Need for Speed: NVidia CUDA promises faster graphics processing

NVIDIA Corporation has unveiled NVIDIA CUDA technology, what the company calls a fundamentally new architecture for computing on NVIDIA graphics processing units (GPUs), and the industry’s first C-compiler development environment for the GPU.

The company says the new approach, where hundreds of on-chip processor cores simultaneously communicate and cooperate to solve complex computing problems up, will increase processing speeds by as much as 100 times faster than traditional approaches. This breakthrough architecture is complemented by another first-the NVIDIA C-compiler for the GPU. This complete development environment gives developers the tools they need to solve new problems in computation-intensive applications such as product design, data analysis, technical computing, and game physics.

Available on the new GeForce 8800 graphics card and future NVIDIA Quadro Professional Graphics solutions, computing with CUDA transcends the limitations of traditional GPU stream computing by enabling GPU processor cores to communicate, synchronize, and share data.

“Our customers, including every cell phone manufacturer in the world, see the value in using NVIDIA GPUs with Acceleware’s GPU-accelerated solver, to speed up their time to market,” said Dr. Nicolas Chavannes, director software for Schmid and Partner Engineering AG (SPEAG). “The level of computing performance now achievable with CUDA-enabled GPUs, will positively impact our customers’ bottom lines.”

CUDA-enabled GPUs offer dedicated features for computing, including the Parallel Data Cache, which allows 128, 1.35GHz processor cores in newest generation NVIDIA GPUs to cooperate with each other while performing intricate computations. Developers access these new features through a separate computing driver that communicates with DirectX and OpenGL, and the new NVIDIA C compiler for the GPU, which obsoletes streaming languages for GPU computing.

A CUDA-enabled GPU operates as either a flexible thread processor, where thousands of computing programs called threads work together to solve complex problems, or as a streaming processor in specific applications such as imaging where threads do not communicate. CUDA-enabled applications use the GPU for fine grained data-intensive processing, and the multi-core CPUs for complicated coarse grained tasks such as control and data management.

“CUDA gives us a whole new level of computing capability and enables closer access to the hardware,” said Ryan Schneider, CTO of Acceleware Corp.