlier effects owing to faulty readings and malicious attacks, as well as against missing data due to privacy concerns and communication errors. In this context, a novel load cleansing and imputation scheme is developed leveraging the low intrinsic-dimensionality of spatiotemporal load profiles and the sparse nature of "bad data.'' A robust estimator based on principal components pursuit (PCP) is adopted, which effects a twofold sparsity-promoting regularization through an $\ell_1$-norm of the outliers, and the nuclear norm of the nominal load profiles. Upon recasting the non-separable nuclear norm into a form amenable to decentralized optimization, a distributed (D-) PCP algorithm is developed to carry out the imputation and cleansing tasks using networked devices comprising the so-termed advanced metering infrastructure. If D-PCP converges and a qualification inequality is satisfied, the novel distributed estimator provably attains the performance of its centralized PCP counterpart, which has access to all networkwide data. Computer simulations and tests with real load curve data corroborate the convergence and effectiveness of the novel D-PCP algorithm.Comment: 8 figures, submitted to IEEE Transactions on Smart Grid - Special issue on "Optimization methods and algorithms applied to smart grid

Computational Aspects of Heat Transfer in Structures

Techniques for the computation of heat transfer and associated phenomena in complex structures are examined with an emphasis on reentry flight vehicle structures. Analysis methods, computer programs, thermal analysis of large space structures and high speed vehicles, and the impact of computer systems are addressed

Scalability of preconditioners as a strategy for parallel computation of compressible fluid flow

Parallel implementations of a Newton-Krylov-Schwarz algorithm are used to solve a model problem representing low Mach number compressible fluid flow over a backward-facing step. The Mach number is specifically selected to result in a numerically {open_quote}stiff{close_quotes} matrix problem, based on an implicit finite volume discretization of the compressible 2D Navier-Stokes/energy equations using primitive variables. Newton`s method is used to linearize the discrete system, and a preconditioned Krylov projection technique is used to solve the resulting linear system. Domain decomposition enables the development of a global preconditioner via the parallel construction of contributions derived from subdomains. Formation of the global preconditioner is based upon additive and multiplicative Schwarz algorithms, with and without subdomain overlap. The degree of parallelism of this technique is further enhanced with the use of a matrix-free approximation for the Jacobian used in the Krylov technique (in this case, GMRES(k)). Of paramount interest to this study is the implementation and optimization of these techniques on parallel shared-memory hardware, namely the Cray C90 and SGI Challenge architectures. These architectures were chosen as representative and commonly available to researchers interested in the solution of problems of this type. The Newton-Krylov-Schwarz solution technique is increasingly being investigated for computational fluid dynamics (CFD) applications due to the advantages of full coupling of all variables and equations, rapid non-linear convergence, and moderate memory requirements. A parallel version of this method that scales effectively on the above architectures would be extremely attractive to practitioners, resulting in efficient, cost-effective, parallel solutions exhibiting the benefits of the solution technique

Geometric data understanding : deriving case specific features

There exists a tradition using precise geometric modeling, where uncertainties in data can be considered noise. Another tradition relies on statistical nature of vast quantity of data, where geometric regularity is intrinsic to data and statistical models usually grasp this level only indirectly. This work focuses on point cloud data of natural resources and the silhouette recognition from video input as two real world examples of problems having geometric content which is intangible at the raw data presentation. This content could be discovered and modeled to some degree by such machine learning (ML) approaches like deep learning, but either a direct coverage of geometry in samples or addition of special geometry invariant layer is necessary. Geometric content is central when there is a need for direct observations of spatial variables, or one needs to gain a mapping to a geometrically consistent data representation, where e.g. outliers or noise can be easily discerned. In this thesis we consider transformation of original input data to a geometric feature space in two example problems. The first example is curvature of surfaces, which has met renewed interest since the introduction of ubiquitous point cloud data and the maturation of the discrete differential geometry. Curvature spectra can characterize a spatial sample rather well, and provide useful features for ML purposes. The second example involves projective methods used to video stereo-signal analysis in swimming analytics. The aim is to find meaningful local geometric representations for feature generation, which also facilitate additional analysis based on geometric understanding of the model. The features are associated directly to some geometric quantity, and this makes it easier to express the geometric constraints in a natural way, as shown in the thesis. Also, the visualization and further feature generation is much easier. Third, the approach provides sound baseline methods to more traditional ML approaches, e.g. neural network methods. Fourth, most of the ML methods can utilize the geometric features presented in this work as additional features.Geometriassa käytetään perinteisesti tarkkoja malleja, jolloin datassa esiintyvät epätarkkuudet edustavat melua. Toisessa perinteessä nojataan suuren datamäärän tilastolliseen luonteeseen, jolloin geometrinen säännönmukaisuus on datan sisäsyntyinen ominaisuus, joka hahmotetaan tilastollisilla malleilla ainoastaan epäsuorasti. Tämä työ keskittyy kahteen esimerkkiin: luonnonvaroja kuvaaviin pistepilviin ja videohahmontunnistukseen. Nämä ovat todellisia ongelmia, joissa geometrinen sisältö on tavoittamattomissa raakadatan tasolla. Tämä sisältö voitaisiin jossain määrin löytää ja mallintaa koneoppimisen keinoin, esim. syväoppimisen avulla, mutta joko geometria pitää kattaa suoraan näytteistämällä tai tarvitaan neuronien lisäkerros geometrisia invariansseja varten. Geometrinen sisältö on keskeinen, kun tarvitaan suoraa avaruudellisten suureiden havainnointia, tai kun tarvitaan kuvaus geometrisesti yhtenäiseen dataesitykseen, jossa poikkeavat näytteet tai melu voidaan helposti erottaa. Tässä työssä tarkastellaan datan muuntamista geometriseen piirreavaruuteen kahden esimerkkiohjelman suhteen. Ensimmäinen esimerkki on pintakaarevuus, joka on uudelleen virinneen kiinnostuksen kohde kaikkialle saatavissa olevan datan ja diskreetin geometrian kypsymisen takia. Kaarevuusspektrit voivat luonnehtia avaruudellista kohdetta melko hyvin ja tarjota koneoppimisessa hyödyllisiä piirteitä. Toinen esimerkki koskee projektiivisia menetelmiä käytettäessä stereovideosignaalia uinnin analytiikkaan. Tavoite on löytää merkityksellisiä paikallisen geometrian esityksiä, jotka samalla mahdollistavat muun geometrian ymmärrykseen perustuvan analyysin. Piirteet liittyvät suoraan johonkin geometriseen suureeseen, ja tämä helpottaa luonnollisella tavalla geometristen rajoitteiden käsittelyä, kuten väitöstyössä osoitetaan. Myös visualisointi ja lisäpiirteiden luonti muuttuu helpommaksi. Kolmanneksi, lähestymistapa suo selkeän vertailumenetelmän perinteisemmille koneoppimisen lähestymistavoille, esim. hermoverkkomenetelmille. Neljänneksi, useimmat koneoppimismenetelmät voivat hyödyntää tässä työssä esitettyjä geometrisia piirteitä lisäämällä ne muiden piirteiden joukkoon

Improving optical access, sampling speed, and resolution for in vivo multiphoton microscopy

Multiphoton microscopy is a powerful optical imaging modality renowned for its non-invasive nature and relatively affordable characteristics. In particular, it has found its niche in neuroimaging due to its ability to probe in vivo biological processes in scattering brain tissue approaching millimeter depths with cellular resolution. However, the brain is a large and complex organ, and in order to fully understand its heterogeneous architecture and associated functional roles, several distal regions must be imaged simultaneously. Moreover, due to the critical implications of organelle features in various macroscale processes, whole-brain imaging at subcellular resolution scales presents itself as one of the outstanding challenges faced by the neuroscientific community today. Primarily, this research aims to expand the depth, field-of-view, and temporal throughput of multiphoton microscopy to enable large volume imaging of microvasculature at greater acquisition speeds. To accomplish this, we combine multi-faceted efforts focused on the engineering and development of advanced multiphoton microscopy techniques and technologies. This includes the characterization of novel contrast agents, the optimization of scan system optics, and the integration of high-repetition rate lasers with a resonant galvanometer. In addition, we develop a two-color imaging system capable of enhancing excitation efficiency, improving signal-to- background ratio, and further extending imaging depth. Finally, we present a novel application for two-color non-degenerate mode mixing to effectively circumvent the diffraction-limited nature of optical resolution and enable subcellular imaging. Collectively, these efforts advance the state-of-the art of multiphoton microscopy for routine cerebrovascular and neuroimaging.Biomedical Engineerin

CYBER 200 Applications Seminar

Applications suited for the CYBER 200 digital computer are discussed. Various areas of application including meteorology, algorithms, fluid dynamics, monte carlo methods, petroleum, electronic circuit simulation, biochemistry, lattice gauge theory, economics and ray tracing are discussed

Learning-Based Data-Driven and Vision Methodology for Optimized Printed Electronics

Inkjet printing is an active domain of additive manufacturing and printed electronics due to its promising features, starting from low-cost, scalability, non-contact printing, and microscale on-demand pattern customization. Up until now, mainstream research has been making headway in the development of ink material and printing process optimization through traditional methods, with almost no work concentrated on machine learning and vision-based drop behavior prediction, pattern generation, and enhancement. In this work, we first carry out a systematic piezoelectric drop on demand inkjet drop generation and characterization study to structure our dataset, which is later used to develop a drop formulation prediction module for diverse materials. Machine learning enables us to predict the drop speed and radius for particular material and printer electrical signal configuration. We verify our prediction results with untested graphene oxide ink. Thereafter, we study automated pattern generation and evaluation algorithms for inkjet printing via computer vision schema for several shapes, scales and finalize the best sequencing method in terms of comparative pattern quality, along with the underlying causes. In a nutshell, we develop and validate an automated vision methodology to optimize any given two-dimensional patterns. We show that traditional raster printing is inferior to other promising methods such as contour printing, segmented matrix printing, depending on the shape and dimension of the designed pattern. Our proposed vision-based printing algorithm eliminates manual printing configuration workload and is intelligent enough to decide on which segment of the pattern should be printed in which order and sequence. Besides, process defect monitoring and tracking has shown promising results equivalent to manual short circuit, open circuit, and sheet resistance testing for deciding over pattern acceptance or rejection with reduced device testing time. Drop behavior forecast, automatic pattern optimization, and defect quantization compared with the designed image allow dynamic adaptation of any materials properties with regards to any substrate and sophisticated design as established here with varying material properties; complex design features such as corners, edges, and miniature scale can be achieved