39 research outputs found
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
Uncertainty Modelling in Rainfall-Runoff Models
Import 05/08/2014Cílem této práce je prozkoumat srážko-odtokové modely s ohledem na neurčitosti jejich vstupních parametrů, provést analýzy těchto vstupních parametrů na základě historických dat projektu Floreon+ a navrhnout a implementovat obecný mechanismus modelování neurčitostí srážko-odtokových modelů, který bude přizpůsobený ke spouštění na HPC.The aim of this work is to investigate the rainfall-runoff models with regard to the uncertainty of their input parameters, perform the analysis of the input parameters based on historical data provided by project Floreon+ and design and implement a general mechanism for modeling the uncertainty of rainfall-runoff models, which will be adapted to run on HPC.460 - Katedra informatikyvýborn