Type-driven automated program transformations and cost modelling for optimising streaming programs on FPGAs

In this paper we present a novel approach to program optimisation based on compiler-based type-driven program transformations and a fast and accurate cost/performance model for the target architecture. We target streaming programs for the problem domain of scientific computing, such as numerical weather prediction. We present our theoretical framework for type-driven program transformation, our target high-level language and intermediate representation languages and the cost model and demonstrate the effectiveness of our approach by comparison with a commercial toolchain

A Croatian Weather Domain Spoken Dialog System Prototype

Speech technologies and language technologies have been already in use in IT for a certain time. Because of their great impact and fast growth, it is necessary to introduce these technologies for Croatian language. In this paper we propose a solution for developing a domain-oriented spoken dialog system for Croatian language. We have chosen a weather domain because it has limited vocabulary, it has easily accessible data and it is highly applicable. The Croatian weather dialog system provides information about weather in different regions of Croatia. The modules of the spoken dialog system perform automatic word recognition, semantic analysis, dialog management, response generation and text-to-speech synthesis. This is a first attempt to develop such a system for Croatian language and some new approaches are presented

The ESCAPE project : Energy-efficient Scalable Algorithms for Weather Prediction at Exascale

In the simulation of complex multi-scale flows arising in weather and climate modelling, one of the biggest challenges is to satisfy strict service requirements in terms of time to solution and to satisfy budgetary constraints in terms of energy to solution, without compromising the accuracy and stability of the application. These simulations require algorithms that minimise the energy footprint along with the time required to produce a solution, maintain the physically required level of accuracy, are numerically stable, and are resilient in case of hardware failure. The European Centre for Medium-Range Weather Forecasts (ECMWF) led the ESCAPE (Energy-efficient Scalable Algorithms for Weather Prediction at Exascale) project, funded by Horizon 2020 (H2020) under the FET-HPC (Future and Emerging Technologies in High Performance Computing) initiative. The goal of ESCAPE was to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres, and hardware vendors. This paper presents an overview of the ESCAPE strategy: (i) identify domain-specific key algorithmic motifs in weather prediction and climate models (which we term Weather & Climate Dwarfs), (ii) categorise them in terms of computational and communication patterns while (iii) adapting them to different hardware architectures with alternative programming models, (iv) analyse the challenges in optimising, and (v) find alternative algorithms for the same scheme. The participating weather prediction models are the following: IFS (Integrated Forecasting System); ALARO, a combination of AROME (Application de la Recherche a l'Operationnel a Meso-Echelle) and ALADIN (Aire Limitee Adaptation Dynamique Developpement International); and COSMO-EULAG, a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian and semi-Lagrangian fluid solver). For many of the weather and climate dwarfs ESCAPE provides prototype implementations on different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi, Optalysys optical processor) with different programming models. The spectral transform dwarf represents a detailed example of the co-design cycle of an ESCAPE dwarf. The dwarf concept has proven to be extremely useful for the rapid prototyping of alternative algorithms and their interaction with hardware; e.g. the use of a domain-specific language (DSL). Manual adaptations have led to substantial accelerations of key algorithms in numerical weather prediction (NWP) but are not a general recipe for the performance portability of complex NWP models. Existing DSLs are found to require further evolution but are promising tools for achieving the latter. Measurements of energy and time to solution suggest that a future focus needs to be on exploiting the simultaneous use of all available resources in hybrid CPU-GPU arrangements

AEMIX: semantic verification of weather forecasts on the web

Ponencia presentada en: 12th International Conference on Web Information Systems and Technologies celebrada en Roma del 23 al 25 de abril de 2016The main objectives of a meteorological service are the development, implementation and delivery of weather forecasts. Weather predictions are broadcasted to society through different channels, i.e. newspaper, television, radio, etc. Today, the use of theWeb through personal computers and mobile devices stands out. The forecasts, which can be presented in numerical format, in charts, or in written natural language, have a certain margin of error. Providing automatic tools able to assess the precision of predictions allows to improve these forecasts, quantify the degree of success depending on certain variables (geographic areas, weather conditions, time of year, etc.), and focus future work on areas for improvement that increase such accuracy. Despite technological advances, the task of verifying forecasts written in natural language is still performed manually by people in many cases, which is expensive, time-consuming, and subjected to human errors. On the other hand, weather forecasts usually follow several conventions in both structure and use of language, which, while not completely formal, can be exploited to increase the quality of the verification. In this paper, we describe a methodology to quantify the accuracy of weather forecasts posted on the Web and based on natural language. This work obtains relevant information from weather forecasts by using ontologies to capture and take advantage of the structure and language conventions. This approach is implemented in a framework that allows to address different types of predictions with minimal effort. Experimental results with real data are promising, and most importantly, they allow direct use in a real meteorological service.This research work has been supported by the CICYT project TIN2013-46238-C4-4-R, and DGAFS

The Effects of Severe Weather Warnings on Limited English Proficient (LEP) Hispanics/Latinos in Rural Nebraska

The language barrier may severely restrict how severe weather warnings are received and responded to by Hispanics/Latinos in rural Nebraska, a state well known for frequent, volatile weather patterns. Nearly 50% of Spanish speaking Nebraskans rated their English abilities as “less than very well” (US Census Bureau, 2013). The estimated number of Hispanics/Latinos with limited English proficiency (LEP) in Nebraska equates to approximately 57,000 people. This thesis attempted to assess English ability and how severe weather warnings were received and responded to by LEP Hispanics/Latinos in rural Nebraska. This was accomplished by analysis of data from completed optional Spanish or English surveys. This study was exploratory in nature and conducted among a convenience sample of Hispanics/Latinos from five rural health departments across Nebraska. The effects of limited English proficiency revealed multiple modes of media were utilized to confirm severe weather warnings. The results of this study support the notion of needed language and culturally specific severe weather warnings for non-English speaking, or limited English proficient residents. The use of multiple modes of media to confirm severe weather in this study, may in fact delay response times for mitigating actions, which could result in potentially disastrous situations. This study demonstrates a need for more robust research on how non-English speaking residents in Nebraska receive risk communications, not only for severe weather, but all emergent notifications

Impact of an occupancy and activity based window use model on the prediction of the residential energy use and thermal comfort

The opening of windows can lead to high energy losses in wintertime, especially in nearly zero-energy buildings. But can reduce overheating significantly in summertime. Therefore, window use models have been created in the past to assess the energy use and thermal comfort in residential buildings. The models are mostly based on weather-variables. However, a recent study (Verbruggen, Janssens, et al. 2018) indicated that these models were not able to accurately predict the window use in wintertime. For that reason, an occupancy and activity based model was developed. In this article, the impact of the application of the new window opening model on the residential energy use and thermal comfort was assessed. The object-oriented modelling language Modelica was used to simulate the energy use and temperatures in a nearly-zero energy house, which is a representation of an existing house in a nearly zero-energy neighbourhood in Kortrijk. From this neighbourhood, measured energy use data was available as well as window sensor data for some of the houses. These measured data were compared to the simulated data of the new window use model, a weather-based model and the Belgian EPBD-calculation method. The occupancy and activity based model could predict more accurately the average opening durations in wintertime and could better account for the large variation in window use compared to weather-based models. An optimal window opening strategy could limit the overheating significantly, even prevent it in the bedrooms and bathroom. However, opening the windows also implies an increase in energy use for heating. Some combinations of different window opening habits can limit the overheating, while limiting the increase in energy use at the same time

Statistical Language Models for Croatian Weather-domain Corpus

Statistical language modelling estimates the regularities in natural languages. Language models are used in speech recognition, machine translation and other applications for speech and language technologies. In this paper we will present a procedure for language models building for the Croatian weather domain corpus. Different types of n-gram statistic language models and smoothing methods for language modelling are presented. Those models are compared in terms of their estimated perplexity