Institute for Advanced Computing Applications and Technologies at the University of Illinois at Urbana-Champaign

Resources

Accelerator Cluster

Contact Information

For more information about the accelerator cluster, contact:

Mike Showerman
mshow@ncsa.illinois.edu
217-244-5478

This 40-node cluster combines both GPU (graphics processing units) and FPGA (field-programmable gate array) technology in order to explore the potential of these novel architectures to accelerate scientific computing.

32 compute nodes feature:

Two dual-core 2.4 GHz AMD Opterons, 8 GB of memory

One NVIDIA Tesla S1070 containing 4 GT200 GPUs, each with 4 GB of memory (donated by NVIDIA)

Nallatech H101-PCIX FPGA accelerator, 16 MB SRAM, 512 MB SDRAM (donated by Xilinx)

2GB/sec Infiniband connection. (QDR hardware connected to generation 1 PCI-E)

Two compute nodes contain 16GB of memory

Eight nodes, donated by AMD, each with:

Two six-core Istanbul CPUs

Three AMD/ATI 5870 GPUs, each with 1GB of memory

32 GB DDR2 main memory

QDR Infiniband (4GB/sec)

The cluster is used for electrical and computer engineering and computer science courses, for training workshops, as a platform to explore the potential for GPUs to accelerate applications to the petascale and beyond, and as a resource for science and engineering researchers.

Staff at the National Center for Supercomputing Applications (NCSA) assist with deployment and support for the accelerator cluster. NCSA explores innovative architectures and techniques to accelerate scientific computing through its Innovative Systems Laboratory.

Additional details on AC are available in this conference paper.

Acknowledging the AC cluster

This work utilized the AC cluster [ref] operated by the Innovative Systems Laboratory (ISL) at the National Center for Supercomputing Applications (NCSA) at the University of Illinois. The cluster was funded by NSF SCI 05-25308 and CNS 05-51665 grants along with generous donations of hardware from NVIDIA, Nallatech, and AMD.

[ref] is to this publication:
V. Kindratenko, J. Enos, G. Shi, M. Showerman, G. Arnold, J. Stone, J. Phillips, W. Hwu, GPU Clusters for High-Performance Computing, in Proc. Workshop on Parallel Programming on Accelerator Clusters, IEEE International Conference on Cluster Computing, 2009. DOI: 10.1109/CLUSTR.2009.5289128

CUDA software downloads