Desktop Supercomputers : The CUDA-enabled EDA (Near) Future

In my first ever post, Need for Speed (way back in Oct'2007), I proposed that specialized EDA platforms could be built using Nvidia's CUDA technology. Since then, Nvidia's CUDA technology has a steadily increasing list of EDA adopters.

• February 2008: Gauda uses Nvidia's CUDA platform (and distributed processing) to accelerate OPC by 200x.
  
• April 2008: Nascentric announced the availability of a GPU-accelerated spice simulator (OmegaSim GX) using none other than Nvidia's CUDA technology.
  
• August 2008: Agilent announced the use of CUDA technology for the acceleration of signal integrity simulations in their ADS (Advanced Design System) Transient Convolution Simulator.

Is this a flash in the pan or is CUDA-enabled EDA here to stay? Why use CUDA?

• C-Based SDK allows for easy porting of code to the CUDA platform
• Ecosystem of tools and applications built on the CUDA platform. Nvidia's doing its part by hosting CUDA-based code design contests. Right now, you can read and download academic papers on the utilization of the CUDA platform for statistical timing analysis and graph algorithms.
• Cost-effective computation is perhaps the biggest thing going for the CUDA platform. Where else can you get a 100x improvement in runtime for a mere $600?
  

Despite these advantages, there are some missing pieces that would hold CUDA back from really taking the EDA world by storm. My wishlist for CUDA is the following:

• Backward compatibility is key to a low-risk path to adoption. Without it, who's going to risk porting their code base to CUDA hoping that their customers will move to CUDA-enabled platforms? With backward compatibility comes a great hook: "Use our tools on your current platform but you can get a 100x improvement in runtime just by buying a PCI card". Right now, that's not the case. Nascentric, for example, offers OmegaSim and OmegaSim-GX as two separate tools. Wouldn't it be great if the same code could run on both platforms but one runs a whole lot faster because of CUDA?
• Native support for distributed processing could bump performance up even higher. Graphics processors are built to solve "embarrassingly" parallel problems. It's not really much of a leap to distribute the workload amongst multiple graphics processors. The CUDA pitch (do more with less money) becomes that much sweeter.

Tags : ASIC, VLSI