CS 584 Lecture 11 l Assignment? l Paper Schedule –10 Students –5 Days –Look at the schedule and email me your preference. Quickly.

Slides:

Advertisements

Similar presentations

Analyzing Parallel Performance Intel Software College Introduction to Parallel Programming – Part 6.

Advertisements

CSE 160 – Lecture 9 Speed-up, Amdahl’s Law, Gustafson’s Law, efficiency, basic performance metrics.

Performance Measurement n Assignment? n Timing #include double When() { struct timeval tp; gettimeofday(&tp, NULL); return((double)tp.tv_sec + (double)tp.tv_usec.

Prepared 7/28/2011 by T. O’Neil for 3460:677, Fall 2011, The University of Akron.

Distributed Systems CS

Performance Analysis of Multiprocessor Architectures

1 Lecture 5: Part 1 Performance Laws: Speedup and Scalability.

Parallel System Performance CS 524 – High-Performance Computing.

11Sahalu JunaiduICS 573: High Performance Computing5.1 Analytical Modeling of Parallel Programs Sources of Overhead in Parallel Programs Performance Metrics.

Slide 1 COMP 308 Parallel Efficient Algorithms Lecturer: Dr. Igor Potapov Ashton Building, room COMP 308 web-page:

Example (1) Two computer systems have been tested using three benchmarks. Using the normalized ratio formula and the following tables below, find which.

1 COMP 206: Computer Architecture and Implementation Montek Singh Mon., Sep 5, 2005 Lecture 2.

1 Lecture 4 Analytical Modeling of Parallel Programs Parallel Computing Fall 2008.

CS 584. Logic The art of thinking and reasoning in strict accordance with the limitations and incapacities of the human misunderstanding. The basis of.

1 Tuesday, October 03, 2006 If I have seen further, it is by standing on the shoulders of giants. -Isaac Newton.

Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. Parallel Programming with MPI and OpenMP Michael J. Quinn.

CS 284a, 4 November 1997 Copyright (c) , John Thornley1 CS 284a Lecture Tuesday, 4 November, 1997.

CS 257 Database Systems Principles Assignment 2 Instructor: Student: Dr. T. Y. Lin Rajan Vyas (119)

Lecture 5 Today’s Topics and Learning Objectives Quinn Chapter 7 Predict performance of parallel programs Understand barriers to higher performance.

1 Software Testing and Quality Assurance Lecture 40 – Software Quality Assurance.

Parallel System Performance CS 524 – High-Performance Computing.

MPI Program Performance. Introduction Defining the performance of a parallel program is more complex than simply optimizing its execution time. This is.

Rechen- und Kommunikationszentrum (RZ) Parallelization at a Glance Christian Terboven / Aachen, Germany Stand: Version 2.3.

Computer Science 320 Measuring Speedup. What Is Running Time? T(N, K) says that the running time T is a function of the problem size N and the number.

Chapter 4 Performance. Times User CPU time – Time that the CPU is executing the program System CPU time – time the CPU is executing OS routines for the.

Lecture 3 – Parallel Performance Theory - 1 Parallel Performance Theory - 1 Parallel Computing CIS 410/510 Department of Computer and Information Science.

Performance Evaluation of Parallel Processing. Why Performance?

“elbowing out” Processors used Speedup Efficiency timeexecution Parallel Processors timeexecution Sequential Efficiency   

INTEL CONFIDENTIAL Predicting Parallel Performance Introduction to Parallel Programming – Part 10.

Flynn’s Taxonomy SISD: Although instruction execution may be pipelined, computers in this category can decode only a single instruction in unit time SIMD:

Amdahl's Law Validity of the single processor approach to achieving large scale computing capabilities Presented By: Mohinderpartap Salooja.

Performance Measurement n Assignment? n Timing #include double When() { struct timeval tp; gettimeofday(&tp, NULL); return((double)tp.tv_sec + (double)tp.tv_usec.

CS453 Lecture 3.  A sequential algorithm is evaluated by its runtime (in general, asymptotic runtime as a function of input size).  The asymptotic runtime.

Performance Measurement. A Quantitative Basis for Design n Parallel programming is an optimization problem. n Must take into account several factors:

Compiled by Maria Ramila Jimenez

Lecturer: Simon Winberg Lecture 18 Amdahl’s Law (+- 25 min)

CS4402 – Parallel Computing Lecture 1: Classification of Parallel Computers Classification of Parallel Computation Important Laws of Parallel Compuation.

Lecture 9 TTH 03:30AM-04:45PM Dr. Jianjun Hu CSCE569 Parallel Computing University of South Carolina Department of.

Scaling Area Under a Curve. Why do parallelism? Speedup – solve a problem faster. Accuracy – solve a problem better. Scaling – solve a bigger problem.

From lecture slides for Computer Organization and Architecture: Designing for Performance, Eighth Edition, Prentice Hall, 2010 CS 211: Computer Architecture.

Lecture 8: 9/19/2002CS170 Fall CS170 Computer Organization and Architecture I Ayman Abdel-Hamid Department of Computer Science Old Dominion University.

Parallel Programming with MPI and OpenMP

Limits of Instruction-Level Parallelism Presentation by: Robert Duckles CSE 520 Paper being presented: Limits of Instruction-Level Parallelism David W.

Pipelining and Parallelism Mark Staveley

Motivation: Sorting is among the fundamental problems of computer science. Sorting of different datasets is present in most applications, ranging from.

Efficiency of small size tasks calculation in grid clusters using parallel processing.. Olgerts Belmanis Jānis Kūliņš RTU ETF Riga Technical University.

A System Performance Model Distributed Process Scheduling.

3/12/2013Computer Engg, IIT(BHU)1 INTRODUCTION-1.

Scaling Conway’s Game of Life. Why do parallelism? Speedup – solve a problem faster. Accuracy – solve a problem better. Scaling – solve a bigger problem.

1a.1 Parallel Computing and Parallel Computers ITCS 4/5145 Cluster Computing, UNC-Charlotte, B. Wilkinson, 2006.

LECTURE #1 INTRODUCTON TO PARALLEL COMPUTING. 1.What is parallel computing? 2.Why we need parallel computing? 3.Why parallel computing is more difficult?

1 Potential for Parallel Computation Chapter 2 – Part 2 Jordan & Alaghband.

1 Parallel Processing Fundamental Concepts. 2 Selection of an Application for Parallelization Can use parallel computation for 2 things: –Speed up an.

Classification of parallel computers Limitations of parallel processing.

Distributed and Parallel Processing George Wells.

Potential for parallel computers/parallel programming

4- Performance Analysis of Parallel Programs

Introduction to Parallelism.

Chapter 3: Principles of Scalable Performance

Quiz Questions Parallel Programming Parallel Computing Potential

By Brandon, Ben, and Lee Parallel Computing.

PERFORMANCE MEASURES. COMPUTATIONAL MODELS Equal Duration Model:  It is assumed that a given task can be divided into n equal subtasks, each of which.

Performance of computer systems

Potential for parallel computers/parallel programming

The University of Adelaide, School of Computer Science

Potential for parallel computers/parallel programming

Quiz Questions Parallel Programming Parallel Computing Potential

Quiz Questions Parallel Programming Parallel Computing Potential

Quiz Questions Parallel Programming Parallel Computing Potential

Presentation transcript:

CS 584 Lecture 11 l Assignment? l Paper Schedule –10 Students –5 Days –Look at the schedule and me your preference. Quickly.

A Quantitative Basis for Design l Parallel programming is an optimization problem. l Must take into account several factors: –execution time –scalability –efficiency

A Quantitative Basis for Design l Parallel programming is an optimization problem. l Must take into account several factors: l Also must take into account the costs: –memory requirements –implementation costs –maintenance costs etc.

A Quantitative Basis for Design l Parallel programming is an optimization problem. l Must take into account several factors: l Also must take into account the costs: l Mathematical performance models are used to asses these costs and predict performance.

Defining Performance l How do you define parallel performance? l What do you define it in terms of? l Consider –Distributed databases –Image processing pipeline –Nuclear weapons testbed

Amdahl's Law l Every algorithm has a sequential component. l Sequential component limits speedup Sequential Component Maximum Speedup = 1/s = s

Amdahl's Law s Speedup

What's wrong? l Works fine for a given algorithm. –But what if we change the algorithm? l We may change algorithms to increase parallelism and thus eventually increase performance. –May introduce inefficiency

Metrics for Performance l Speedup l Efficiency l Scalability l Others …………..

Speedup What is Speed? What algorithm for Speed1? What is the work performed? How much work?

Two kinds of Speedup l Relative –Uses parallel algorithm on 1 processor –Most common l Absolute –Uses best known serial algorithm –Eliminates overheads in calculation.

Speedup l Algorithm A –Serial execution time is 10 sec. –Parallel execution time is 2 sec. l Algorithm B –Serial execution time is 2 sec. –Parallel execution time is 1 sec. l What if I told you A = B?

Performance Measurement l Algorithm X achieved speedup of 10.8 on 12 processors. –What is wrong? l A single point of reference is not enough! l What about asymptotic analysis?

Performance Measurement l There is not a perfect way to measure and report performance. l Wall clock time seems to be the best. l But how much work do you do? l Best Bet: –Develop a model that fits experimental results.