Hydra Architecture - Multi-Core Cluster

Server name: hydra.ucdenver.pvt

The Multi-core cluster consists of following primary components:

  • 1 master node
  • 16 compute nodes
  • NVIDIA Tesla Fermi S2050 GPU servers
  • Cluster private network interface

Master Node

The master node is mainly used to manage all computing resources and operations of the Hydra cluster and correspond the node -1 in the cluster. It is also the machine that users log into, create/edit/compile programs, and submit them for execution on one or more of the compute nodes. Users do not run their programs on the master. Repeat: user programs MUST NOT be run applications on the master . Instead, they are submitted to the compute nodes for execution. The master node of Hydra cluster is featured by:
  • Processors: Dual AMD Opteron processors (six cores per processor, total 12 cores on master node).
  • Each processor chip has:
  •        - 6 Core 2.2GHz/processor chip
           - 6 Core 2.2GHz/processor chip
           - 6x64KB (Data) + 6x64KB(Instruction) L1 Cache
           - 6x512KB L2, Cache
           - 6MB L3 Cache
           - Memory: 32 GB RAM
  • HW RAID:
  •        - RAID 1 Volume: 238 GB        - RAID 5 Volume: 2861 GB
  • Operating System: CentOS 6.7

Compute Nodes

Compute nodes are nodes that execute the jobs submitted by users. From the master node, clients submit programs to execute them on one or more compute nodes.There are 16 compute nodes (nodes 0 to 15) on Hydra including:
  • Two AMD Opteron 2427 processors, 12 cores
  • Each processor chip has:
  •        - Six Core 2.2GHz / processor chip
           - 6x64KB (Data) + 6x64KB(Instruction) L1 Cache
           - 6x512KB L2,
           - 6MB L3 Cache
  • Memory: 24 GB RAM
  • Storage: 160 GB SATA
  • Operating System: CentOS 6.7
** Nodes 02, 03, 04, 05, 08, 14, and 15 are currently down

Multicore Cluster - System Especification

Host Name
Operating System
CentOS release 6.7 (Final)
Scyld ClusterWare release 6.7.6
Number Nodes
16 computer nodes plus 1 master node
Total CPU Cores
204 (12 cores on master nodes plus 192 cores in all 16 computer nodes)
Number GPUs
8 Tesla Fermi GPUs
Total GPU CUDA Cores
3584 cuda cores (8 x 448)
1.15 GHz per core
Total Max GFLOPS of CPUs
480 (2.5 GFLOPS per core)
Total Disk Space 7566 GB
Total RAM
544 GB
Total RAM of GPUs
27 GB
Processors per Node
2 x 6-core processors (Node 0 - Node 15)
Cores per Node
12 cores (Node 0 - Node 15)
Processor Type
AMD Opteron 2427 (Node 0 - Node 15)
Processor Speed
2.2 Ghz
L1 Instruction Cache per Processor
6 x 64 KB
L1 Data Cache per Processor
6 x 64 KB
L2 Cache per Processor
6 x 512KB
L3 Cache per Processor
64 bit Support
RAM on Master Node
32 GB
Disk Space on Master Node
238 GB (RAID1)
2,861GB (RAID5)

NVIDIA Tesla Fermi S2050 GPU servers

  • There are two NVIDIA Tesla Fermi S2050 GPU servers.
  • Each GPU server has 4 NVIDIA GPUs.
  • Currently, there are 2 compute nodes connected to NVIDIA Tesla GPU server (Node 12 and 13). Each node is connected to two GPUs of a GPU server.
Tesla S2050 GPU Features
  CUDA Driver Version / Runtime Version               
6.5 / 6.5
CUDA Capability Major/Minor version number
Total amount of global memory
2687 MBytes (2817982464 bytes)
(14) Multiprocessors, ( 32) CUDA Cores/MP
448 CUDA Cores
GPU Clock rate
1147 MHz (1.15 GHz)
Memory Clock rate
1546 Mhz
Memory Bus Width
L2 Cache Size
786432 bytes
Maximum Texture Dimension Size (x,y,z)
2D=(65536, 65535),
3D=(2048, 2048, 2048)
Total amount of constant memory
65536 bytes
Total amount of shared memory per block
49152 bytes
Total number of registers available per block
Warp size
Maximum number of threads per multiprocessor
Maximum number of threads per multiprocessor
Maximum number of threads per block
Max dimension size of a thread block (x,y,z)
(65535, 65535, 65535)
Integrated GPU sharing Host Memory
Compute Mode
multiple host threads can use ::cudaSetDevice() with device simultaneously


[1] C. N. Keltcher, K. J. McGrath, A. Ahmed and P. Conway, "The AMD Opteron processor for multiprocessor servers," in IEEE Micro, vol. 23, no. 2, pp. 66-76, March-April 2003. doi: 10.1109/MM.2003.1196116. [pdf]