MachineList - Revision history

Rmh325 at 17:55, 9 June 2023

2023-06-09T17:55:40Z

Rmh325 at 13:48, 28 March 2023

2023-03-28T13:48:10Z

Rmh325: /* Math Department Linux Machines */

2023-03-28T13:47:56Z

Math Department Linux Machines

Rmh325 at 13:37, 28 March 2023

2023-03-28T13:37:08Z

Rmh325 at 13:36, 28 March 2023

2023-03-28T13:36:55Z

Rmh325 at 15:37, 8 March 2023

2023-03-08T15:37:30Z

Rmh325 at 15:08, 1 December 2022

2022-12-01T15:08:43Z

Rmh325 at 14:29, 15 November 2022

2022-11-15T14:29:10Z

Rmh325 at 14:26, 15 November 2022

2022-11-15T14:26:55Z

Admin: /* Math Department Linux Machines */

2022-07-25T19:32:17Z

Math Department Linux Machines

@@ Line 47: / Line 47: @@
 |-
 |dynkin || VM on AMD Epyc Milan || 14 || 64GB || N/A ||10Gb
 |}

← Older revision		Revision as of 13:48, 28 March 2023
Line 10:		Line 10:

	'''Offsite Cloud Machines'''		'''Offsite Cloud Machines'''
		+
	In addition to the machines below, it's now possible to launch a fully-configured Math machine, with all software, licenses, and access to your files, at cloud providers such as Cornell Center for Advanced Computing, or Amazon AWS. These machines appear on the Math network just like other Math machines and are accessed in the same way. These cloud machines can be very large and fast, or include top of the line GPUs. The cloud machines are pay-as-you-go, rounded up to the next hour. For always-on computers like the ones at the Math department, these cloud machines would be very expensive. However, for fixed-duration use, they can be a fantastic bargain. Eventually there will be a self-service page for this, but for now, if you want more information about using Cloud computing resources, email mathsystems@cornell.edu.		In addition to the machines below, it's now possible to launch a fully-configured Math machine, with all software, licenses, and access to your files, at cloud providers such as Cornell Center for Advanced Computing, or Amazon AWS. These machines appear on the Math network just like other Math machines and are accessed in the same way. These cloud machines can be very large and fast, or include top of the line GPUs. The cloud machines are pay-as-you-go, rounded up to the next hour. For always-on computers like the ones at the Math department, these cloud machines would be very expensive. However, for fixed-duration use, they can be a fantastic bargain. Eventually there will be a self-service page for this, but for now, if you want more information about using Cloud computing resources, email mathsystems@cornell.edu.

← Older revision		Revision as of 13:47, 28 March 2023
Line 8:		Line 8:

	The easiest way to connect to these machines is through the [[Math Portal]].		The easiest way to connect to these machines is through the [[Math Portal]].
		+
		+	'''Offsite Cloud Machines'''
		+	In addition to the machines below, it's now possible to launch a fully-configured Math machine, with all software, licenses, and access to your files, at cloud providers such as Cornell Center for Advanced Computing, or Amazon AWS. These machines appear on the Math network just like other Math machines and are accessed in the same way. These cloud machines can be very large and fast, or include top of the line GPUs. The cloud machines are pay-as-you-go, rounded up to the next hour. For always-on computers like the ones at the Math department, these cloud machines would be very expensive. However, for fixed-duration use, they can be a fantastic bargain. Eventually there will be a self-service page for this, but for now, if you want more information about using Cloud computing resources, email mathsystems@cornell.edu.

	'''Dedicated Computation Machines'''		'''Dedicated Computation Machines'''

@@ Line 7: / Line 7: @@
 You can connect to the machines using their complete domain name, such as squid2.math.cornell.edu
-The easiest way to connect to these machines is through the [[Math Portal]]
+The easiest way to connect to these machines is through the [[Math Portal]].
 '''Dedicated Computation Machines'''

← Older revision		Revision as of 13:36, 28 March 2023
Line 6:		Line 6:

	You can connect to the machines using their complete domain name, such as squid2.math.cornell.edu		You can connect to the machines using their complete domain name, such as squid2.math.cornell.edu
		+
		+	The easiest way to connect to these machines is through the [[Math Portal]]

@@ Line 63: / Line 63: @@
 |Hostname ||Processor ||Cores / Threads ||RAM  ||GPU ||Net ||Owner
 |-
-|leo || Xeon E5-2698 || 20/40 || 256GB || N/A || 10Gb || A. Townsend
+|leo || Xeon E5-2698 || 40/80 || 256GB || N/A || 10Gb || A. Townsend
 |-
 |wooster ||  AMD Ryzen 9 5950x || 16/32 || 128GB || RTX 3080Ti || 1Gb || D. Barbasch

@@ Line 9: / Line 9: @@
 '''Dedicated Computation Machines'''
-{| border="1"
+{| class="wikitable" border="1"
 |Hostname ||Processor ||Cores / Threads ||RAM  ||GPU ||Net
 |-

@@ Line 28: / Line 28: @@
 |-
 |hopper||Xeon  E5-2640 || 16/32 ||256GB || N/A || 10Gb
 |}
@@ Line 39: / Line 35: @@
 These are virtual machines made available with extra resources from the department servers.<br>
-NOTE: At this time these machines are not available due to upgrades on their physical systems.
+NOTE: These machines are available but their memory and cpu count are subject to change.
 {| border="1"
 |Hostname ||Processor ||vCores ||RAM  ||GPU ||Net
@@ Line 56: / Line 51: @@
 |aio01 || i5-6600 CPU @ 3.30GHz || 4/4 || 16GB || N/A
 |-
-|hank || i7-7700 CPU @ 3.60GHz || 4/8 || 16GB || N/A
+|cardano || i7-7700 CPU @ 3.60GHz || 4/8 || 16GB || N/A
 |-
-|feynman || i7-7500 CPU @ 3.40GHz ||4/8 || 16GB || N/A
+|feynman || i7-7700 CPU @ 3.60GHz ||4/8 || 16GB || N/A
 |}

@@ Line 19: / Line 19: @@
 |-
 |squid2|| i7-6700k CPU @ 4.0GHz || 4/8 || 64GB || RTX 2080 Super || 10Gb
 |-
-|kraken|| Quad Opteron || 64/64 || 512GB || RTX 2080 || 1Gb
+|kraken|| Quad Opteron || 64/64 || 512GB || N/A|| 1Gb
 |-
 |heaviside|| Xeon  E5-2640 || 12/24 || 256GB || N/A || 10Gb
@@ Line 27: / Line 29: @@
 |hopper||Xeon  E5-2640 || 16/32 ||256GB || N/A || 10Gb
 |-
-|nautilus || Xeon X5560 || 8/16 || 96GB || N/A || 1Gb
+|dynkin || Epyc Milan VM || 14 || 64GB || N/A || 10Gb
 |}

@@ Line 16: / Line 16: @@
 |fibonacci||  AMD Ryzen 9 5950x || 16/32 || 128GB || RTX 3080Ti || 10Gb
 |-
 |squid2|| i7-6700k CPU @ 4.0GHz || 4/8 || 64GB || RTX 2080 Super || 10Gb
 |-