CoE Cluster April 2012

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Assignments

• MD5 attack singlethreaded
• Dell Cluster Installation
• Programmering med MPI
• Programmering med CUDA

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)

Along with the CUDA C programming Guide (see Literature List), the CUDA Occupancy Calculator provides a good basis for understanding and explaining the results seen. In general, it was found that more than 32 blocks and/or 128 threads per block did not provide noticable performance gains, whereas there were significant performance gains for every step increase below these values.

Above: Mandelbrot 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.

For the reasons given above, the Mandelbrot Set was also a good candidate for testing [Message Passing Interface - MPI http://en.wikipedia.org/wiki/Message_Passing_Interface] programming capabilities and strategies.

Above: 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.

Slides

• PXE
• DNS
• NFS
• MPI
• APT-Cacher

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