GPUs In The CS Linux Clients - Revision history

Steve410 at 16:27, 25 November 2025

2025-11-25T16:27:52Z

Steve410: /* How To Use The GPUs */

2025-11-20T14:34:54Z

How To Use The GPUs

Steve410 at 14:29, 20 November 2025

2025-11-20T14:29:55Z

Phil at 13:28, 19 September 2022

2022-09-19T13:28:53Z

Phil at 13:28, 19 September 2022

2022-09-19T13:28:24Z

Steve410 at 20:45, 9 September 2021

2021-09-09T20:45:06Z

Steve410 at 20:44, 9 September 2021

2021-09-09T20:44:48Z

Steve410 at 20:44, 9 September 2021

2021-09-09T20:44:02Z

Steve410 at 20:42, 9 September 2021

2021-09-09T20:42:57Z

Steve410 at 20:42, 9 September 2021

2021-09-09T20:42:43Z

← Older revision		Revision as of 16:27, 25 November 2025
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	''However'', our "compute server" Linux clients, ''gradx, gradz, ugradx,'' and ''ugradz'' (that are only remotely available through ssh or scp) '''''do not''''' have CUDA-capable GPUs in them. You'll notice that CUDA software ''is'' available on those machines merely because we share the same configuration amongst all our Linux client systems, whether they have the same hardware or not.		''However'', our "compute server" Linux clients, ''gradx, gradz, ugradx,'' and ''ugradz'' (that are only remotely available through ssh or scp) '''''do not''''' have CUDA-capable GPUs in them. You'll notice that CUDA software ''is'' available on those machines merely because we share the same configuration amongst all our Linux client systems, whether they have the same hardware or not.
		+
		+	==Details About Our GPUs==
		+
		+	To see specific details about our GPU, log into any of our labs' Linux computers referenced above and run:
		+
		+	:'''nvidia-smi'''

	==How To Use The GPUs==		==How To Use The GPUs==

@@ Line 13: / Line 13: @@
 We do not have specific info on the "how-tos" of using our GPUs, because... how you use our GPUs will very much depend on which library or framework you're using and what you want to do with them. Generally, CS courses that make use of the GPUs will cover how to make use of them in the course materials.
-We recommend that you consult documentation for the specific GPU library you want to use. For example if you plan to use CUDA with our GPUs (and we believe several CS students do), you can check CUDA's own documentation.  Another common framework is PyTorch (which itself uses CUDA, but also provides a lot of other functionality). Our systems also support OpenCL, in case you want to write code that is portable across more GPU architectures than just the Nvidia GPUs we have.
+We recommend that you consult documentation for the specific GPU library you want to use. For example if you plan to use CUDA with our GPUs (and we believe several CS students do), you can check CUDA's own documentation.  Another common framework is PyTorch (which itself uses CUDA, but also provides a lot of other functionality). Our systems also support OpenCL, in case you want to write code that is portable across more GPU architectures than just the Nvidia GPUs we have.  And your instructor might be able to assist you as well.
 [[Category:Linux Clients]]

← Older revision		Revision as of 14:29, 20 November 2025
Line 9:		Line 9:
	''However'', our "compute server" Linux clients, ''gradx, gradz, ugradx,'' and ''ugradz'' (that are only remotely available through ssh or scp) '''''do not''''' have CUDA-capable GPUs in them. You'll notice that CUDA software ''is'' available on those machines merely because we share the same configuration amongst all our Linux client systems, whether they have the same hardware or not.		''However'', our "compute server" Linux clients, ''gradx, gradz, ugradx,'' and ''ugradz'' (that are only remotely available through ssh or scp) '''''do not''''' have CUDA-capable GPUs in them. You'll notice that CUDA software ''is'' available on those machines merely because we share the same configuration amongst all our Linux client systems, whether they have the same hardware or not.

		+	==How To Use The GPUs==
		+
		+	We do not have specific info on the "how-tos" of using our GPUs, because... how you use our GPUs will very much depend on which library or framework you're using and what you want to do with them. Generally, CS courses that make use of the GPUs will cover how to make use of them in the course materials.
		+
		+	We recommend that you consult documentation for the specific GPU library you want to use. For example if you plan to use CUDA with our GPUs (and we believe several CS students do), you can check CUDA's own documentation. Another common framework is PyTorch (which itself uses CUDA, but also provides a lot of other functionality). Our systems also support OpenCL, in case you want to write code that is portable across more GPU architectures than just the Nvidia GPUs we have.

	[[Category:Linux Clients]]		[[Category:Linux Clients]]

← Older revision		Revision as of 13:28, 19 September 2022
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−	The CS Lab [[:Category:Linux Clients\|Linux clients]] contain '''Nvidia GPUs''' with support for [~~https://support.cs.jhu.edu/wiki/CUDA_On_The_CS_Linux_Clients~~ '''CUDA'''] and OpenCL.	+	The CS Lab [[:Category:Linux Clients\|Linux clients]] contain '''Nvidia GPUs''' with support for [[CUDA On The CS Linux Clients\|'''CUDA''']] and OpenCL.

	These systems include:		These systems include:

← Older revision		Revision as of 13:28, 19 September 2022
Line 1:		Line 1:
−	The CS Lab [[:Category:Linux Clients\|Linux clients]] contain '''Nvidia GPUs''' with support for [https://support.cs.jhu.edu/wiki/CUDA_On_The_CS_Linux_Clients '''CUDA'''].	+	The CS Lab [[:Category:Linux Clients\|Linux clients]] contain '''Nvidia GPUs''' with support for [https://support.cs.jhu.edu/wiki/CUDA_On_The_CS_Linux_Clients '''CUDA'''] and OpenCL.

	These systems include:		These systems include:

@@ Line 7: / Line 7: @@
 * ''ugrad1'' through ''ugrad24'' (Ugrad Labs)
-''However'', our "compute server" Linux clients, ''gradx, gradz, ugradx,'' and ''ugradz (that are only remotely available through ssh or scp)'' '''''do not''''' have CUDA-capable GPUs in them.  You'll notice that CUDA software ''is'' available on those machines merely because we share the same configuration amongst all our Linux client systems, whether they have the same hardware or not.
+''However'', our "compute server" Linux clients, ''gradx, gradz, ugradx,'' and ''ugradz'' (that are only remotely available through ssh or scp) '''''do not''''' have CUDA-capable GPUs in them.  You'll notice that CUDA software ''is'' available on those machines merely because we share the same configuration amongst all our Linux client systems, whether they have the same hardware or not.
 [[Category:Linux Clients]]

← Older revision		Revision as of 20:42, 9 September 2021
Line 1:		Line 1:
−	The CS Lab [[:Category:Linux Clients\|Linux clients]] contain Nvidia GPUs with support for [https://support.cs.jhu.edu/wiki/CUDA_On_The_CS_Linux_Clients '''CUDA'''].	+	The CS Lab [[:Category:Linux Clients\|Linux clients]] contain '''Nvidia GPUs''' with support for [https://support.cs.jhu.edu/wiki/CUDA_On_The_CS_Linux_Clients '''CUDA'''].

	These systems include:		These systems include: