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1 X : Nvidia Tesla K10 GPU 8GB GDDR5 3,072 CUDA Cores GPU Acceleration card
1 X : Nvidia Tesla K10 GPU 8GB GDDR5 3,072 CUDA Cores GPU Acceleration card
** ( These are working pulled from Special GPU Servers / No PCI bracket will be included GPU Only )
CUDA PARALLEL PROCESSING CORES = 3072 (2 x 1536)
PROCESSOR CORE CLOCK = 745 MHz
PEAK DOUBLE PRECISION FLOATING POINT PERFORMANCE (BOARD) =0.19 teraflops
PEAK SINGLE PRECISION FLOATING POINT PERFORMANCE (BOARD) 4.58 teraflops NUMBER OF GPUS = 2 x GK104s
PROCESSOR CORE CLOCK = 706 MHz
FRAME BUFFER MEMORY 8 GB
MEMORY BANDWIDTH = 320 GBytes/sec
EXTERNAL CONNECTORS None INTERNAL CONNECTORS AND HEADERS =
(1) 6-pin PCI Express power connector
GRAPHICS BUS = PCI Express 2.0 x16
FORM FACTOR = 110 mm (H) x 265 mm (L)Dual Slot
BOARD POWER = 225 W
THERMAL SOLUTION = Passive
PROCESSOR CORE CLOCK = 745 MHz
PEAK DOUBLE PRECISION FLOATING POINT PERFORMANCE (BOARD) =0.19 teraflops
PEAK SINGLE PRECISION FLOATING POINT PERFORMANCE (BOARD) 4.58 teraflops NUMBER OF GPUS = 2 x GK104s
PROCESSOR CORE CLOCK = 706 MHz
FRAME BUFFER MEMORY 8 GB
MEMORY BANDWIDTH = 320 GBytes/sec
EXTERNAL CONNECTORS None INTERNAL CONNECTORS AND HEADERS =
(1) 6-pin PCI Express power connector
(1) 8-pin PCI Express power connector
GRAPHICS BUS = PCI Express 2.0 x16
FORM FACTOR = 110 mm (H) x 265 mm (L)Dual Slot
BOARD POWER = 225 W
THERMAL SOLUTION = Passive
Fastest accelerators
Solve your most demanding High-Performance Computing (HPC) challenges with NVIDIA Tesla family of GPUs. They’re built on the
NVIDIA Kepler™ compute architecture and powered by NVIDIA CUDA
, the world’s most pervasive parallel computing model. This
makes them ideal for delivering record acceleration and more efficient compute performance for big data applications in fields
including seismic processing; computational biology and chemistry; weather and climate modeling; image, video and signal processing;
computational finance, computational physics; CAE and CFD; and data analytics.
Optimized for single precision applications, the Tesla K10 is a throughput monster based on the ultra-efficient GK104 Kepler GPU.
The accelerator board features two GK104 GPUs and delivers up to 2x the performance for single precision applications compared to the
previous generation Fermi-based Tesla M2090 in the same power envelope. With an aggregate performance of 4.58 teraflop peak single pre
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cision and 320 gigabytes per second memory bandwidth for both GPUs put together, the Tesla K10 is optimized for computations in seismic,
signal, image processing, and video analytics.
With teraflops of single and double precision performance, NVIDIA® Kepler GPU Computing Accelerators are the world’s fastest and most
efficient high performance computing (HPC) companion processors. Based on the Kepler compute architecture, which is 3 times higher per
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formance per watt than the previous “Fermi” compute architecture, the Tesla Kepler GPU Computing Accelerators make hybrid computing
dramatically easier, and applicable to a broader set of computing applications. NVIDIA Tesla GPUs deliver the best performance and power
efficiency for seismic processing, biochemistry simulations, weather and climate modeling, image, video and signal processing, computational
finance, computational physics, CAE, CFD, and data analytics.
The innovative design of the Kepler compute architecture includes:
>> SMX (streaming multiprocessor) design that delivers up to 3x more performance per watt compared to the SM in Fermi. It also delivers 1
petaflop of computing in just 10 server racks.
>> Dynamic Parallelism capability that enables GPU threads to automatically spawn new threads. By adapting to the data without going back
to the CPU, it greatly simplifies parallel programming and enables GPU acceleration of a broader set of popular algorithms, like adaptive mesh
refinement (AMR), fast multipole method (FMM), and multigrid methods.
>> Hyper-Q feature that enables multiple CPU cores to simultaneously utilize the CUDA cores on a single Kepler GPU, dramatically increasing
GPU utilization, slashing CPU idle times, and advancing programmability. Ideal for cluster applications that use MPI.
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