nVidia Tesla S1070 16GB GPU Computing Server with 4x M1060 GPU's
|Manufacturer: ||nVidia |
|Model Number: ||Tesla S1070 |
|Regulatory Model: ||P804 |
|Form Factor: ||1U |
Fits 4-post, 19” EIA compatible racks
Rack depth between posts: 28.7 to 36.3 inches
|# of GPU's: ||4 x M1060 |
|Total Memory: ||16GB Memory Total (4GB per GPU) |
|Memory Interface: ||4x 512-bit GDDR3 memory interface (organized as a 512-bit interface per GPU) |
|Floating Point Precision: ||Integer, IEEE 754 single-precision, and IEEE 754 double-precision floating point operations |
|Power Consumption: ||700W |
|Host Adapter Card Interface: ||Connects to host via cabling to a PCI Express x16 or x8 adapter card |
|Host Cards Included: ||No |
|Included: ||S1070 Server Only! Host Cards, Data Cables, Power Cables are not included! |
|Condition: ||Refurbished |
|Warranty: ||30 Days |
High Performance Petascale Computing Systems
NVIDIA Tesla™ S1070 computing system delivers the world’s first teraflop processor, combining breakthrough performance with energy efficiency. A cluster with as few as 250 Tesla systems would have a peak theoretical performance of a petaflop. This system raises the bar for many-core computing in a heterogeneous environment that mixes multi-core CPUs and many-core GPUs for optimized performance. The combination of performance and energy efficiency enables scientists, engineers, and business users to tackle larger problems with the most advanced algorithms. Tesla S1070 delivers incredible performance per watt and can upgrade the performance of your data centers without requiring infrastructure changes for power or cooling and without massive increases in your energy bills.
Feeding The Relentless Demand For HPC Performance
With the world’s first teraflop many-core processor, the NVIDIA® Tesla™ S1070 computing system speeds the transition to energy-efficient parallel computing. With 960 processor cores delivering four teraflops of peak performance, 16 GB of ultra-fast memory for maximum performance with larger data sets, and a standard C compiler that simplifies application development, Tesla S1070 scales to solve the world’s most important computing challenges — more quickly and accurately.
Many-Core Architecture Delivers Optimum Scaling Across HPC Applications
Demand for computing performance in science and industry has far outpaced the ability of traditional CPU to keep up, even with the recent shift to multi-core CPUs. Many-core computing is the architectural answer to this problem, delivering hundreds of cores in a single processor compared to multi-core designs with only four, six, or eight. The availability of processors with hundreds of cores creates a discontinuity in computing because 1U systems with nearly one thousand cores are practical for the first time. This is practical now because the computing cores in a GPU were designed to be part of a massively-parallel system rather than being designed like a traditional CPU core. Dedicated ultra-fast memory for each Tesla processor also improves scalability as total memory bandwidth expands linearly when more GPUs are added to the solution.
High Efficiency Computing Platform for Energy-Conscious Organizations
Unlike any other solution available in the HPC space, the Tesla S1070 delivers four teraflops in a 1U chassis with a typical energy footprint of only 700 watts. This “high density computing” allows data center managers to deliver more performance for their users without new demands on the electrical and thermal capabilities of their existing data centers. Tesla S1070 creates the foundation for new green computing initiatives, while saving you money.
NVIDIA CUDA™ Architecture Unlocks the Power Of GPU Parallel Computing
The CUDA parallel computing architecture enhances performance by offloading computationally-intensive activities from the CPU to the GPU — unlocking the many-core processing power of NVIDIA GPUs to solve the most complex computation-intensive challenges such as protein docking, molecular dynamics, financial analysis, fluid dynamics, structural analysis and many others.