Research Essay Unsw GPU computing is something which is fast becoming a very important feature on "video" cards. NVIDIA have a number of useful utilities for CUDA enabled cards which all help to enhance the performance of day to day tasks. Here we have taken a few examples of the different technologies at work to find out how the cards benefit us in real world use.
http://coral.ufsm.br/hans/?purchase-thesis Purchase Thesis The cards are able to take advantage of GPU acceleration for transfers to compatible media players. This means that when we copy a video file to a player Windows recognises if the format is compatible or not. If a conversion is needed we are asked if we would like to convert and copy as one task and should we choose yes the system uses our GPU to enhance performance. This makes the task more simple than converting and copying as two separate tasks, quicker and often cheaper as we don’t need to buy a 3rd party utility to convert the files.
http://euratex.eu/?dissertation-abstracts-online-journal Dissertation Abstracts Online Journal Our test file is a 42 minute AVI file.
http://www.econ.unideb.hu/?research-essay-introduction Research Essay Introduction GPU Assisted Media Conversion Theme Essay Oedipus Rex Our second media encoding test takes a 4GB 1920×1080 file and converts it with Arcsoft Media Convertor 7 to iPad format. One run is completed with CPU only encoding, the second and third with ATI Stream /NVIDIA CUDA (GPU assistance).
Using vReveal we are able to enhance low quality video files such as those from mobile phones or digital cameras and reduce the processing time. If a user has any video which is shaky, dark, fuzzy, etc. then vReveal and an NVIDIA GPU can improve each of these aspects and more (for free!).
David Sedaris Essays Online When looking at the Windows Drag and Drop Transfer and vReveal results there are very clear advantages to be seen from using our GPU to assist the CPU. In the case of our media conversion test the results look less impressive but what they don’t show is that the CUDA results were achieved with less CPU use. The GTX 560 freed up around 5% (on average) of CPU performance for other tasks while the encoding was taking place.