[ Skip to the content ]

Institute of Formal and Applied Linguistics Wiki

• ÚFAL Wiki Home
• ÚFAL Home
• Sitemap
• RSS
• ÚFAL calendar
• Recent changes
• Login
• Hledat:

[ Back to the navigation ]

Differences

This shows you the differences between two versions of the page.

View differences: Side by SideInline Go

Link to this comparison view

Both sides previous revision Previous revision 2018/08/10 13:49 rosa [Set-up CUDA and CUDNN] 2018/06/21 10:04 vodrazka [Servers with GPU units] 2018/06/21 10:04 vodrazka [Servers with GPU units] 2018/06/12 14:17 popel 2018/06/12 13:50 machacek.dominik [Servers with GPU units] 2018/06/12 12:42 vodrazka [Servers with GPU units] 2018/06/12 12:42 vodrazka [Servers with GPU units] 2018/06/12 12:08 ufal [TensorFlow Environment] 2018/05/31 10:30 popel [Set-up CUDA and CUDNN] 2018/05/30 09:57 vodrazka [Set-up CUDA and CUDNN] 2018/05/30 09:54 vodrazka [Set-up CUDA and CUDNN] 2018/04/27 18:22 popel [Servers with GPU units] 2018/04/24 10:01 vodrazka [Set-up CUDA and CUDNN] 2018/04/23 12:53 vodrazka [Set-up CUDA and CUDNN] 2018/04/16 11:35 kocmanek [Performance tests] 2018/04/16 11:29 kocmanek [Performance tests] 2018/04/16 11:18 kocmanek [Performance tests] 2018/04/13 12:50 kocmanek [Performance tests] 2018/04/12 17:11 kocmanek [Performance tests] 2018/04/12 16:02 kocmanek [Performance tests] 2018/04/12 15:17 kocmanek [Performance tests] 2018/04/09 12:45 vodrazka documenting default settings for cuda and cudnn libraries2018/04/05 14:41 vodrazka table update - Quadro P5000 for testing in twister22018/03/22 11:14 vodrazka Updated cluster GPU table2018/03/21 15:29 ufal [Servers with GPU units] 2018/02/20 17:54 kruza [Rules] typo2018/02/09 15:15 popel [Set-up CUDA and CUDNN] 2018/02/08 15:26 popel [Rules] 2018/02/08 15:21 kocmanek [Servers with GPU units] 2018/02/08 15:20 kocmanek [Rules] 2018/02/08 15:16 kocmanek [Servers with GPU units] 2018/02/08 15:10 kocmanek [Servers with GPU units] 2018/02/08 14:56 kocmanek [Servers with GPU units] 2018/01/25 17:27 popel [Using cluster] 2018/01/23 23:16 popel [Servers with GPU units] 2018/01/15 21:51 popel [Rules] 2018/01/15 10:39 popel [Set-up CUDA and CUDNN] 2018/01/08 13:25 popel dll6 now has 9 cards, so it is the same config as dll32018/01/06 21:17 popel [Rules] 2017/12/05 18:02 popel [Rules] 2017/11/23 14:15 bojar link to munin graphs2017/11/13 09:43 bojar [Rules] 2017/10/17 16:51 popel [Rules] 2017/10/17 16:43 popel [Rules] 2017/10/17 16:39 popel [Using cluster] 2017/10/17 16:38 popel [Rules] 2017/10/17 16:37 popel 2017/10/12 13:42 ufal [How to use cluster] 2017/10/11 13:07 bojar people should not set CUDA_VISIBLE_DEVICES2017/10/11 11:46 popel [Basic commands] 2017/09/11 11:26 fucik Go Next revision		Previous revision 2019/02/07 10:44 vodrazka GPU troja machines added2019/01/08 09:36 vodrazka [Servers with GPU units] 2018/12/19 12:21 vodrazka GPU machine table update2018/12/16 16:26 straka [Rules] 2018/12/01 21:09 popel [Rules] -pe smp now works with gpu2018/11/28 11:21 laitoch [Rules] 2018/11/28 11:11 laitoch [Rules] 2018/11/14 13:46 kocmi [Using cluster] 2018/11/12 10:36 vodrazka [Servers with GPU units] 2018/11/01 14:41 vodrazka [Servers with GPU units] 2018/10/22 16:02 vodrazka [Servers with GPU units] 2018/10/10 16:48 fucik 2018/10/02 16:41 kocmi [Set-up CUDA and CUDNN] 2018/10/01 10:07 vodrazka [Servers with GPU units] 2018/09/05 10:17 vodrazka [Servers with GPU units] 2018/08/10 13:49 rosa [Set-up CUDA and CUDNN] 2018/06/21 10:04 vodrazka [Servers with GPU units] 2018/06/21 10:04 vodrazka [Servers with GPU units] 2018/06/12 14:17 popel 2018/06/12 13:50 machacek.dominik [Servers with GPU units] 2018/06/12 12:42 vodrazka [Servers with GPU units] 2018/06/12 12:42 vodrazka [Servers with GPU units] 2018/06/12 12:08 ufal [TensorFlow Environment] 2018/05/31 10:30 popel [Set-up CUDA and CUDNN] 2018/05/30 09:57 vodrazka [Set-up CUDA and CUDNN] 2018/05/30 09:54 vodrazka [Set-up CUDA and CUDNN] 2018/04/27 18:22 popel [Servers with GPU units] 2018/04/24 10:01 vodrazka [Set-up CUDA and CUDNN] 2018/04/23 12:53 vodrazka [Set-up CUDA and CUDNN] 2018/04/16 11:35 kocmanek [Performance tests] 2018/04/16 11:29 kocmanek [Performance tests] 2018/04/16 11:18 kocmanek [Performance tests] 2018/04/13 12:50 kocmanek [Performance tests] 2018/04/12 17:11 kocmanek [Performance tests] 2018/04/12 16:02 kocmanek [Performance tests] 2018/04/12 15:17 kocmanek [Performance tests] 2018/04/09 12:45 vodrazka documenting default settings for cuda and cudnn libraries2018/04/05 14:41 vodrazka table update - Quadro P5000 for testing in twister22018/03/22 11:14 vodrazka Updated cluster GPU table2018/03/21 15:29 ufal [Servers with GPU units] 2018/02/20 17:54 kruza [Rules] typo2018/02/09 15:15 popel [Set-up CUDA and CUDNN] 2018/02/08 15:26 popel [Rules] 2018/02/08 15:21 kocmanek [Servers with GPU units] 2018/02/08 15:20 kocmanek [Rules] 2018/02/08 15:16 kocmanek [Servers with GPU units] 2018/02/08 15:10 kocmanek [Servers with GPU units] 2018/02/08 14:56 kocmanek [Servers with GPU units] 2018/01/25 17:27 popel [Using cluster] 2018/01/23 23:16 popel [Servers with GPU units] 2018/01/15 21:51 popel [Rules] Go Next revision Both sides next revision
gpu [2018/02/20 17:54] kruza [Rules] typo		gpu [2018/05/30 09:54] vodrazka [Set-up CUDA and CUDNN]
Line 5:		Line 5:
	===== Servers with GPU units =====		===== Servers with GPU units =====
	GPU cluster ''gpu.q'' at Malá Strana:		GPU cluster ''gpu.q'' at Malá Strana:
-		+	| machine | GPU type | GPU driver version | [[https://en.wikipedia.org/wiki/CUDA#GPUs_supported|cc]] | GPU cnt | GPU RAM (GB) | machine RAM (GB)|
-	| machine                    | GPU type | [[https://en.wikipedia.org/wiki/CUDA#GPUs_supported|cc]] | GPUs | GPU RAM | RAM | Comment |	+	| dll1 |  GeForce GTX 1080 |  384.69 |  6.1 |  8 |  8 |  250 |
-	| iridium                    | Quadro K2000        |  cc3.0 |   1|   2 GB | 52 GB | driver(iridium)=367.48 |	+	| dll2 |  GeForce GTX 1080 |  387.34 |  6.1 |  8 |  8 |  250 |
-	| titan-gpu                  | GeForce GTX Titan Z |  cc3.5 |   2|   6 GB | 32 GB | driver(titan-gpu)=381.22 |	+	| dll3 |  GeForce GTX 1080 Ti |  375.66 |  6.1 |  9 |  11 |  250 |
-	| twister1; twister2; kronos | Tesla K40c          |  cc3.5 |   1|  12 GB | 48 GB; 48GB; 128 GB | driver(twister*)=367.48, driver(kronos)=384.81 |	+	| dll4 |  GeForce GTX 1080 Ti |  375.66 |  6.1 |  10 |  11 |  250 |
-	| titan                      | GeForce GTX 1080    |  cc6.1 |   1|   8 GB | 32 GB  | driver(titan)=381.22|	+	| dll5 |  GeForce GTX 1080 Ti |  384.69 |  6.1 |  10 |  11 |  250 |
-	| dll1; dll2                 | GeForce GTX 1080    |  cc6.1 |   8|   8 GB | 250 GB | driver(dll1)=375.66, driver(dll2)=387.26 |	+	| dll6 |  GeForce GTX 1080 Ti |  384.69 |  6.1 |  9 |  11 |  122 |
-	| dll4; dll5                 | GeForce GTX 1080 Ti |  cc6.1 |  10|  11 GB | 250 GB | driver(dll4)=375.66, driver(dll5)=384.69 |	+	| titan-gpu |  GeForce GTX TITAN Z |  381.22 |  3.5 |  2 |  6 |  31 |
-	| dll3                       | GeForce GTX 1080 Ti |  cc6.1 |   9|  11 GB | 250 GB | driver(dll3)=375.66 |	+	| iridium |  Quadro K2000 |  367.48 |  3.0 |  1 |  2 |  504 |
-	| dll6                       | GeForce GTX 1080 Ti |  cc6.1 |   9|  11 GB | 126 GB | driver(dll6)=384.69 |	+	| kronos |  GeForce GTX 1080 Ti |  384.81 |  6.1 |  1 |  11 |  125 |
		+	| titan |  GeForce GTX 1080 |  381.22 |  6.1 |  1 |  8 |  31 |
		+	| twister1 |  Tesla K40c |  367.48 |  3.5 |  1 |  11 |  47 |
		+	| twister2 |  Quadro P5000 |  367.48 |  6.1 |  1 |  17 |  47 |
	Desktop machines:		Desktop machines:
Line 60:		Line 63:
	You also need to use ''qsub -q gpu.q@dll[256]'' because only those machines have drivers which support CUDA 9.		You also need to use ''qsub -q gpu.q@dll[256]'' because only those machines have drivers which support CUDA 9.
		+
		+	**THE NEW CLUSTER (SGE 8.1.9)**
		+
		+	Multiple versions of ''cuda'' can be accessed in ''/opt/cuda''. **Compared to the old cluster there is a difference in setting the CUDA_DIR_OPT variable!!**
		+
		+	You need to set library path from your ''~/.bashrc'':
		+
		+	CUDNN_version=7.0
		+	CUDA_version=9.0
		+	CUDA_DIR_OPT=/opt/cuda/$CUDA_version
		+	if [ -d "$CUDA_DIR_OPT" ] ; then
		+	CUDA_DIR=$CUDA_DIR_OPT
		+	export CUDA_HOME=$CUDA_DIR
		+	export THEANO_FLAGS="cuda.root=$CUDA_HOME,device=gpu,floatX=float32"
		+	export PATH=$PATH:$CUDA_DIR/bin
		+	export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_DIR/cudnn/$CUDNN_version/lib64:$CUDA_DIR/lib64
		+	export CPATH=$CUDA_DIR/cudnn/$CUDNN_version/include:$CPATH
		+	fi
		+
		+	* When not using Theano, just Tensorflow this can be simplified to ''export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/cuda/9.0/cudnn/7.0/lib64:/opt/cuda/8.0/lib64''.
		+
		+	* There is no default and you always need to set ''LD_LIBRARY_PATH'' to suit explicitly.
		+
		+	* Note that ''cudnn'' library is compiled for specific version of ''cuda''. If you need specific version of ''cudnn'', you can look in ''/opt/cuda/$CUDA_version/cudnn/'' whether it is available for given ''$CUDA_version''.
		+
		+
		+
		+
	==== TensorFlow Environment ====		==== TensorFlow Environment ====
Line 132:		Line 163:
	| titan      | GeForce GTX 1080 Ti                |   10:45:11 | (new result with correct CUDA version) |		| titan      | GeForce GTX 1080 Ti                |   10:45:11 | (new result with correct CUDA version) |
	| dll1       | (2 GPU) GeForce GTX 1080; cc6.1    |   12:34:34 | Probably only one GPU was used |		| dll1       | (2 GPU) GeForce GTX 1080; cc6.1    |   12:34:34 | Probably only one GPU was used |
		+	| twister2   | Quadro P5000                       |   13:19:00 |  |
	| dll2       | GeForce GTX 1080; cc6.1            |   13:01:05 | Only one GPU was used |		| dll2       | GeForce GTX 1080; cc6.1            |   13:01:05 | Only one GPU was used |
	| titan-gpu  | (2 GPU) GeForce GTX Titan Z; cc3.5 |   16:05:24 | Probably only one GPU was used |		| titan-gpu  | (2 GPU) GeForce GTX Titan Z; cc3.5 |   16:05:24 | Probably only one GPU was used |
Line 149:		Line 181:
	The previous benchmark only compares the speed of processing units within the GPUs and do not take into account the size of memory. Therefore I have conducted another benchmark, this time for each graphic card I have increased the batch size as much as possible so the model still could fit into the GPU (the previous benchmark model had batch size 20). This way the results should be more representative of the power for each GPU.		The previous benchmark only compares the speed of processing units within the GPUs and do not take into account the size of memory. Therefore I have conducted another benchmark, this time for each graphic card I have increased the batch size as much as possible so the model still could fit into the GPU (the previous benchmark model had batch size 20). This way the results should be more representative of the power for each GPU.
-	| GPU; Cuda capability       | GPU RAM |  Walltime | Batch size | Machine |	+	| GPU; Cuda capability       | GPU RAM |  Walltime | Batch size | Machine   |
-	| Tesla K40c; cc3.5          |   12 GB |           |        |  |	+	| GeForce GTX 1080 Ti; cc6.1 |   11 GB |  00:55:56 |       2300 | dll5      |
-	| GeForce GTX 1080 Ti; cc6.1 |   11 GB |  00:55:56 |       2300 | dll5 |	+	| GeForce GTX 1080; cc6.1    |    8 GB |  01:10:57 |       1700 | dll1      |
-	| GeForce GTX 1080; cc6.1    |    8 GB |  01:10:57 |       1700 | dll1 |	+	| Quadro P5000               |   16 GB |  01:17:00 |       3400 | twister2  |
	| GeForce GTX Titan Z; cc3.5 |    6 GB |  02:20:47 |       1100 | titan-gpu |		| GeForce GTX Titan Z; cc3.5 |    6 GB |  02:20:47 |       1100 | titan-gpu |
-	| Quadro K2000; cc3.0        |    2 GB |  28:15:26 |         50 | iridium |	+	| Quadro K2000; cc3.0        |    2 GB |  28:15:26 |         50 | iridium   |
	===== Links =====		===== Links =====

---

[ Back to the navigation ] [ Back to the content ]