Ny sökning
PFS0068
Parallell bildanalys
In this course we will explore techniques to take full advantage of modern desktop hardware, as well as high-performance computing facilities such as UPPMAX, for image analysis applications.
The course will give an introduction to:
•how to use computing facilities to reduce the time it takes to run your long-running experiments;
•how to turn a sequential algorithm into a parallel one;
•how to use OpenMP to divide the work over all the cores in your multi-core computer;
•how to use OpenCL and your graphics card to speed up simple algorithms;
•how to use MPI for large-scale parallelism on distributed-memory computer clusters; and
•how to use profiling tools to improve load balancing.
In addition to a simple exercise with each of these technologies, you will choose one of these technologies to try to speed up your favorite algorithm as much as you can. You will then share with the class your experiences and lessons learned. This project will serve as the course evaluation.
The course will give an introduction to:
•how to use computing facilities to reduce the time it takes to run your long-running experiments;
•how to turn a sequential algorithm into a parallel one;
•how to use OpenMP to divide the work over all the cores in your multi-core computer;
•how to use OpenCL and your graphics card to speed up simple algorithms;
•how to use MPI for large-scale parallelism on distributed-memory computer clusters; and
•how to use profiling tools to improve load balancing.
In addition to a simple exercise with each of these technologies, you will choose one of these technologies to try to speed up your favorite algorithm as much as you can. You will then share with the class your experiences and lessons learned. This project will serve as the course evaluation.
Kursplan och övrig information
Kursplan
PFS0068 Parallell bildanalys, 3,0 Hp
Ämnen
BildanalysUtbildningens nivå
ForskarnivåFörkunskapskrav
At least 5 hp in image analysis courses, and experience with MATLAB and C programming.Mål
After the course, the student shall: •have some experience using a high-performance computing facility; •have implemented an image analysis algorithm that benefits from an increased number of computing cores; •have implemented an image filter that uses common graphics hardware; •have implemented an image analysis algorithm that can run on a distributed-memory computer cluster; •used a profiler; and •have translated a sequential algorithm to a parallel one.Innehåll
In this course we will explore techniques to take full advantage of modern desktop hardware, as well as high-performance computing facilities such as UPPMAX, for image analysis applications. The course will give an introduction to: •how to use computing facilities to reduce the time it takes to run your long-running experiments; •how to turn a sequential algorithm into a parallel one; •how to use OpenMP to divide the work over all the cores in your multi-core computer; •how to use OpenCL and your graphics card to speed up simple algorithms; •how to use MPI for large-scale parallelism on distributed-memory computer clusters; and •how to use profiling tools to improve load balancing. In addition to a simple exercise with each of these technologies, you will choose one of these technologies to try to speed up your favorite algorithm as much as you can. You will then share with the class your experiences and lessons learned. This project will serve as the course evaluation.Ytterligare information
Students will need to use their own desktop/laptop for the project. Remote access to UPPMAX computing facilities will be given for the duration of the course.Sign up for the course by emailing Cris Luengo. The deadline is one week before the course start. The course is limited to 24 participants, and taught in English. All lectures will be held at the Centre for Image Analysis.
The schedule will be announced in due course. We are planning the course for the months April and May, 2011