Avaluació de la tecnologia de les GPUs

En el projecte s’ha dut a terme un estudi sobre la tecnologia que aporten les targetes gràfiques (GPU) dins l’àmbit de programació d’aplicacions que tradicionalment eren executades en la CPU o altrament conegut com a GPGPU. S’ha fet una anàlisi profunda del marc tecnològic actual explicant part del maquinari de les targetes gràfiques i de què tracta el GPGPU. També s’han estudiat les diferents opcions que existeixen per poder realitzar els tests de rendiment que permetran avaluar el programari, quin programari està dissenyat per ser executat amb aquesta tecnologia i quin és el procediment a seguir per poder utilitzar-los. S’han efectuat diverses proves per avaluar el rendiment de programari dissenyat o compatible d’executar en la GPU, realitzant taules comparatives amb els temps de còmput. Un cop finalitzades les diferents proves del programari, es pot concloure que no tota aplicació processada en la GPU aporta un benefici. Per poder veure millores és necessari que l’aplicació reuneixi una sèrie de requisits com que disposi d’un elevat nombre d’operacions que es puguin realitzar en paral lel, que no existeixin condicionants per a l’execució de les operacions i que sigui un procés amb càlcul aritmètic intensiu.

Testing the descriptive performance of the rank-dependent utility in the domain of health profiles

Expected utility theory (EUT) has been challenged as a descriptive theoryin many contexts. The medical decision analysis context is not an exception.Several researchers have suggested that rank dependent utility theory (RDUT)may accurately describe how people evaluate alternative medical treatments.Recent research in this domain has addressed a relevant feature of RDU models-probability weighting-but to date no direct test of this theoryhas been made. This paper provides a test of the main axiomatic differencebetween EUT and RDUT when health profiles are used as outcomes of riskytreatments. Overall, EU best described the data. However, evidence on theediting and cancellation operation hypothesized in Prospect Theory andCumulative Prospect Theory was apparent in our study. we found that RDUoutperformed EU in the presentation of the risky treatment pairs in whichthe common outcome was not obvious. The influence of framing effects onthe performance of RDU and their importance as a topic for future researchis discussed.

Proving the performance of a new revenue management system

Revenue management (RM) is a complicated business process that can best be described ascontrol of sales (using prices, restrictions, or capacity), usually using software as a tool to aiddecisions. RM software can play a mere informative role, supplying analysts with formatted andsummarized data who use it to make control decisions (setting a price or allocating capacity fora price point), or, play a deeper role, automating the decisions process completely, at the otherextreme. The RM models and algorithms in the academic literature by and large concentrateon the latter, completely automated, level of functionality.A firm considering using a new RM model or RM system needs to evaluate its performance.Academic papers justify the performance of their models using simulations, where customerbooking requests are simulated according to some process and model, and the revenue perfor-mance of the algorithm compared to an alternate set of algorithms. Such simulations, whilean accepted part of the academic literature, and indeed providing research insight, often lackcredibility with management. Even methodologically, they are usually awed, as the simula-tions only test \within-model" performance, and say nothing as to the appropriateness of themodel in the first place. Even simulations that test against alternate models or competition arelimited by their inherent necessity on fixing some model as the universe for their testing. Theseproblems are exacerbated with RM models that attempt to model customer purchase behav-ior or competition, as the right models for competitive actions or customer purchases remainsomewhat of a mystery, or at least with no consensus on their validity.How then to validate a model? Putting it another way, we want to show that a particularmodel or algorithm is the cause of a certain improvement to the RM process compared to theexisting process. We take care to emphasize that we want to prove the said model as the causeof performance, and to compare against a (incumbent) process rather than against an alternatemodel.In this paper we describe a \live" testing experiment that we conducted at Iberia Airlineson a set of flights. A set of competing algorithms control a set of flights during adjacentweeks, and their behavior and results are observed over a relatively long period of time (9months). In parallel, a group of control flights were managed using the traditional mix of manualand algorithmic control (incumbent system). Such \sandbox" testing, while common at manylarge internet search and e-commerce companies is relatively rare in the revenue managementarea. Sandbox testing has an undisputable model of customer behavior but the experimentaldesign and analysis of results is less clear. In this paper we describe the philosophy behind theexperiment, the organizational challenges, the design and setup of the experiment, and outlinethe analysis of the results. This paper is a complement to a (more technical) related paper thatdescribes the econometrics and statistical analysis of the results.

Testing for multicointegration in panel data with common factors

The paper addresses the concept of multicointegration in panel data frame- work. The proposal builds upon the panel data cointegration procedures developed in Pedroni (2004), for which we compute the moments of the parametric statistics. When individuals are either cross-section independent or cross-section dependence can be re- moved by cross-section demeaning, our approach can be applied to the wider framework of mixed I(2) and I(1) stochastic processes analysis. The paper also deals with the issue of cross-section dependence using approximate common factor models. Finite sample performance is investigated through Monte Carlo simulations. Finally, we illustrate the use of the procedure investigating inventories, sales and production relationship for a panel of US industries.

Testing for multicointegration in panel data with common factors

The paper addresses the concept of multicointegration in panel data frame- work. The proposal builds upon the panel data cointegration procedures developed in Pedroni (2004), for which we compute the moments of the parametric statistics. When individuals are either cross-section independent or cross-section dependence can be re- moved by cross-section demeaning, our approach can be applied to the wider framework of mixed I(2) and I(1) stochastic processes analysis. The paper also deals with the issue of cross-section dependence using approximate common factor models. Finite sample performance is investigated through Monte Carlo simulations. Finally, we illustrate the use of the procedure investigating inventories, sales and production relationship for a panel of US industries.

Lag-one autocorrelation in short series: Estimation and hypothesis testing

In the first part of the study, nine estimators of the first-order autoregressive parameter are reviewed and a new estimator is proposed. The relationships and discrepancies between the estimators are discussed in order to achieve a clear differentiation. In the second part of the study, the precision in the estimation of autocorrelation is studied. The performance of the ten lag-one autocorrelation estimators is compared in terms of Mean Square Error (combining bias and variance) using data series generated by Monte Carlo simulation. The results show that there is not a single optimal estimator for all conditions, suggesting that the estimator ought to be chosen according to sample size and to the information available of the possible direction of the serial dependence. Additionally, the probability of labelling an actually existing autocorrelation as statistically significant is explored using Monte Carlo sampling. The power estimates obtained are quite similar among the tests associated with the different estimators. These estimates evidence the small probability of detecting autocorrelation in series with less than 20 measurement times.