Late last year AMD and ATI merged
into one company, forming not just a bigger entity but creating a very specific
roadmap altogether. AMD's grand scheme with ATI is to develop a single chip
handling both general
purpose computing as well as graphics. The Fusion project
as AMD calls it is this very goal.
Today, NVIDIA reveals that it is not behind when it comes to general purpose
GPU, or GPGPU, computing. Earlier this year the company announced its complete unified device
architecture, or CUDA, Technology, which laid the groundwork for GPGPU
programming for NVIDIA GPUs. CUDA Technology directly competes with AMD’s Stream Computing
The GPGPU product lineup will be known as Tesla. Tesla is a top to bottom
product lineup consisting of internal PCIe cards and external high-performance
computing, or HPC, systems – Tesla C870, S870 and D870.
The internal PCIe solution consists of an output-less GeForce 8-series
based card on a PCIe x16 card. The Tesla D870 is NVIDIA’s only internal GPGPU
card for desktops. The GPGPU still requires two external PCIe power connectors
and consumes up to 170-watts of power at maximum. NVIDIA claims the Tesla D870
delivers 518 Gigaflops of GPGPU processing power.
Last year, the company announced a highly integrated graphics sub-system named QuadroPlex. Using
a number of GPUs in a tightly integrated system, the QuadroPlex family of
machines accelerated 3D rendering and graphics work. QuadroPlex became the
stepping-stone for the new Tesla C870.
The Tesla C870 GPGPU server packs two GeForce 8-series GPUs in an external
system with packaging similar to the QuadroPlex. The GPGPU delivers one Teraflop of GPGPU computing power while consuming up to 550-watts of power.
Finally, the Tesla S870 comes equipped with four GeForce 8-series GPUs and offers
up to two Teraflops of computing power. The Tesla S870 consumes up to 800-watts of power and fits into a stackable 1U chassis.
Tesla C870 and S870 systems connect to workstation systems via an external PCIe
Gen2 x16 interconnect. The machines contain PCIe switches and can be
daisy-chained with more systems. As with the Tesla D870 GPU card, the Tesla C870 and S870 systems lack output capabilities. Theoretically, customers can
purchase multiple Tesla GPGPU systems and chain them up for big increases in
NVIDIA designed the new Tesla family for everything from graphics rending to
medical research and data farming. At the core level, GPUs are far more
efficient at dealing with parallel computing than general-purpose processors.
This makes Tesla very powerful for cluster-type applications.
The Tesla S870, D870 and C870 carry an MSRP of $12,000, $1,499 and $7,500,
quote: Given that, you do a much better job leaking illegally shared information better than you do as a "journalist."
quote: unless you have just stolen it off a leaked PDF.