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1 COSC 6374 Parallel Computation Introduction and Organizational Issues Spring 2008 Organizational issues (I) Classes: Monday, 1.00pm 2.30pm, F 154 Wednesday, 1.00pm 2.30pm, F 154 Evaluation 2 quizzes 1 25% each 1 project, 50% Groups consisting of up to 4 people 2 presentations and demonstrations: 25% Code review Documentation Measurements 1
2 Organizational issues (II) In case of questions: Tel: (713) Office hours: Monday 3pm-4.00pm or by appointment All slides available on the website: TA: Saber Feki Office hours: to be announced. Why Parallel Computing? To solve larger problems many applications need significantly more memory than a regular PC can provide/handle To solve problems faster despite of many advances in computer hardware technology, many applications are running slower and slower e.g. databases having to handle more and more data e.g. large simulations working on even more accurate solutions 2
3 Parallel Programming Exploit concurrency Internet: Client and server are independent, interacting applications Searching an element: distribute the search database onto multiple processors Adding two arrays of integers: + = Processor 1 Processor 2 + = + = Parallel Programming (II) Scalar product: s= 1 i= 0 Parallel algorithm s= /2 1 i= 0 / 2 1 a[ i]* b[ i] ( a[ i]* b[ i]) + 1 i= / 2 = ( alocal[ i]* blocal[ i]) i= rank= 0 ( a[ i]* b[ i]) + / 2 1 ( alocal[ i]* blocal[ i]) i= rank= 1 requires communication between the processes 3
4 Designing energy-efficient airplanes Flow around a space vehicle during re-entry 4
5 Simulation of combustion processes Determining phylogenetic trees 5
6 Special effects are highly compute intensive Example: Lord of the Rings company: Weta Digitals 3200 processor cluster a single scene contains: per second 24 frames per frame: 4996 x 3112 points with 32- or 64 bit color encoding Number of computer-added special effects in movies: Jurassic Park : 75 Lord of the Rings (I): 540 each of the following episodes of Lord of the Rings doubled the number of special effects last episode of Star-Wars: HPC in financial business large data-bases simulations for risk assessment of portfolios 6
7 each access (=web search) means that several hundreds MB of data have to be touched Google s database consists of hundreds of terabytes of data A web search is trivially parallel Google s cluster-philosophy: many cheap (unreliable) nodes Reliability achieved through replication in software and hardware End of 2003: several PC cluster with each nodes Luis Andre Barrosso, Jeffrey Dean, Urs Hölzle, Web search for a planet: The Google Cluster Architecture, IEEE Computer Society 7
8 Multi-core processors Parallel Computer 8
9 Parallel Computer PC cluster in U height per node - Myrinet Network - Fast Ethernet Network (1U = 1.75 = 4.45 cm) Parallel Computer PC Cluster in U height - Infiniband Network - Gigabit Ethernet Network 9
10 Earth Simulator Target: Achievement of high-speed numerical simulations with processing speed of 1000 times higher than that of the most frequently used supercomputers in ( 640 nodes 8 processors/node 40 TFLOPS peak 10 TByte memory 10
11 Top 500 List ( Top 500 List 11
12 Accompanying literature (I) Ananth Grama, Anshul Gupta, George Karypis, Vipin Kumar: Introduction to Parallel Computing, Pearson Education, John May: Parallel I/O for High Performance Computing, Morgan Kaufmann, Accompanying literature (II) Accompanying literature Timothy G. Mattson, Beverly A. Sanders, Berna L. Massingill Patterns for Parallel Programming, Software Pattern Series, Addison Wessley, Jack Dongarra, Ian Foster, Geoffrey Fox, William Gropp, Ken Kennedy, Linda Torczon, Andy White Sourcebook of Parallel Computing, Morgan Kaufmann Publishers, Michael J. Quinn: Parallel Programming in C with MPI and OpenMP, McGrawHill, L. Ridgeway Scott, Terry Clark, Babak Bagheri: Scientific Parallel Computing, Princeton University Press,
13 Outline of the course 1. Introduction to Parallel Computation 2. Parallel Computer Architecture 3. Parallel Programming MPI Algorithms Application domains and examples Classification of parallel algorithms Parallel I/O 4. Parallel Performance Analysis Performance modeling 13
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