SKALB Conferences

Conferences I've presented SKALB related results.

• 2010




• Facing the Multicore-Challenge, Heidelberger Akademie der Wissenschaften, Heidelberg, Germany, March 2010


• ECCOMAS CFD 2010, Lisbon, Portugal, June 2010




• 2009





• First International Workshop on Computational Engineering – Special Topic Fluid-Structure Interaction, Herrsching am Ammersee, Germany, October 2009


• 5th Erlangen High-End-Computing Symposium , Erlangen, Germany, June 2009



• First International Conference on Parallel, Distributed and Grid Computing for Engineering (PARENG09-S01), Pecs, Hungary, April 2009.

TinyGPU offers new hardware

TinyGPU has new hardware: tg010. The hardware configuration and the currently deployed software are different to the non-Fermi nodes:

• Ubuntu 10.04 LTS (instead of 8.04 LTS) as OS.
  Note: For using the Intel Compiler <= 11.1 locally on tg010, you have to use gcc/3.3.6 Module [currently]. If not,  libstdc++.so.5 is missing , as Ubuntu 10.04 does no longer contain this version. This is necessary only for compilation. Compiled Intel binaries will run as expected.


• /home/hpc and /home/vault are mounted [only] through NFS  (and natively via GPFS-Cross-Cluster-Mount)


• Dual-Socket-System with  Intel Westmere X5650 (2.66 GHz) processor, having 6 native cores per socket (instead of Dual-Socket-System with  Intel Nehalem X5550 (2.66 GHz), having  4 native cores per socket)


• 48 GB DDR3 RAM (instead of  24 GB DDR3 RAM)


• 1x NVidia Tesla C250 (“Fermi” with  3 GB GDDR5 featuring ECC)


• 1x NVidia GTX 280 (Consumer-Card with 1 GB RAM – formerly know as F22)


• 2 further PCIe2.0 16x slots will be equipped with  NVidia C2070 Cards (“Fermi” with  6 GB GDDR5 featuring ECC) in Q4, instead of  2x NVidia Tesla M1060 (“Tesla” with  4 GB RAM) as in the remaining cluster nodes


• SuperServer 7046GT-TRF / X8DTG-QF with  dual Intel 5520 (Tylersburg) chipset instead of  SuperServer 6016GT-TF-TM2 / X8DTG-DF with  Intel 5520 (Tylersburg) chipset

To allocate the fermi node, specify  :ppn=24 with your job  (instead of  :ppn=16) and explicitly submit to  the  TinyGPU-Queue fermi. The wallclock limit is set to the default of 24h . The ECC Memory status is shown on job startup.
This article tries to be a translation from the original posted here: Zuwachs im TinyGPU-Cluster