STUDY OF PARALLEL MONTE CARLO SIMULATION TECHNIQUES

Slides:



Advertisements
Similar presentations
Project M.AI.S. Multi-threaded AI system Per Erskjäns game engineer.
Advertisements

NCeSS e-Stat quantitative node Prof. William Browne & Prof. Jon Rasbash University of Bristol.
Combining Monte Carlo Estimators If I have many MC estimators, with/without various variance reduction techniques, which should I choose?
1 Presenter: Chien-Chih Chen. 2 Dynamic Scheduler for Multi-core Systems Analysis of The Linux 2.6 Kernel Scheduler Optimal Task Scheduler for Multi-core.
SE503 Advanced Project Management Dr. Ahmed Sameh, Ph.D. Professor, CS & IS Project Uncertainty Management.
Approaches to Data Acquisition The LCA depends upon data acquisition Qualitative vs. Quantitative –While some quantitative analysis is appropriate, inappropriate.
1 Lecture 12 Monte Carlo methods in parallel computing Parallel Computing Fall 2008.
Pricing an Option Monte Carlo Simulation. We will explore a technique, called Monte Carlo simulation, to numerically derive the price of an option or.
Nonlinear Stochastic Programming by the Monte-Carlo method Lecture 4 Leonidas Sakalauskas Institute of Mathematics and Informatics Vilnius, Lithuania EURO.
Oregon State Mathematics Assessment By Sandy Kralovec.
Problem 1 Given a high-resolution computer image of a map of an irregularly shaped lake with several islands, determine the water surface area. Assume.
Performance Evaluation of Hybrid MPI/OpenMP Implementation of a Lattice Boltzmann Application on Multicore Systems Department of Computer Science and Engineering,
Negative-mass electronic transport in Gallium Nitride using analytic approximations in Monte-Carlo Simulations Daniel R. Naylor*, Angela Dyson* & Brian.
Independent Study of Parallel Programming Languages An Independent Study By: Haris Ribic, Computer Science - Theoretical Independent Study Advisor: Professor.
File System Benchmarking
1 Hybrid methods for solving large-scale parameter estimation problems Carlos A. Quintero 1 Miguel Argáez 1 Hector Klie 2 Leticia Velázquez 1 Mary Wheeler.
WSEAS AIKED, Cambridge, Feature Importance in Bayesian Assessment of Newborn Brain Maturity from EEG Livia Jakaite, Vitaly Schetinin and Carsten.
Financial Services Developer Conference Excel Solutions with CCS Antonio Zurlo Technology Specialist HPC Microsoft Corporation.
A SCALABLE LIBRARY FOR PSEUDORANDOM NUMBER GENERATION ALGORITHM 806: SPRNG.
Lecture 8. Profiling - for Performance Analysis - Prof. Taeweon Suh Computer Science Education Korea University COM503 Parallel Computer Architecture &
Monte Carlo Simulation and Personal Finance Jacob Foley.
MCSL Monte Carlo simulation language Diego Garcia Eita Shuto Yunling Wang Chong Zhai.
Trace Generation to Simulate Large Scale Distributed Application Olivier Dalle, Emiio P. ManciniMar. 8th, 2012.
Study on Genetic Network Programming (GNP) with Learning and Evolution Hirasawa laboratory, Artificial Intelligence section Information architecture field.
Genetic Programming on General Purpose Graphics Processing Units (GPGPGPU) Muhammad Iqbal Evolutionary Computation Research Group School of Engineering.
1 SMU EMIS 7364 NTU TO-570-N Inferences About Process Quality Updated: 2/3/04 Statistical Quality Control Dr. Jerrell T. Stracener, SAE Fellow.
Analysis of Exchange Ratio for Exchange Monte Carlo Method Kenji Nagata, Sumio Watanabe Tokyo Institute of Technology Japan.
Computer Science, Software Engineering & Robotics Workshop, FGCU, April 27-28, 2012 Fault Prediction with Particle Filters by David Hatfield mentors: Dr.
Monte Carlo Methods1 T Special Course In Information Science II Tomas Ukkonen
© K. Cuthbertson, D. Nitzsche FINANCIAL ENGINEERING: DERIVATIVES AND RISK MANAGEMENT (J. Wiley, 2001) K. Cuthbertson and D. Nitzsche Lecture Pricing Interest.
1 A New Method for Composite System Annualized Reliability Indices Based on Genetic Algorithms Nader Samaan, Student,IEEE Dr. C. Singh, Fellow, IEEE Department.
Monté Carlo Simulation  Understand the concept of Monté Carlo Simulation  Learn how to use Monté Carlo Simulation to make good decisions  Learn how.
Using Math Course SLOs in the Development of PSLOs and PSLO Assessments Shannon Gracey.
Sep 08, 2009 SPEEDUP – Optimization and Porting of Path Integral MC Code to New Computing Architectures V. Slavnić, A. Balaž, D. Stojiljković, A. Belić,
MESQUITE: Mesh Optimization Toolkit Brian Miller, LLNL
02/17/10 CSCE 769 Optimization Homayoun Valafar Department of Computer Science and Engineering, USC.
Application of the MCMC Method for the Calibration of DSMC Parameters James S. Strand and David B. Goldstein The University of Texas at Austin Sponsored.
Smoothing, Sampling, and Simulation Vasileios Hatzivassiloglou University of Texas at Dallas.
1 1 Slide © 2004 Thomson/South-Western Simulation n Simulation is one of the most frequently employed management science techniques. n It is typically.
Lecture Fall 2001 Controlling Animation Boundary-Value Problems Shooting Methods Constrained Optimization Robot Control.
Simulink Continuous Library by Dr. Amin Danial Asham.
REU 2007-ParSat: A Parallel SAT Solver Christopher Earl, Mentor: Dr. Hao Zheng Department of Computer Science & Engineering Introduction Results and Conclusions.
Multi-cellular paradigm The molecular level can support self- replication (and self- repair). But we also need cells that can be designed to fit the specific.
Parallel OpenFOAM CFD Performance Studies Student: Adi Farshteindiker Advisors: Dr. Guy Tel-Zur,Prof. Shlomi Dolev The Department of Computer Science Faculty.
Generalization Performance of Exchange Monte Carlo Method for Normal Mixture Models Kenji Nagata, Sumio Watanabe Tokyo Institute of Technology.
Sub-fields of computer science. Sub-fields of computer science.
Ad Exchange optimization algorithms on advertising networks
PI: Professor Yong Zeng Department of Mathematics and Statistics
Kai Li, Allen D. Malony, Sameer Shende, Robert Bell
CST 1101 Problem Solving Using Computers
Software Testing.
Chapter 19 Monte Carlo Valuation.
Comparing Dynamic Programming / Decision Trees and Simulation Techniques BDAuU, Prof. Eckstein.
Reducing Photometric Redshift Uncertainties Through Galaxy Clustering
Optimization of Monte Carlo Integration
Analysis of Computing Options at ISU
High Performance Computing and Monte Carlo Methods
WORKFLOW PETRI NETS USED IN MODELING OF PARALLEL ARCHITECTURES
Inculcating “Parallel Programming” in UG curriculum
Rutgers Intelligent Transportation Systems (RITS) Laboratory
GENERAL VIEW OF KRATOS MULTIPHYSICS
Monte Carlo Valuation Bahattin Buyuksahin, Celso Brunetti 12/8/2018.
Using Math Course SLOs in the Development of PSLOs and PSLO Assessments Shannon Gracey.
Monte Carlo Integration Using MPI
By Brandon, Ben, and Lee Parallel Computing.
What I've done in past 6 months
11. Monte Carlo Applications
By Harsh Tiwari.
Option Pricing Black-Scholes Equation
The steps of scientific computation
Presentation transcript:

STUDY OF PARALLEL MONTE CARLO SIMULATION TECHNIQUES OBJECTIVES PROJECT FRAMEWORK FUTURE PLAN To solve practical application problems using MONTE CARLO simulation . To provide a range of possible results in cases when it is impossible to arithmetically calculate just one solution. Judging the solution obtained upon parameter of accuracy and time taken for computation To perform various processing techniques over the practical application problem and composing the results with serial and benchmarked programs. To apply Monte Carlo techniques over these application problems to reduce the average computation time. Monte Carlo techniques Acceptance-Rejection techniques Likelihood weighing Localization technique Game tree search Graphical analysis and interpretation of each technique If time permits, then we will try to apply MPI techniques also. Applications used Mathematics Estimation of ∏(pi) value. Multi-dimensional integral estimation (∫∫∫..dxdydz) Curve tracing. Random walks. Financial planning Black scholes option valuation. Autonomous robotics Maze solver. Games a) Game of Tantrix. PURPOSE Monte Carlo exploits the power of randomness and we can sometime converge very quickly. problem solved using parallelization of Monte Carlo simulation increases accuracy to a great degree, as we can combine results obtained from multiple nodes and choose the solution value PROGRESS PLATFORM Serial c code for ∏ ,multi-dimensional integral estimation. Optimizations No of samples /experiment=108. Time of execution a)Basic code 2-3 minutes. b)Optimized(parallelized file i/o)3-5 sec Accuracy=3.14156 Precision(std. dev.)=0.0000164 Linux GCC compiler I/O library parallelization support Open MP P threads Multi core system GUIDE: Dr. S. R. SATHE DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING VISVESVARAYA NATIONAL INSTITUTE OF TECHNOLOGY, NAGPUR PRAKHAR SINGH (BT09ECE055) SHEETAL BORKAR (BT09ECE003) NIPUN AGRAWAL (BT09ECE051) DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING