User Tools
opt:graphics_cards
Differences
This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision Next revision | Previous revision Last revision Both sides next revision | ||
|
opt:graphics_cards [2018/08/31 09:52] pklapetek |
opt:graphics_cards [2018/08/31 13:02] pklapetek |
||
|---|---|---|---|
| Line 12: | Line 12: | ||
| To use GPU for a calculation is not straightforward, unfortunataly. We cannot simply take a conventional PC executable and run it on GPU. Both data processing and memory model is completely different for GPU and for CPU and the part of the code that should be run on GPU (called kernel) must be written to fulfill these conditions. GPU is equipped by several multiprocessors, consisting of a large number of processors. Many hundreds of threads (kernel calls) grouped in thread blocks can be processed simultaneously on GPU, which is the basis of tremendous speedup that we can achieve. Memory available on GPU can be divided into a global memory - accessible by all the multiprocessors, a shared memory - accessible by processors within one multiprocessor, and a local memory - accessible by single processor. All the memories are hardware limited (for each type of GPU differently). We refer to Nvidia CUDA developer zone for further details. | To use GPU for a calculation is not straightforward, unfortunataly. We cannot simply take a conventional PC executable and run it on GPU. Both data processing and memory model is completely different for GPU and for CPU and the part of the code that should be run on GPU (called kernel) must be written to fulfill these conditions. GPU is equipped by several multiprocessors, consisting of a large number of processors. Many hundreds of threads (kernel calls) grouped in thread blocks can be processed simultaneously on GPU, which is the basis of tremendous speedup that we can achieve. Memory available on GPU can be divided into a global memory - accessible by all the multiprocessors, a shared memory - accessible by processors within one multiprocessor, and a local memory - accessible by single processor. All the memories are hardware limited (for each type of GPU differently). We refer to Nvidia CUDA developer zone for further details. | ||
| - | To check if the graphics card installed on your computer is suitable for GSvit calculations and if GSvit was installed with graphics card support at all, you can run the solver with parameter "test 0", e.g. on Linux system: | + | To check if the graphics card installed on your computer is suitable for GSvit calculations and if GSvit was installed with graphics card support at all, you can run the solver with parameter "test 0", e.g. on Linux system (see [[start:tests|all the tests]] for more details): |
| <html> | <html> | ||
| Line 22: | Line 22: | ||
| Program is compiled with GPU support | Program is compiled with GPU support | ||
| Searching for available GPUs... | Searching for available GPUs... | ||
| - | Found 4 GPUs | + | Found 5 GPUs |
| The Properties of the Device with ID 0 are | The Properties of the Device with ID 0 are | ||
| Device Name : Tesla K40m | Device Name : Tesla K40m | ||
| Line 42: | Line 42: | ||
| Max threads dim : 1024x1024x64 | Max threads dim : 1024x1024x64 | ||
| The Properties of the Device with ID 3 are | The Properties of the Device with ID 3 are | ||
| + | Device Name : Tesla K20Xm | ||
| + | Device Memory Size : 1744371712 | ||
| + | Block Shared memory size: 49152 | ||
| + | Max grid size : 2147483647x65535x65535 | ||
| + | Max threads dim : 1024x1024x64 | ||
| + | The Properties of the Device with ID 4 are | ||
| Device Name : Tesla K20Xm | Device Name : Tesla K20Xm | ||
| Device Memory Size : 1744371712 | Device Memory Size : 1744371712 | ||
| Line 50: | Line 56: | ||
| </html> | </html> | ||
| - | This means that you can use four different GPUs on this system (which does not usually happen on a standard computer). GSvit does not support use of all of them at once, but you can still run up to four different instances on different cards. The basic settings for using the GPU are therefore whether to use it at all (GPU directive in the parameter file) and eventually which one to use (UGPU directive in the parameter file). GPUs are numbered from 0, exactly the same way as what <tt>gsvit test 0</tt> outputs, so these settings in the parameter file: | + | This means that you can use five different GPUs on this system (which does not usually happen on a standard computer). GSvit does not support use of all of them at once, but you can still run up to four different instances on different cards. The basic settings for using the GPU are therefore whether to use it at all (GPU directive in the parameter file) and eventually which one to use (UGPU directive in the parameter file). GPUs are numbered from 0, exactly the same way as what <tt>gsvit test 0</tt> outputs, so these settings in the parameter file: |
| <html> | <html> | ||
opt/graphics_cards.txt ยท Last modified: 2018/09/04 17:24 by pklapetek
Except where otherwise noted, content on this wiki is licensed under the following license: CC Attribution-Noncommercial 4.0 International
