Server-side navigation service benchmarking tool

908

Budget Optimization

Bakalářská práce má za cíl přiblížit problematiku tvorby rozpočtu veřejné vysoké školy a následnou tvorbu matematického modelu. V práci jsou vysvětlena pravidla a vzorce pro rozdělování finančních prostředků vysokého školství jednotlivým vysokým školám. Poté jsou uvedeny vzorce na přerozdělování těchto dotací mezi jednotlivé fakulty. Následně je sestaven matematický model nelineárního programování v systému GAMS pomocí reálných dat a omezení. Model je poté použit na zkoumání změny rozdělení financí pro různé účelové funkce. Cílem sestavení modelu nebylo nabídnout nástroj, který bude automaticky používán pro rozdělování dotací na VUT, ale poskytnout jeho uživatelům širší možnosti výpočtových experimentů a získat lepší vhled do problému.The bachelor's thesis aims to approach the issue of creating a budget for a public university and the subsequent creation of a mathematical model. The thesis explains the rules and formulas for the distribution of funds for higher education to individual universities. Then, the formulas for the redistribution of these funds between individual faculties are given. Subsequently, a mathematical model of nonlinear programming in the GAMS system is built using real data and constraints. The model is then used to examine the change in the distribution of funds for various objective functions. The aim of compiling the model was not to offer a tool that will be automatically used for the distribution of funds at BUT, but to provide its users with a wider range of computational experiments and gain better insight into the problem.

An Efficient Monte Carlo-based Probabilistic Time-Dependent Routing Calculation Targeting a Server-Side Car Navigation System

Incorporating speed probability distribution to the computation of the route planning in car navigation systems guarantees more accurate and precise responses. In this paper, we propose a novel approach for dynamically selecting the number of samples used for the Monte Carlo simulation to solve the Probabilistic Time-Dependent Routing (PTDR) problem, thus improving the computation efficiency. The proposed method is used to determine in a proactive manner the number of simulations to be done to extract the travel-time estimation for each specific request while respecting an error threshold as output quality level. The methodology requires a reduced effort on the application development side. We adopted an aspect-oriented programming language (LARA) together with a flexible dynamic autotuning library (mARGOt) respectively to instrument the code and to take tuning decisions on the number of samples improving the execution efficiency. Experimental results demonstrate that the proposed adaptive approach saves a large fraction of simulations (between 36% and 81%) with respect to a static approach while considering different traffic situations, paths and error requirements. Given the negligible runtime overhead of the proposed approach, it results in an execution-time speedup between 1.5x and 5.1x. This speedup is reflected at infrastructure-level in terms of a reduction of around 36% of the computing resources needed to support the whole navigation pipeline

Precision-Aware application execution for Energy-optimization in HPC node system

Power consumption is a critical consideration in high performance computing systems and it is becoming the limiting factor to build and operate Petascale and Exascale systems. When studying the power consumption of existing systems running HPC workloads, we find that power, energy and performance are closely related which leads to the possibility to optimize energy consumption without sacrificing (much or at all) the performance. In this paper, we propose a HPC system running with a GNU/Linux OS and a Real Time Resource Manager (RTRM) that is aware and monitors the healthy of the platform. On the system, an application for disaster management runs. The application can run with different QoS depending on the situation. We defined two main situations. Normal execution, when there is no risk of a disaster, even though we still have to run the system to look ahead in the near future if the situation changes suddenly. In the second scenario, the possibilities for a disaster are very high. Then the allocation of more resources for improving the precision and the human decision has to be taken into account. The paper shows that at design time, it is possible to describe different optimal points that are going to be used at runtime by the RTOS with the application. This environment helps to the system that must run 24/7 in saving energy with the trade-off of losing precision. The paper shows a model execution which can improve the precision of results by 65% in average by increasing the number of iterations from 1e3 to 1e4. This also produces one order of magnitude longer execution time which leads to the need to use a multi-node solution. The optimal trade-off between precision vs. execution time is computed by the RTOS with the time overhead less than 10% against a native execution

Chapter 2 The LEXIS Platform for Distributed Workflow Execution and Data Management

Artificial Intelligence, Deep Learning, Machine Learning, Supercomputin

Chapter 4 Data System and Data Management in a Federation of HPC/ Cloud Centers

Artificial Intelligence, Deep Learning, Machine Learning, Supercomputin

Pegasus: Performance Engineering for Software Applications Targeting HPC Systems

Developing and optimizing software applications for high performance and energy efficiency is a very challenging task, even when considering a single target machine. For instance, optimizing for multicore-based computing systems requires in-depth knowledge about programming languages, application programming interfaces, compilers, performance tuning tools, and computer architecture and organization. Many of the tasks of performance engineering methodologies require manual efforts and the use of different tools not always part of an integrated toolchain. This paper presents Pegasus, a performance engineering approach supported by a framework that consists of a source-to-source compiler, controlled and guided by strategies programmed in a Domain-Specific Language, and an autotuner. Pegasus is a holistic and versatile approach spanning various decision layers composing the software stack, and exploiting the system capabilities and workloads effectively through the use of runtime autotuning. The Pegasus approach helps developers by automating tasks regarding the efficient implementation of software applications in multicore computing systems. These tasks focus on application analysis, profiling, code transformations, and the integration of runtime autotuning. Pegasus allows developers to program their strategies or to automatically apply existing strategies to software applications in order to ensure the compliance of non-functional requirements, such as performance and energy efficiency. We show how to apply Pegasus and demonstrate its applicability and effectiveness in a complex case study, which includes tasks from a smart navigation system

An efficient Monte Carlo-based Probabilistic Time-Dependent Routing calculation targeting a server-side car navigation system

Incorporating speed probability distribution to the computation of the route planning in car navigation systems guarantees more accurate and precise responses. In this paper, we propose a novel approach for selecting dynamically the number of samples used for the Monte Carlo simulation to solve the Probabilistic Time-Dependent Routing (PTDR) problem, thus improving the computation efficiency. The proposed method is used to determine in a proactive manner the number of simulations to be done to extract the travel-time estimation for each specific request, while respecting an error threshold as output quality level. The methodology requires a reduced effort on the application development side. We adopted an aspect-oriented programming language (LARA) together with a flexible dynamic autotuning library (mARGOt) respectively to instrument the code and to make decisions on tuning the number of samples to improve the execution efficiency. Experimental results demonstrate that the proposed adaptive approach saves a large fraction of simulations (between 36 and 81 percent) with respect to a static approach, while considering different traffic situations, paths and error requirements. Given the negligible runtime overhead of the proposed approach, the execution-time speedup is between 1.5x and 5.1x. This speedup is reflected at the infrastructure-level in terms of a reduction of 36 percent of the computing resources needed to support the whole navigation pipeline.Web of Science921019100

Spolehlivost elektrických sítí v návazosti na databázi poruch a výpadků

Import 04/02/2009Prezenční451 - Katedra elektroenergetikyNeuveden