Tag Archives: gpu computing

CUDA 2.0 Available

CUDA 2.0 is available here: CUDA Zone. To take advantage of CUDA, you need Forceware 177.84 or better (177.89). Samples work with GeForce 8/9 series.

NVIDIA CUDA technology is the world’s only C language environment that enables programmers and developers to write software to solve complex computational problems in a fraction of the time by tapping into the many-core parallel processing power of GPUs.
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CUDA: GPU Usage and Data Structure Design

Here is a thesis that discusses the usage of NVIDIA’s CUDA in two applications:
– Einstein@Home: a distributed computing software
– OpenSteer: a game-like application.

CUDA exposes the GPU processing power in the C programming language and can be integrated in existing applications with ease. But in order to exploit the power a GPU can deliver, one has to design the data structures in order to become optimized for CUDA.

Download the thesis here: GPU usage and data structure design (1532)

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NVIDIA CUDA: One Year After

The hardware.fr staff has been invited by NVIDIA to make a point on CUDA. Since this exellent article is written in french, I’ll try to highlight the interesting parts.

One of the new thing in CUDA 2.0 is, according to hardware.fr, the adding in the CUDA compiler of an optimzed profile for multicores x86 CPUs. Currently, CUDA code is splitted in two parts: one part processed by the CPU and the other one by the GPU via the CUDA compiler.
The new thing is that we can now compile the GPU code explicitly for the CPU in order to take advantage of multicores capabilities of the latest CPUs.

Another new thing is Tesla Series 10. NVIDIA has equiped all Tesla 10 products with 4Gb of graphics memory by GPU (recall that GeForce GTX 280 has 1Gb of memory). This boost in memory amount is useful in situations where dataset to be processed are very large.


A Tesla 10 card has only 6-pin PCI-Express power connector (the 8-pin is optional – a GeForce GTX 280 has one 6-pin and one 8-pin an both are required!). The reason is in GPU Computing the GPU has a lower power consumption because some transitors dedicated to 3D graphics are not used.

The article shows also some practical cases where CUDA is used: financial analysis, medical imagery (3D scans) and password recovering.


Read the complete article HERE – in french only
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Real-time Visual Tracker by Stream Processing

This work describes the implementation of a real-time visual tracker that targets the position and 3D pose of objects (specifically faces) in video sequences. The use of GPUs for the computation and efficient sparse-template-based particle filtering allows real-time processing even when tracking multiple faces simultaneously in high-resolution video frames. Using a GPU and the NVIDIA CUDA technology, performance improvements as large as ten times compared to a similar CPU-only tracker are achieved.

Real-time Visual Tracker by Stream Processing by Oscar Mateo Lozano, and Kazuhiro Otsuka. Journal of Signal Processing Systems.


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Thanks to tho for the news.