Parallel Programming Henri Bal Vrije Universiteit Faculty of Sciences Amsterdam
Overview What is this large display?? What is parallel programming? Why do we need parallel programming? Organization of this course Parallel Programming & Visualization Practicum Parallel Programming
ICWall A large display suitable for: – Education (classroom) – Modern scientific applications that need to visualize large data-sets – 3D applications
ICWall: an "off-the-shelf" video wall High-end graphics is very expensive Use tiled display with many projectors Use standard (inexpensive) components – PCs, graphic and network cards – 'PowerPoint' video projectors (~ 1 kg) Similar idea as cluster computing, but applied to graphics
Setup 4x2x2 projectors – 1024x768 pixels (4096x1536 total) Screen – Back-projection, ~5.5m x 2m PC cluster – 8 nodes + 1 server – Myrinet network – Dual AMD Athlons – Nvidia GeForce 3
Problems – Mechanical alignment of projectors is difficult – Projectors have different colors, brightness, etc. Solution – Project test image – Observe image with a camera – Analyse difference between original and observed image – Compensate for differences in software Making a single image
Parallel Programming Sequential programming Single thread of control Parallel programming Multiple threads of control Why parallel programming?
Parallel Programming Sequential programming Single thread of control Parallel programming Multiple threads of control Why parallel programming? Eases programming? Not really.
Parallel Programming Sequential programming Single thread of control Parallel programming Multiple threads of control Why parallel programming? Eases programming? Not really. Performance? Yes!
Parallel Processing Many applications need much faster machines Sequential machines are reaching their speed limits Use multiple processors to solve large problems fast Microprocessors are getting cheaper and cheaper
Grand Challenges Applications with high impact on industry or society Require extraordinary performance: 1 Teraflop = operations/sec Several Teraflop parallel machines exist See
Challenging Applications Analyzing video images Aircraft modeling Ozone layer modeling Climate modeling Ocean circulation Quantum chemistry General: computational science Computer chess Protein folding
About this Course Goal: Study how to write programs that run in parallel on a large number of machines. Focus on programming methods, languages, applications Prerequisites: Some knowledge about sequential languages Little knowledge about networking and operating systems
Aspects of Parallel Computing Algorithms and applications Programming methods, languages, and environments Parallel machines and architectures
Course Outline Introduction in algorithms and applications Parallel machines and architectures Overview of parallel machines Cluster computers (Myrinet) Programming methods, languages, and environments Message passing (SR, MPI, Java) Higher-level languages: Linda, Orca, HPF Applications N-body problems, search algorithms Grid computing
Course Information Examination Written exam based on: - Reader - Handouts - Lectures More information (slides, recommended books):
Related Courses Parallel programming practicum Computer graphics Scientific visualization (together with Physics)
Practicum Parallel Programming Separate practicum (6 ECTS) Implement ASP + SOR algorithms in C/MPI Implement IDA* search algorithm in Java/RMI Test and measure the programs on our DAS-2 cluster
More information Register by to (Rob van Nieuwpoort) now Starts after MPI lecture (25 October) See for a complete description