OpenCL Particle Collision Simulation
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.
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 (1366)
AMD/ATI plans to update the Stream Software Development Kit in order to offer a fast solution to produce GPU accelerated applications. Stream Software Development Kit will have an enhanced support for C/C++ and new APIs, including Microsoft’s DirectX 11 and OpenCL.
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
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.
Thanks to tho for the news.
CUDA was announced along with G80 in November 2006, released as a public beta in February 2007, and then finally hit the Version 1.0 milestone in June 2007 along with the launch of the G80-based Tesla solutions for the HPC market. Today, Beyond3D looks at the next stage in the CUDA/Tesla journey: GT200-based solutions, CUDA 2.0, and the overall state of NVIDIA’s HPC business.
Read the article HERE.