Thursday, June 21, 2007

Nvidia Tesla, hardcore graphic superchip!


Nvidia finally releases it's entry to the high performance chips arena with Tesla, which Nvidia introduced as making high-density parallel processing capabilities available in workstation computers. It's intended application is focused on high-performance computing environments such as geosciences, molecular biology, or medical diagnostics. These fields require very powerful computers to continuously calculate predictions and give exact values, especially in the field of medicine.

The power of supercomputing coupled with Nvidia's advanced capabilities, enable the Tesla to graphically rendered some of the most complex graphics, examples include complex AI neural nets, and a super detailed view of biological specimens.

Tesla is currently available in two packages called
Deskside Supercomputer and the Computing Server, the workstation and server versions, respectively. The Deskside Supercomputer includes two Tesla GPUs, is attached to a PC terminal or worksta, and delivers up to 8 teraflops of processing power. A teraflop is the processing of a trillion floating point operations per second.

Taken from Nvidia's Tesla Page

NVIDIA® Tesla™ D870 deskside supercomputer is the first to bring a massively multi-threaded architecture to high performance computing (HPC) applications for scientists, analysts and other technical professionals.

The Tesla D870 deskside supercomputer connects easily to a certified PCI Express workstation creating an advanced HPC solution powered by two 128-processor computing core GPUs.

With a C-language development environment for the GPU, a suite of developer tools, and the world’s largest ISV development community for GPU computing, Tesla D870 deskside supercomputer allows professionals to develop applications faster and to deploy them across multiple generations of processors.

The Tesla D870 deskside supercomputer is used in tandem with multi-core CPU systems to create a flexible computing solution that fits seamlessly into your existing IT infrastructure.

Technical Specifications
Product Tesla D870
Form Factor Deskside System
# of Tesla GPUs 2
Total Dedicated Memory 3 GB (1.5 GB GDDR3 per GPU)
Peak Flops Over 500 gigaflops per GPU
Floating Point Precision IEEE 754 single-precision floating point
Host Adapter Card PCI Express x16 or x8, Small Form Factor, Passive (10W)
Power 520W max., 100-240 VAC Autosensing
Acoustics 40 dB
Weight ~19 lb


Taken from Nvidia's Tesla Page

NVIDIA® Tesla™ S870 GPU computing server is the first to bring a massively multi-threaded architecture to high performance computing (HPC) applications for scientists, analysts and other technical professionals.

The Tesla S870 GPU computing server is a slim 1U form factor to fit into an enterprise server cluster environment. In addition, it easily scales to solve the most complex, data-intensive HPC problems.

Featuring four 128-processor computing core GPUs, a C-language development environment for the GPU, a suite of developer tools, and the world's largest GPU ISV development community, the Tesla S870 GPU computing server allows professionals to develop applications faster and to deploy them across multiple generations of processors.

The Tesla S870 GPU computing server is used in tandem with multi-core CPU systems to create a flexible computing solution that fits seamlessly into your existing IT infrastructure.

Technical Specifications
Product Tesla S870
Form Factor Standard 19" 1U rack-mount chassis
# of GPUs 4
Total Dedicated Memory 6 GB (1.5 GB GDDR3 per GPU)
Peak Flops Over 500 gigaflops per GPU
Floating Point Precision IEEE 754 single-precision floating point
Power 550W typ. (800W max), 100-240 VAC Autosensing
Host Adapter Card PCI Express x16 or x8, Small Form Factor, Passive (10W)

But all this comes at a relatively steep price tag of course! The Tesla GPU in it's lonesome cost about $1499, the Deskside Supercomputer at $7500 and the Computing Server at $12000! Tesla is expected to ship by November or December, if you're itching to get ordering information, visit Nvidia's "Where to buy" page.




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