NVIDIA Unveils Next Generation CUDA GPU Architecture, Codenamed

By DE Editors

NVIDIA Corp. has introduced its next generation CUDA GPU architecture, codenamed “Fermi.” An entirely new design, the Fermi architecture is the foundation for computational graphics processing units (GPUs), delivering breakthroughs in both graphics and GPU computing.

“NVIDIA and the Fermi team have taken a giant step towards making GPUs attractive for a broader class of programs,” said Dave Patterson, director Parallel Computing Research Laboratory, U.C. Berkeley and co-author of Computer Architecture: A Quantitative Approach. “I believe history will record Fermi as a significant milestone.”

Presented at the company’s inaugural GPU Technology Conference, in San Jose, California, Fermi delivers a feature set that accelerates performance on a wider array of computational applications than before. Joining NVIDIA’s press conference was Oak Ridge National Laboratory which announced plans for a new supercomputer that will use NVIDIA GPUs based on the Fermi architecture. Fermi also garnered the support of Bloomberg, Cray, Dell, HP, IBM and Microsoft.

“It is completely clear that GPUs are now general purpose parallel computing processors with amazing graphics, and not just graphics chips anymore,” said Jen-Hsun Huang, co-founder and CEO of NVIDIA. “The Fermi architecture, the integrated tools, libraries, and engines are the direct results of the insights we have gained from working with thousands of CUDA developers around the world. We will look back in the coming years and see that Fermi started the new GPU industry.”

As the foundation for NVIDIA’s family of next generation GPUs, Fermi features a host of new technologies, including:

• C++, complementing existing support for C, Fortran, Java, Python, OpenCL, and DirectCompute.
• ECC, a critical requirement for datacenters and supercomputing centers deploying GPUs on a large scale
• 512 CUDA Cores featuring the new IEEE 754-2008 floating-point standard
• 8x the peak double precision arithmetic performance over NVIDIA’s last generation GPU.
• NVIDIA Parallel DataCache, a cache hierarchy in a GPU that speeds up algorithms such as physics solvers, raytracing, and sparse matrix multiplication where data addresses are not known beforehand.
• NVIDIA GigaThread Engine with support for concurrent kernel execution, where different kernels of the same application context can execute on the GPU at the same time.
• Nexus, an integrated heterogeneous computing application development environment within Microsoft Visual Studio.
 

For more information, visit NVIDIA.

Sources: Press materials received from the company and additional information gleaned from the company’s website.