X-MAP A Performance Prediction Tool for Porting Algorithms and Applications to Accelerators

Most modern high-performance computing systems comprise of one or more accelerators with varying architectures in addition to traditional multicore Central Processing Units (CPUs). Examples of these accelerators include Graphic Processing Units (GPU) and Intel’s Many Integrated Cores architecture called Xeon Phi (PHI). These architectures provide massive parallel computation capabilities, which provide substantial performance benefits over traditional CPUs for a variety of scientific applications. We know that all accelerators are not similar because each of them has their own unique architecture. This difference in the underlying architecture plays a crucial role in determining if a given accelerator will provide a significant speedup over its competition. In addition to the architecture itself, one more differentiating factor for these accelerators is the programming language used to program them. For example, Nvidia GPUs can be programmed using Compute Unified Device Architecture (CUDA) and OpenCL while Intel Xeon PHIs can be programmed using OpenMP and OpenCL. The choice of programming language also plays a critical role in the speedup obtained depending on how close the language is to the hardware in addition to the level of optimization. With that said, it is thus very difficult for an application developer to choose the ideal accelerator to achieve the best possible speedup. In light of this, we present an easy to use Graphical User Interface (GUI) Tool called X-MAP which is a performance prediction tool for porting algorithms and applications to architectures which encompasses a Machine Learning based inference model to predict the performance of an applica-tion on a number of well-known accelerators and at the same time predict the best architecture and programming language for the application. We do this by collecting hardware counters from a given application and predicting run time by providing this data as inputs to a Neural Network Regressor based inference model. We predict the architecture and associated programming language by pro viding the hardware counters as inputs to an inference model based on Random Forest Classification Model. Finally, with a mean absolute prediction error of 8.52 and features such as syntax high-lighting for multiple programming languages, a function-wise breakdown of the entire application to understand bottlenecks and the ability for end users to submit their own prediction models to further improve the system, makes X-MAP a unique tool that has a significant edge over existing performance prediction solutions

Understanding Impact of Twitter Feed on Bitcoin Price and Trading Patterns

‘‘Cryptocurrency trading was one of the most exciting jobs of 2017’’. ‘‘Bit- coin’’,‘‘Blockchain’’, ‘‘Bitcoin Trading’’ were the most searched words in Google during 2017. High return on investment has attracted many people towards this crypto market. Existing research has shown that the trading price is completely based on speculation, and its trading volume is highly impacted by news media. This paper discusses the existing work to evaluate the sentiment and price of the cryptocurrency, the issues with the current trading models. It builds possible solutions to understand better the semantic orientation of text by comparing different machine learning techniques and predicts Bitcoin trading price based on Twitter feed sentiment and additional Bitcoin metrics. We observe that the statistical machine learning model was able to better predict the sentiment of Twitter tweet feed compared to the advanced BERT model. Using Twitter feed sentiment and additional Bitcoin metrics, we were able to improve the prediction of bitcoin price compared to only using bitcoin’s previous day closing pricing

Evaluation of explosives using ground vibration criterion

Recent times have experienced an increase in infrastructure and mineral resource developments. As a result, mining activities have also increased to supply the needed mineral. Blasting has been the main technique for loosening insitu rock before use. Consequently there is a growing concern of the effects of blasting activities on the environment. These effects are normally nuisances to the neighboring residence as they come in the form of: dust, toxic gases, noise, fly rocks and ground vibration. Of the set of nuisances the one that is of most concern is ground vibrations which can cause damage to structures. In most cases worldwide, after blasting activities there are the usual complaints about damage to residence, and less mining activities which is also a focus of the thesis. A study was conducted to evaluate the effect of heavy blasting in open-pit coal mines on the stability of adjoining under ground coal mine workings. There have been researches on the subject of ground vibrations to help refute some of these complaints. The works of Lewis Oriard and Charles Dowding are the foundation on which standards and regulations are built as guides to assist blasters in the prevention of creating unnecessary nuisances. Most countries have developed their own regulations with respect to blasting and parameters are set according to the geological conditions. This is of importance as the rock structures determine the transmission of the peak particle velocity. However, most countries in the west adopt standards similar to ones put forward by the United States Bureau of Mines. It my opinion that a whole scale adoption should not take place as the criteria used may not be suitable for other countries’ geological conditions. For this thesis the aim was to identify a vibration level that will not cause damage to structures close to a mining area and increase production by effective blasting. Based on the literature review it was revealed that there are a number of parameters that needed to be considered. These ranges: construction material, age of structures, distance from structures, geology of the location, type and quantities of explosives and the blast design. There was also the review of standards to building threshold with respect to the level of ground vibration. The case study with its main focus on evaluation of explosive using ground vibration criterion which will not result in any form of damage to the structures. However, having established a PPV limit using the USBM that appears reasonable there is the need for criteria similar to those of the USBM using blasting and geological conditions

Simulation of a Himalayan cloudburst event

Intense rainfall often leads to floods and landslides in the Himalayan region even with rainfall amounts that are considered comparatively moderate over the plains; for example, ‘cloudbursts’, which are devastating convective phenomena producing sudden high-intensity rainfall (∼10 cm per hour) over a small area. Early prediction and warning of such severe local weather systems is crucial to mitigate societal impact arising from the accompanying flash floods. We examine a cloudburst event in the Himalayan region at Shillagarh village in the early hours of 16 July 2003. The storm lasted for less than half an hour, followed by flash floods that affected hundreds of people. We examine the fidelity of MM5 configured with multiple-nested domains (81, 27, 9 and 3 km grid-resolution) for predicting a cloudburst event with attention to horizontal resolution and the cloud microphysics parameterization. The MM5 model predicts the rainfall amount 24 hours in advance. However, the location of the cloudburst is displaced by tens of kilometer

AstraZeneca Internship: Comparison of Chinese and Indian Contract Research & Manufacturing Organisations

AstraZeneca Internship: Comparison of Chinese and Indian Contract Research & Manufacturing Organisation

The effect of doubled CO2 and model basic state biases on the monsoon-ENSO system. I: Mean response and interannual variability

The impact of doubled CO2 concentration on the Asian summer monsoon is studied using a coupled ocean-atmosphere model. Both the mean seasonal precipitation and interannual monsoon variability are found to increase in the future climate scenario presented. Systematic biases in current climate simulations of the coupled system prevent accurate representation of the monsoon-ENSO teleconnection, of prime importance for seasonal prediction and for determining monsoon interannual variability. By applying seasonally varying heat flux adjustments to the tropical Pacific and Indian Ocean surface in the future climate simulation, some assessment can be made of the impact of systematic model biases on future climate predictions. In simulations where the flux adjustments are implemented, the response to climate change is magnified, with the suggestion that systematic biases may be masking the true impact of increased greenhouse gas forcing. The teleconnection between ENSO and the Asian summer monsoon remains robust in the future climate, although the Indo-Pacific takes on more of a biennial character for long periods of the flux-adjusted simulation. Assessing the teleconnection across interdecadal timescales shows wide variations in its amplitude, despite the absence of external forcing. This suggests that recent changes in the observed record cannot be distinguished from internal variations and as such are not necessarily related to climate change

A comparison of two statistical postprocessing methods for heavy‐precipitation forecasts over India during the summer monsoon

Accurate ensemble forecasts of heavy precipitation in India are vital for many applications and essential for early warning of damaging flood events, especially during the monsoon season. In this study we investigate to what extent Quantile Mapping (QM) and Ensemble Model Output Statistics (EMOS) statistical postprocessing reduce errors in precipitation ensemble forecasts over India, in particular for heavy precipitation. Both methods are applied to day‐1 forecasts at 12‐km resolution from the 23‐member National Centre for Medium Range Weather Forecasting (NCMRWF) global ensemble prediction system (NEPS‐G). By construction, QM leads to distributions close to the observed ones, while EMOS optimizes the ensemble spread, and it is not a priori clear which is better suited for practical applications. The methods are therefore compared with respect to several key aspects of the forecasts: local distributions, ensemble spread, and skill for forecasting precipitation amounts and the exceedance of heavy‐precipitation thresholds. The evaluation includes rank histograms, Continuous Ranked Probability Skill Scores (CRPSS), Brier Skill Scores (BSS), reliability diagrams, and receiver operating characteristic. EMOS performs best not only with respect to correcting under‐ or overdispersive ensembles, but also in terms of forecast skill for precipitation amounts and heavy precipitation events, with positive CRPSS and BSS in most regions (both up to about 0.4 in some areas), while QM in many regions performs worse than the raw forecast. QM performs best with respect to the overall local precipitation distributions. Which aspects of the forecasts are most relevant depends to some extent on how the forecasts are used. If the main criteria are the correction of under‐ or overdispersion, forecast reliability, match between the forecasted distribution for individual days and observations (CRPSS), and the skill in forecasting heavy‐precipitation events (BSS), then EMOS is the better choice for postprocessing NEPS‐G forecasts for short lead times

Skills of different mesoscale models over Indian region during monsoon season: Forecast errors

Performance of four mesoscale models namely, the MM5, ETA, RSM and WRF, run at NCMRWF for short range weather forecasting has been examined during monsoon-2006. Evaluation is carried out based upon comparisons between observations and day-1 and day-3 forecasts of wind, temperature, speci.c humidity, geopotential height, rainfall, systematic errors, root mean square errors and specific events like the monsoon depressions. It is very difficult to address the question of which model performs best over the Indian region? An honest answer is 'none'. Perhaps an ensemble approach would be the best. However, if we must make a final verdict, it can be stated that in general, (i) the WRF is able to produce best All India rainfall prediction compared to observations in the day-1 forecast and, the MM5 is able to produce best All India rainfall forecasts in day-3, but ETA and RSM are able to depict the best distribution of rainfall maxima along the west coast of India, (ii) the MM5 is able to produce least RMSE of wind and geopotential fields at most of the time, and (iii) the RSM is able to produce least errors in the day-1 forecasts of the tracks, while the ETA model produces least errors in the day-3 forecasts

Heavy rainfall episode over Mumbai on 26 July 2005: Assessment of NWP guidance

In the present work a qualitative assessment of guidance from NCMRWF operational global and regional Numerical Weather Prediction (NWP) systems in the episode of unprecedented rainfall over Mumbai has been attempted. This also consolidates and examines the predictions that were provided by some of the leading global operational centres. Some hindcast runs were also made with different initial conditions. It reveals that the use of very high resolution global and regional models with advanced data assimilation techniques (4D Var), that optimally utilizes information from satellite observations, could significantly enhance the usefulness of NWP guidance

The role of the basic state in the ENSO-monsoon relationship and implications for predictability

The impact of systematic model errors on a coupled simulation of the Asian Summer monsoon and its interannual variability is studied. Although the mean monsoon climate is reasonably well captured, systematic errors in the equatorial Pacific mean that the monsoon-ENSO teleconnection is rather poorly represented in the GCM. A system of ocean-surface heat flux adjustments is implemented in the tropical Pacific and Indian Oceans in order to reduce the systematic biases. In this version of the GCM, the monsoon-ENSO teleconnection is better simulated, particularly the lag-lead relationships in which weak monsoons precede the peak of El Nino. In part this is related to changes in the characteristics of El Nino, which has a more realistic evolution in its developing phase. A stronger ENSO amplitude in the new model version also feeds back to further strengthen the teleconnection. These results have important implications for the use of coupled models for seasonal prediction of systems such as the monsoon, and suggest that some form of flux correction may have significant benefits where model systematic error compromises important teleconnections and modes of interannual variability