NVIDIA has planned to drop the support for GPUs with Tesla architecture (compute capability 1.x) in upcoming releases of CUDA Toolkit. In fact, GPUs with compute capability 1.0 have already been removed as a target device from CUDA Toolkit 6.5, released in August 2014. With toolkit 6.5, you can no longer specify compute_10, sm_10 for the code generation. Not only this, NIVIDIA has also removed the CC 1.0 from the comparison tables in the Programming Guide 6.5
The default architecture has been changed to compute_20, sm_20 in the rules file of CUDA Toolkit 6.5. As for the rest of Tesla architectures, i.e. CC 1.1, 1.2 and 1.3, they are still supported as a target, but are marked as deprecated. The following warning is generated by the compiler if we attempt to compile the code for Tesla architecture with CUDA 6.5:
CUDACOMPILE : nvcc warning : The ‘compute_11′, ‘compute_12′, ‘compute_13′, ‘sm_11′, ‘sm_12′, and ‘sm_13′ architectures are deprecated, and may be removed in a future release.
The era of the next-generation graphics is finally upon us. If you’ve been hankering after a graphics card upgrade lately and wanted to see what NVIDIA’s reply to AMD’s Radeon HD 7970 is, wait no further. The green squad has taken the cloaks off of their GeForce GTX 680, a new piece of graphics silicon targeted at consumers and the enthusiast mob based on its latest Kepler architecture. NVIDIA claims that its new GTX 680 is the fastest and most power-efficient GPU ever made offering significant performance enhancements over its rivals.
We are pleased to announce the new version of CUVI lib. Our team has been working hard on this release to bring significant improvements over the previous version. You can download the latest release from this link.
Changes from version 0.5:
- Pyramidal Optical Flow (Lucas-Kanade)
- Feature selection (KLT | Harris | Peter)
- Feature tracking (KLT)
- Lightning Adjustment
- Bayer to RGB conversion
CUVI library comes with a lot of image processing building blocks that can be used to build countless applications. For example the Feature Detection & Tracking module of CUVI can be used for motion detection in a live video stream, intrusion detection and tracking an object of interest throughout a video stream or series of cameras. The processing pipeline for motion detection goes as follows:
CUVI version 0.6 is under development. One of its new features is the histogram equalization of images.
Histogram Equalization is an image processing technique for contrast enhancement of low contrast images. Our implementation is 3 to 6 times faster than the one offered by OpenCV 2.2, with same output. The speed depends on image size as well as image intensity values. Currently, only 8 bit greyscale images are supported, and we hope to add support for other image types in future releases of CUVI.