The NVIDIA Quadro NVS 310 graphic card was released on June 2012. This video card features a graphic processing unit stacked with 48 cuda cores running at a clock speed of 550 MHz. The Quadro NVS 310 is equipped with 512 MB DDR3 memory. This graphic card memory has a bandwidth of up to 14.0 GB/s.
Performance And Power
The Quadro NVS 310 video card can deliver a computing power of up to 124.8 GFLOPS of single precision computations. As per the video rendering performance, this graphic card can reach a pixel fill rate of 2.2 Gigapixels/s and a texture fill rate of 4.4 Gigatexels/s. When operating, the Quadro NVS 310 video card can consume a power of 20 W .
The Quadro NVS 310 graphic card can be plugged to a motherboard through the support of a PCI-E 2.0 x 16 bus. More over, Furthermore, this card supports the following display connectors: "2 x DisplayPort" .
The Quadro NVS 310 supports the following libraries: " DirectX 11.0, OpenCL 1.1, Shader Model 5.0 ". OpenGL is a cross language and multi-platform application programming interface for rendering 2D and 3D vector graphics. OpenGL is used to interact with a GPU to harness Hardware-accelerated rendering and eliminates the need to recompile shaders making it an efficient and performance oriented standard. Microsoft DirectX is an Application programming Interface for the Windows operating systems which Integrates well with AMD and NVIDIA GPUs. The Microsoft DirectX 10 is a significant step in 3D graphics. It features a highly optimized runtime, powerfull geometry sharders, and many other features. The Microsoft DirectX 11, introduced with Windows 7, brings numerous improvement over its precedessor DirectX 10. It is designed to be more efficient, supports sophisticated shading and texturing techniques and levearage the use of multi processors, resulting in smoother 3D animations. The open computing language (OpenCL) is programming interface used for general purpose programming across CPUs and GPUs and other processors. The standard gives developers the portability and efficiency of heterogeneous processing platforms.