====== Cluster Calcolo ======

Il cluster R&D del CNAF si compone attualmente delle seguenti macchine:
  * [[cluster_calcolo#rd-ui|rd-ui]]: VM per il login degli utenti e per la sottomissione dei job
  * [[cluster_calcolo#rd-lsfmaster|rd-lsfmaster]]: VM con il master daemon e scheduler del batch system LSF
  * [[cluster_calcolo#rd-slurm|rd-slurm]]: VM con il master daemon e scheduler del batch system SLURM
  * [[cluster_calcolo#rd-coka-01|rd-coka-01]]: server con 1 MIC e 2 GPU K20
  * [[cluster_calcolo#rd-coka-02|rd-coka-02]]: server con 2 MIC
  * [[cluster_calcolo#rd-gpu-02|rd-gpu-02]]: server con 2 GPU C2050
  * [[cluster_calcolo#carma-devkit|carma-devkit]]: host con processore ARM e GPU nVidia
  * [[cluster_calcolo#rd-arm-compiler|rd-arm-compiler]]: host per la cross compilazione
\\
{{:strutture:cnaf:cnaf_rd:infrastruttura:map.png}}
\\
==== Descrizione Hardware ====
^ Name         ^ Chassis ^ MotherBoard         ^ Cores                               ^ Memory         ^ GPU/Co-processor                       ^ OS           ^ 
| rd-coka-01   | E4      | Supermicro X9DRG-HF | SandyBridge 2 x 12 cores @ 2.00 GHz | 32GB @ 1333MHz | 2 x nVidia K20m + Intel Xeon Phi 3110P | CentOS 6.4   |
| rd-coka-02   | E4      | Supermicro X9DRG-HF | SandyBridge 2 x 12 cores @ 2.00 GHz | 32GB @ 1333MHz | 2 x Intel Xeon Phi 5110P               | SL 6.4       |
| rd-gpu-02    | E4      | Supermicro X8DTG-QF | Westmere 2 x 8 cores @ 2.40 GHz     | 24GB @ 1066MHz | 3 x nVidia C2050                       | CentOS 6.4   |
| carma-devkit | Seco    | cardhu              | ARM A9 1 x 4 cores @ 1.4GHz         | 2GB DDR3       | nVidia Quadro 1000M                    | Ubuntu 12.04 |
\\
== Dettagli Hardware ==
^    GPU   K20                          ^^ 
| Modello           | NVIDIA Tesla K20m | 
| Cores             | 13 (Multiprocessors) x 192 (CUDA cores) = 2496 CUDA cores |  
| GPU clock rate    | 706 MHz |
| Memory            | 5 GB GDDR5 |
| Memory clock rate | 2600 MHz |
| Memory bandwidth  | 208 GB/sec |
| Max power cons.   | 225 Watt |
| Peak SP perf.     | 3.52 Tflops |
| Peak DP perf.     | 1.17 Tflops |

^    GPU   C2050                          ^^ 
| Modello           | NVIDIA Tesla C2050 | 
| Cores             | 14 (Multiprocessors) x 32 (CUDA cores) = 448 CUDA cores |  
| GPU clock rate    | 1147 MHz |
| Memory            | 3 GB GDDR5 |
| Memory clock rate | 1500 MHz |
| Memory bandwidth  | 144 GB/sec |
| Max power cons.   | 238 Watt |
| Peak SP perf.     | 1.03 Tflops |
| Peak DP perf.     | 515 Gflops |

^    GPU  Quadro 1000M                   ^^ 
| Modello           | NVIDIA Quadro 1000M | 
| Cores             | 2 (Multiprocessors) x 48 (CUDA cores) = 96 CUDA cores |  
| GPU clock rate    | 1400 MHz |
| Memory            | 2048 MBytes |
| Memory clock rate | 900 MHz |
| Memory bandwidth  | 28.8 GB/sec |
| Max power cons.   | 45 Watt |
| Peak SP perf.     | 268.8 Gflops |
| Peak DP perf.     | ... Tflops |

^    MIC 3110P                          ^^ 
| Modello           | Intel Xeon Phi 3110P | 
| Cores             | 57 (Multiprocessors) x 4 (hardware threads) = 228 cores |  
| CPU clock rate    | 1100 MHz |
| Memory            | 3 GBytes |
| Memory clock rate | 2500 MHz |
| Memory bandwidth  | 240 GB/s |
| Max power cons.   | 225 Watt |
| Peak SP perf.     | ... Tflops |
| Peak DP perf.     | 1.0 Tflops |

^    MIC 5110P                          ^^ 
| Modello           | Intel Xeon Phi 5110P | 
| Cores             | 60 (Multiprocessors) x 4 (hardware threads) = 240 cores |  
| CPU clock rate    | 1053 MHz |
| Memory            | 8 GBytes |
| Memory clock rate | 2500 MHz |
| Memory bandwidth  | 320 GB/s |
| Max power cons.   | 225 Watt |
| Peak SP perf.     | ... Tflops |
| Peak DP perf.     | 1.01 Tflops |
==== Descrizione Software ====
== GPU ==
Per utilizzare le GPU sono disponibili:
  * CUDA 5.0 
  * CUDA 5.5
  * Intel OpenCL 1.2-3.0.67279
== Intel Xeon Phi ==
Per l'uso dei co-processori Phi sono disponibili:
  * Intel Parallel Studio XE 2013 (icc 13.1.1)
  * Intel Parallel Studio XE 2013 SP1 (icc 14.0.0)
== MPI ==
  * Intel MPI
  * MVAPICH 2
  * OpenMPI