Studio e valutazione di tecniche di ottimizzazione delle prestazioni per memorie cache in sistemi CMP

Il mio lavoro di tesi si propone di studiare e analizzare tecniche di ottimizzazione per memorie cache di secondo livello in sistemi CMP. In questi sistemi, le comunicazioni tra processori e tra processori e memorie cache sono un collo di bottiglia. Tramite l'utilizzo delle cache NUCA (Non Uniform Cache Access) si riesce ad abbattere tale problema. Occorre però studiare sempre nuovi protocolli di coerenza mirati all'abbassamento del tempo di Hit in L2, ovvero il tempo che impiega la cache di secondo livello a inviare il dato alla CPU richiedente. Uno di questi protocolli è la Victim Replication, implementata nel mio lavoro di tesi su una cache di secondo livello di tipo S-NUCA. Grazie alla Victim Replication la cache L2 risponde impiegando fino al 30% di tempo in meno per alcune applicazioni. Il lavoro di tesi comprende la presentazione del protocollo, la sua implementazione e la completa analisi dei risultati ottenuti

An Examination of Changes in the Florida Seabreeze Climatology

Florida summer precipitation patterns are in part governed by the dominance of different Seabreeze regimes. In this study, we are comparing 60 years of data against itself to see if there has been a significant trend in the tendencies of these regimes. Our goal is to understand if there has been a change, what might be causing them, and if there are better ways we can define synoptic scale atmospheric conditions to better understand and predict precipitation patterns in the Southeast US

Application of Density Altitude Climatology to General Aviation Impacts

Density altitude (DA) plays a key role in flight safety because it helps pilots anticipate poor aircraft performance when temperatures are warmer than standard. In this study, a 30-year climatology of DA for the conterminous United States was created using the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis of the global climate (ERA5) dataset was applied to four separate DA-based, aircraft-performance, rules-of-thumb for general aviation (GA) flight. The goal was to demonstrate a technique to create educational visualization tools showing the variation of operational flight impacts with both month and location. Four such parameters were chosen to show the technique\u27s utility: take-off distance, landing distance, climb rate, and engine power, all of which were expressed as multipliers to be applied to the standard altitude values. The study provided results based on the 30-year (1991-2020) July mean DA values as well as those based on the maximum daily values (worst case) at each grid point occurring during the months of June, July, and August during the same period. Results showed performance parameters tended to have the most variation in the east-west direction following terrain rather than the north-south direction following the solar insolation

Adjusted Tornado Probabilities

Tornado occurrence rates computed from the available reports are biased low relative to the unknown true rates. To correct for this low bias, the authors demonstrate a method to estimate the annual probability of being struck by a tornado that uses the average report density estimated as a function of distance from nearest city/town center. The method is demonstrated on Kansas and then applied to 15 other tornado-prone states from Nebraska to Tennessee. States are ranked according to their adjusted tornado rate and comparisons are made with raw rates published elsewhere. The adjusted rates, expressed as return periods, arestates, including Alabama, Mississippi, Arkansas, and Oklahoma. The expected annual number of people exposed to tornadoes is highest for Illinois followed by Alabama and Indiana. For the four states with the highest tornado rates, exposure increases since 1980 are largest for Oklahoma (24%) and Alabama (23%)

Sensitivity of Limiting Hurricane Intensity to SST in the Atlantic from Observations and GCMs

Abstract A statistical model for the intensity of the strongest hurricanes has been developed and a new methodology introduced for estimating the sensitivity of the strongest hurricanes to changes in sea surface temperature. Here, the authors use this methodology on observed hurricanes and hurricanes generated from two global climate models (GCMs). Hurricanes over the North Atlantic Ocean during the period 1981–2010 show a sensitivity of 7.9 ± 1.19 m s−1 K−1 (standard error; SE) when over seas warmer than 25°C. In contrast, hurricanes over the same region and period generated from the GFDL High Resolution Atmospheric Model (HiRAM) show a significantly lower sensitivity with the highest at 1.8 ± 0.42 m s−1 K−1 (SE). Similar weaker sensitivity is found using hurricanes generated from the Florida State University Center for Ocean–Atmospheric Prediction Studies (FSU-COAPS) model with the highest at 2.9 ± 2.64 m s−1 K−1 (SE). A statistical refinement of HiRAM-generated hurricane intensities heightens the sensitivity to a maximum of 6.9 ± 3.33 m s−1 K−1 (SE), but the increase is offset by additional uncertainty associated with the refinement. Results suggest that the caution that should be exercised when interpreting GCM scenarios of future hurricane intensity stems from the low sensitivity of limiting GCM-generated hurricane intensity to ocean temperature.</jats:p

Application of a Hybrid Statistical–Dynamical System to Seasonal Prediction of North American Temperature and Precipitation

Recent research demonstrates that dynamical models sometimes fail to represent observed teleconnection patterns associated with predictable modes of climate variability. As a result, model forecast skill may be reduced. We address this gap in skill through the application of a Bayesian postprocessing technique—the calibration, bridging, and merging (CBaM) method—which previously has been shown to improve probabilistic seasonal forecast skill over Australia. Calibration models developed from dynamical model reforecasts and observations are employed to statistically correct dynamical model forecasts. Bridging models use dynamical model forecasts of relevant climate modes (e.g., ENSO) as predictors of remote temperature and precipitation. Bridging and calibration models are first developed separately using Bayesian joint probability modeling and then merged using Bayesian model averaging to yield an optimal forecast. We apply CBaM to seasonal forecasts of North American 2-m temperature and precipitation from the North American Multimodel Ensemble (NMME) hindcast. Bridging is done using the model-predicted Niño-3.4 index. Overall, the fully merged CBaM forecasts achieve higher Brier skill scores and better reliability compared to raw NMME forecasts. Bridging enhances forecast skill for individual NMME member model forecasts of temperature, but does not result in significant improvements in precipitation forecast skill, possibly because the models of the NMME better represent the ENSO–precipitation teleconnection pattern compared to the ENSO– temperature pattern. These results demonstrate the potential utility of the CBaM method to improve seasonal forecast skill over North America

Observed Versus GCM-Generated Local Tropical Cyclone Frequency: Comparisons Using a Spatial Lattice

Of broad scientific and public interest is the reliability of global climate models (GCMs) to simulate future regional and local tropical cyclone (TC) occurrences. Atmospheric GCMs are now able to generate vortices resembling actual TCs, but questions remain about their fidelity to observed TCs. Here the authors demonstrate a spatial lattice approach for comparing actual with simulated TC occurrences regionally using observed TCs from the International Best Track Archive for Climate Stewardship (IBTrACS) dataset and GCM-generated TCs from the Geophysical Fluid Dynamics Laboratory (GFDL) High Resolution Atmospheric Model (HiRAM) and Florida State University (FSU) Center for Ocean–Atmospheric Prediction Studies (COAPS) model over the common period 1982–2008. Results show that the spatial distribution of TCs generated by the GFDL model compares well with observations globally, although there are areas of over- and underprediction, particularly in parts of the Pacific Ocean. Difference maps using the spatial lattice highlight these discrepancies. Additionally, comparisons focusing on the North Atlantic Ocean basin are made. Results confirm a large area of overprediction by the FSU COAPS model in the south-central portion of the basin. Relevant to projections of future U.S. hurricane activity is the fact that both models underpredict TC activity in the Gulf of Mexico