Every server manufacturer announced support last week for the new Intel Sandy Bridge CPUs in their new models. That includes PC giant Dell, which announced, for the first time, that it is supporting Tesla GPUs in its mainstream Dell PowerEdge R720 servers. And, our new benchmarks demonstrate why.

The PowerEdge R720 is, by far, one of the most popular servers in the Dell server portfolio, one of the highest volume servers in the world, and often a top choice for IT organizations. The plethora of enterprise-ready peripheral options and highly flexible configurations make the server an easy purchase decision.

By including Tesla GPUs in the top-selling Dell server, GPU computing is now truly available to the mass market.  And, the mass market can now take advantage of GPU acceleration for a broad range of applications.

We benchmarked the new Dell PowerEdge R720s with two Tesla M2090s GPUs using the popular computational bio-chemistry applications NAMD, AMBER, and LAMMPS.  Results are below.

In all benchmarks, the Dell systems that included two GPUs were considerably faster than CPU-only configurations – anywhere from three to six times faster.

So the most popular Dell servers just got better…and much, much faster.

The Dell R720 configuration we benchmarked is:

  • Dual socket Intel Xeon® E5-2650L 1.80GHz (Sandy Bridge), 16 cores total
  • 64 GB, 1066 Mhz DDR3 (32 GB per CPU)
  • Two Tesla M2090 GPUs
  • Redhat Enterprise Linux (RHEL) 6.2
  • NVIDIA driver version 295.20

Detailed data below:

  • Sagar Rawal

    This decision will be appreciated by IT folks around the world looking to be able to sample the benefits of the supercomputing power granted by NVIDIA’s Tesla co-processors.

    As a long time customer of Dell servers, I would not have been able to justify the expense for a more high-end/specialized server, but the R720 would be a ‘no-brainer’ to purchase!

  • Sumit Gupta

    Sagar

    This is precisely the reason why this is such a big deal.   This new server makes it easy for every IT department to deploy GPUs.   

    Its perfect timing too.   This server comes when a wide variety of popular applications are GPU-accelerated.   Examples:
    - Ansys, Abaqus, and Nastran for manufacturing, 
    - bio-sciences applications like AMBER, GROMACS, NAMD, 
    - and finally widely used applications like MATLAB and Mathematica.

    See http://www.nvidia.com/teslaapps for full list.

    What GPU-accelerated application do your users use?

    Sumit

  • Sagar Rawal

    My users currently use life-sciences applications such as AMBER, TeraChem, and GPU-Blast for a wide variety of research projects.

    They run decently on an array of servers containing 2×8 core Xeon servers; but I am excited to sample the 2x to 650x speed ups purported in the marketing materials & white papers I have read.

    I am amazed at how comprehensive the NVIDIA ecosystem has become, from my gaming PC at home, to my tablet the travels with me to work, all the way to my future servers that will be crunching solutions to time sensitive & mission critical problems, they all contain NVIDIA’s reliable and high performance hardware!