Comparison of Parallel Processing Systems. Motivation

Transcription

1 Comparison of Parallel Processing Systems Ash Dean Katie Willis CS 67 George Mason University Motivation Increasingly, corporate and academic projects require more computing power than a typical PC can handle, i.e., The Human Genome Project The cost of traditional supercomputers can range from $1 million to over $0 million. (Source: The Washington Post, Feb. 6) 1

2 Outline Metrics for Parallel Systems Analytical Comparison The LinPack Benchmark Performance of Supercomputers Performance of a Beowulf Cluster Concluding Remarks Speedup Increase in the number of completed jobs, C 0 Decrease in the total execution time, T 0 X = C 0 / T 0 Increased Throughput S p = T 1 / T p

3 Efficiency Need a metric to ascertain the benefit of each additional processor E p = S p /p E 1 = S 1 = 1 Theoretical upper bound since no single processor can do more than p times as much work per time unit as one processor Essentially a measure of overhead As the number of processors increase, overhead increases as well Analytical Comparisons Supercomputer Queuing Network We assume a service demand of.0 seconds 1 3 n 3

4 Analytical Comparison Beowulf Cluster Queuing Network Assume a service demand of.0 seconds 1 Load Balancer/ Fork Process 3 Join n Analytical Comparison

5 Analytical Comparison LinPack Benchmark Developed at the University of Tennessee, Department of Computer Science Internationally used to evaluate high performance computers Top500 Supercomputers worldwide are evaluated using the LinPack Benchmark The fastest computer currently is the Earth Simulator in Japan, which runs at 35,60 GigaFlops 5

6 LinPack Benchmark Measure of a computer s floating point rate of execution Collection of subroutines for solving various systems of dense linear equations Solving a system of equations requires O(n 3 ) floating-point operations Based on LU decomposition with partial pivoting. Supercomputer Performance Computer Uniprocessor Time Number of Processors Multiprocessor Time Speedup Efficiency Cray Y-MP/ Cray Y-MP/ Cray Y-MP/ Cray Y-MP/ Convex SPP Convex SPP Convex SPP

7 Supercomputer Performance Speedup Processors average a speedup of 5.77 Processors average a speedup of 3.3 Supercomputer Performance Efficiency Processors average an efficiency of Processors average an efficiency of 0.5 7

8 Beowulf Cluster Performance Cluster contains up to nodes Each node uses an AMD Athlon XP MHz 56Kb Cache 56Mb Main Memory Each line in the table represents an average of 0 runs Beowulf Cluster Performance With one processor, average approximately.5 E - GigaFlops With two processors, average approximately 7.79 E - GigaFlops With four processors, average approximately.59 E -3 GigaFlops With eight processors, average approximately.63 E -3 GigaFlops

9 Beowulf Cluster Performance Uniprocessor Time Number of Processors Multiprocessor Time Speedup Efficiency Concluding Remarks Beowulf Clusters are an ideal solution when only a relatively small number of processors are necessary In terms of raw computing power, traditional supercomputers with upwards of 1 processing elements cannot be matched by a cluster In terms of cost, the Beowulf cluster, with a relatively small number of processors will be far less expensive 9

10 References McCarthy, Ellen. The Washington Post. February 16, 00. p. E1. Jordan, Harry F. and Gita Alaghband. Fundamentals of Parallel Processing. Pearson Education, Inc. Upper Saddle River, NJ Dongarra, Jack J. Performance of Various Computers Using Standard Linear Equation Software. April 1, 00 Varki, Elizabeth. Response Time Analysis of Parallel Computer and Storage Systems. June 13,