Difference between revisions of "CoE Cluster April 2012"
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*[[/CUDA|Programmering med CUDA]] | *[[/CUDA|Programmering med CUDA]] | ||
=Results= | =Results= | ||
| − | Having investigated CUDA C programming for Nvidia graphics cards and the CUDA architecture, we made some performance measurements using a range of numbers of blocks and threads, executing in parallel. The test program calculated values of the [http://en.wikipedia.org/wiki/Mandelbrot_set Mandelbrot Set ] on a pixel-by-pixel basis. | + | Having investigated CUDA C programming for Nvidia graphics cards and the CUDA architecture, we made some performance measurements using a range of numbers of blocks and threads, executing in parallel. The test program calculated values of the [http://en.wikipedia.org/wiki/Mandelbrot_set Mandelbrot Set ] on a pixel-by-pixel basis. The Mandelbrot Set was a good choice for this test beacuse: |
| + | * It is relatively easy to program | ||
| + | * Every pixel has to calculated individually - there is no correlation between values of neighbouring pixels | ||
| + | * The image can be separated into parts which can be calculated separately and in parallel | ||
| + | * The time taken to calculate a complete image without parallelization is long enough to allow the performance gains from parallelization to be clearly seen | ||
| + | * The resulting images are very pretty (if a little strange)! :-) | ||
| + | |||
| + | The results of the benchmarking are here, as an interactive chart (hover your mouse to find out which values are represented by each line, drag to zoom and hide/reveal curves relating to the number of blocks in the legend beneath): | ||
*[http://mars.tekkom.dk/js/cuda_benchmark.htm CUDA Benchmark] (External JavaScript) | *[http://mars.tekkom.dk/js/cuda_benchmark.htm CUDA Benchmark] (External JavaScript) | ||
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Mandelbort Set drawn in 0,12 seconds, using a single NVidia CUDA capable graphics card (GeForce GTX 460) running 32 blocks and 128 threads in each block. Total image dimension 512 x 512 pixels. | Mandelbort Set drawn in 0,12 seconds, using a single NVidia CUDA capable graphics card (GeForce GTX 460) running 32 blocks and 128 threads in each block. Total image dimension 512 x 512 pixels. | ||
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=Slides= | =Slides= | ||
Revision as of 09:08, 1 May 2012
{{#img: image=Super-computer-artw.jpg | page=Linux Cluster til Center of Excelence/Beskrivelse til CoE West | width=200px | title=Linux Supercomputer projekt }}
Assignments
Results
Having investigated CUDA C programming for Nvidia graphics cards and the CUDA architecture, we made some performance measurements using a range of numbers of blocks and threads, executing in parallel. The test program calculated values of the Mandelbrot Set on a pixel-by-pixel basis. The Mandelbrot Set was a good choice for this test beacuse:
- It is relatively easy to program
- Every pixel has to calculated individually - there is no correlation between values of neighbouring pixels
- The image can be separated into parts which can be calculated separately and in parallel
- The time taken to calculate a complete image without parallelization is long enough to allow the performance gains from parallelization to be clearly seen
- The resulting images are very pretty (if a little strange)! :-)
The results of the benchmarking are here, as an interactive chart (hover your mouse to find out which values are represented by each line, drag to zoom and hide/reveal curves relating to the number of blocks in the legend beneath):
- CUDA Benchmark (External JavaScript)
Mandelbrot Set drawn in 1,2 seconds, using a Master-Worker MPI pattern with 16 worker nodes and 1 master node (which collected calculated results from different parts of the image and output them to an X Window). Total image dimension 800 x 800 pixels.
Mandelbort Set drawn in 0,12 seconds, using a single NVidia CUDA capable graphics card (GeForce GTX 460) running 32 blocks and 128 threads in each block. Total image dimension 512 x 512 pixels.
Slides
Litteratur Liste
- MPI
- Introduction to Parallel Computing
- CUDA Overview from Nvidia
- Nvidia CUDA C Programming Guide
- OpenCV Tutorial
- OpenCV Reference
- Skin Detection algorithms for use in OpenCV/CUDA trials