|
October 2014 |
|
The NVIDIA Quadro K5200 provides great application performance and capability which makes it faster and helps accelerating applications like 3D models, render complex scenes and simulate large datasets.
| Memory Size |
8GB GDDR5 |
| Memory Interface |
256-bit |
| Memory Bandwidth |
192 GBps |
| NVIDIA® CUDA™ Parallel Processor Cores |
2304
|
| Max Power Consumption |
150W |
| Thermal Solution |
Ultra-quiet active fansink |
| Form Factor |
110 mm H × 265 mm L (4.376” H ×10.50” L)
Dual Slot, Full Height
|
| # Simultaneous Displays |
4 |
| Display Connectors |
1 × DVI-I DL
1 × DVI-D DL
2 × DP1.2
|
| Graphics APIs |
Shader Model 5.0, OpenGL 4.5, 11.2
|
| Compute APIs |
CUDA, DirectCompute, OpenCL
|
Energy star Enabling
|
Yes
|
NVIDIA nView™ Desktop
Management Software Compatibility
|
Yes
|
| Mosaic Mode |
Yes
|
| HDCP Support |
Yes
|
| NVIDIA 3D Vision and 3D Vision Pro |
Yes
|
| 3D Stereo Support |
Yes
|
| HD SDI Capture/Output Compatibility |
Yes
|
| NVIDIA GPUDirect™ Support |
Yes
|
| Quadro Sync Compatibility |
Yes
|
Click here to return
|
|
|
October 2014 |
Tesla K40 GPU is equipped with 12 GB of memory and ideal for the most demanding HPC and big data problem sets. Tesla K40 has Nvidia GPU boost feature which increases the clock speed of all CUDA cores, providing a 20% to 30% performance boost for many common applications
Features and Benefits:
- NVIDIA GPU Boost: On-demand performance boost to attain up to 25% additional application speedup
- Streaming Multiprocessor (SMX): Perform 3X the workload with the same power budget.
- NVIDIA Kepler Architecture: Accelerate all your applications with the world’s fastest, most efficient HPC architecture.
- ECC Memory: Address a critical requirement for computing accuracy and reliability in supercomputing and data centers.
- System Monitoring Features: Manage GPU processors in computing systems using widely used cluster/grid-management solutions.
| Specification |
Description |
GPU Chips
|
GK110B
|
Cuda Cores
|
2880 |
Single Precision Performance
|
4.2 TFLPOS |
| Double Precision Performance |
1.439 TFLPOS |
Memory Size
|
12GB GDDR5
|
Memory Bandwidth(ECC off)
|
288 Gbytes/sec
|
Memory I/O
|
384-bit |
Memory Clock
|
3004 MHz
|
SMX Units
|
15 |
GPU Boost Feature
|
Yes |
| Architecture features |
SMX, Dynamic Parallelism, Hyper-Q |
System Interface
|
PCI Express 3.0 x16 |
| Energy Star Enabling |
Yes |
Compute Capabilty
|
3.5 |
Semiconductor Manufacturing Size
|
28nm |
Wattage (TDP)
|
235W |
GPU Applications
|
> Reservoir simulation
> CAE (structural analysis)
> Molecular dynamics, Numerical analytics
> Computational visualization (ray tracing)
|
Thermal Solution
|
Ultra-quiet Active Fansink
|
Form Factor
|
110 mm (H) × 265 mm (L) - Dual Slot, Full-Height
|
|
|
|
October 2014 |
|
Tesla
K20 is designed for double precision applications and the broader
supercomputing market, the Tesla K20 delivers 3x the double precision
performance compared to the previous generation Fermi-based Tesla M2090. Tesla
K20 features a single GK110 Kepler GPU that includes Dynamic parallelism and
Hyper-Q. With more than one teraflop of peak double precision performance, the
Tesla K20 is ideal for a wide range of high performance computing workloads
including climate and weather modelling, CFD, CAE, computational physics,
biochemistry simulations and computational finance.
TESLA K20 - GPU Computing Accelerator Common Features
ECC Memory Error Protection
|
Meets a critical requirement
for computing accuracy and reliability in datacenters and supercomputing
centers. External DRAM is ECC protected in Tesla K10 and both external
and internal memories are ECC protected in Tesla K20X. |
| System Monitoring Features |
Integrates the GPU subsystem with the host system’s monitoring
and management capabilities such as IPMI or OEM-proprietary tools. IT
staff can thus manage the GPU processors in the computing system using
widely used cluster/grid management solutions. |
| L1 and L2 caches |
Accelerates algorithms such as
physics solvers, ray-tracing, and sparse matrix multiplication where
data addresses are not known beforehand |
| Asynchronous Transfer with dual DMA engines |
Turbocharges system performance by transferring data over the PCIe bus while the computing cores are crunching other data. |
| Flexible programming environment with broad support of programming languages and APIs |
Choose OpenACC, CUDA toolkits
for C, C++, or Fortran to express application parallelism and take
advantage of the innovative Kepler architecture. |
Specifications:
| Specification |
Description |
GPU Chips
|
GK110
|
Cuda Cores
|
2496 |
Single Precision Performance
|
3.52 TFLPOS |
| Double Precision Performance |
1.17 TFLPOS |
Memory Size
|
5GB GDDR5
|
Memory Bandwidth(ECC off)
|
208 Gbytes/sec
|
Memory I/O
|
320-bit |
Memory Clock
|
2600 MHz
|
SMX Units
|
13 |
| Architecture features |
SMX, Dynamic Parallelism, Hyper-Q |
| System Interface |
PCI Express 2.0 x16 |
Energy Star Enabling
|
Yes
|
Compute Capabilty
|
3.5 |
| Semiconductor Manufacturing Size
|
28nm |
| Wattage (TDP)
|
225 |
| GPU Applications
|
> Reservoir simulation
> CAE (structural analysis)
> Molecular dynamics, Numerical analytics
> Computational visualization (ray tracing)
|
| Thermal Solution
|
Ultra-quiet Active Fansink
|
| Form Factor
|
110 mm (H) × 265 mm (L) Dual Slot, Full-Height
|
|
|
|
October 2014 |
|
Tesla
K20 is designed for double precision applications and the broader
supercomputing market, the Tesla K20 delivers 3x the double precision
performance compared to the previous generation Fermi-based Tesla M2090. Tesla
K20 features a single GK110 Kepler GPU that includes Dynamic parallelism and
Hyper-Q. With more than one teraflop of peak double precision performance, the
Tesla K20 is ideal for a wide range of high performance computing workloads
including climate and weather modelling, CFD, CAE, computational physics,
biochemistry simulations and computational finance.
TESLA K20 - GPU Computing Accelerator Common Features
ECC Memory Error Protection
|
Meets a critical requirement
for computing accuracy and reliability in datacenters and supercomputing
centers. External DRAM is ECC protected in Tesla K10 and both external
and internal memories are ECC protected in Tesla K20X. |
| System Monitoring Features |
Integrates the GPU subsystem with the host system’s monitoring
and management capabilities such as IPMI or OEM-proprietary tools. IT
staff can thus manage the GPU processors in the computing system using
widely used cluster/grid management solutions. |
| L1 and L2 caches |
Accelerates algorithms such as
physics solvers, ray-tracing, and sparse matrix multiplication where
data addresses are not known beforehand |
| Asynchronous Transfer with dual DMA engines |
Turbocharges system performance by transferring data over the PCIe bus while the computing cores are crunching other data. |
| Flexible programming environment with broad support of programming languages and APIs |
Choose OpenACC, CUDA toolkits
for C, C++, or Fortran to express application parallelism and take
advantage of the innovative Kepler architecture. |
Specifications:
| Specification |
Description |
GPU Chips
|
GK110
|
Cuda Cores
|
2496 |
Single Precision Performance
|
3.52 TFLPOS |
| Double Precision Performance |
1.17 TFLPOS |
Memory Size
|
5GB GDDR5
|
Memory Bandwidth(ECC off)
|
208 Gbytes/sec
|
Memory I/O
|
320-bit |
Memory Clock
|
2600 MHz
|
SMX Units
|
13 |
| Architecture features |
SMX, Dynamic Parallelism, Hyper-Q |
| System Interface |
PCI Express 2.0 x16 |
Energy Star Enabling
|
Yes
|
Compute Capabilty
|
3.5 |
| Semiconductor Manufacturing Size
|
28nm |
| Wattage (TDP)
|
225 |
| GPU Applications
|
> Reservoir simulation
> CAE (structural analysis)
> Molecular dynamics, Numerical analytics
> Computational visualization (ray tracing)
|
| Thermal Solution
|
Ultra-quiet Active Fansink
|
| Form Factor
|
110 mm (H) × 265 mm (L) Dual Slot, Full-Height
|
|
|
|
October 2014 |
Tesla K40 GPU is equipped with 12 GB of memory and ideal for the most demanding HPC and big data problem sets. Tesla K40 has Nvidia GPU boost feature which increases the clock speed of all CUDA cores, providing a 20% to 30% performance boost for many common applications
Features and Benefits:
- NVIDIA GPU Boost: On-demand performance boost to attain up to 25% additional application speedup
- Streaming Multiprocessor (SMX): Perform 3X the workload with the same power budget.
- NVIDIA Kepler Architecture: Accelerate all your applications with the world’s fastest, most efficient HPC architecture.
- ECC Memory: Address a critical requirement for computing accuracy and reliability in supercomputing and data centers.
- System Monitoring Features: Manage GPU processors in computing systems using widely used cluster/grid-management solutions.
| Specification |
Description |
GPU Chips
|
GK110B
|
Cuda Cores
|
2880 |
Single Precision Performance
|
4.2 TFLPOS |
| Double Precision Performance |
1.439 TFLPOS |
Memory Size
|
12GB GDDR5
|
Memory Bandwidth(ECC off)
|
288 Gbytes/sec
|
Memory I/O
|
384-bit |
Memory Clock
|
3004 MHz
|
SMX Units
|
15 |
GPU Boost Feature
|
Yes |
| Architecture features |
SMX, Dynamic Parallelism, Hyper-Q |
System Interface
|
PCI Express 3.0 x16 |
| Energy Star Enabling |
Yes |
Compute Capabilty
|
3.5 |
Semiconductor Manufacturing Size
|
28nm |
Wattage (TDP)
|
235W |
GPU Applications
|
> Reservoir simulation
> CAE (structural analysis)
> Molecular dynamics, Numerical analytics
> Computational visualization (ray tracing)
|
Thermal Solution
|
Ultra-quiet Active Fansink
|
Form Factor
|
110 mm (H) × 265 mm (L) - Dual Slot, Full-Height
|
|
|
|
|
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