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GPUs vs. CPUs for EDA simulations

Posted: 15 Apr 2009     Print Version  Bookmark and Share

Keywords:simulation  processor clocks  CPU  GPU 

A typical application may run 1.9 times faster with two cores (95 percentage efficiency), 3.1 times faster with four cores (78 percentage efficiency) and 4.5 times with eight cores (56 percentage efficiency). As more cores are added, designers can't utilise all of them efficiently. One of the major reasons for the lack of scalability is memory bandwidth limitations—the computer's main memory can't feed them data fast enough to keep them fully utilised.

A GPU can be inexpensively added onto an existing computer, a leading-edge GPU only costs Rs.17,401.72 ($350). There's no need to replace the entire computer. With this upgrade, computer applications, including EDA tools, get access to a processor that can compute at 1 teraflop.

The leading multi-core CPU can only deliver 100 gigaflops. This gives the GPU a 10x performance advantage over the CPU. (For a historical perspective, the first computer to deliver 1 teraflops was ASCI Red at Sandia National Laboratory, which became operational in December 1996. It cost Rs.273.46 crore ($55 million) and took up 2,500 square feet of floor space.)

GPUs have excelled at providing high memory bandwidth. A GPU needs to transform and render millions of geometry primitives 60 times per second to keep video output running in real time. GPUs have their own dedicated memory and super-wide memory bus (512 bits for GPU versus 64 bits for a CPU) to feed the data to the GPU. A state-of-the-art GPU has a memory bandwidth of 159 GBs/s, compared with 25 to 32 GBs/s for the leading multi-core CPU. That gives the GPU a 5- to 6-fold advantage in memory bandwidth.

The programming environment for GPUs makes them relatively easy to program on some of highly parallel EDA applications. Some of the core calculations that are handled by GPUs can run 20 times faster than on a CPU. Not all applications will run on the GPU, but critical bottlenecks can be identified and moved from the CPU to the GPU. We've been working with Acceleware and Nvidia to port our EMPro and ADS Transient-Convolution Simulator products to GPUs. Nvidia not only makes the chips but are also a big consumer of computing power to design their next-generation chips. Technological bootstrapping!

Software developers have been helping us port the code, and chip designers have been our lead beta site. They've achieved a 14-fold improvement in simulation time.

Not quite 26 hours to 26 minutes, but we're getting there.

-Larry Lerner, R&D senior manager
Agilent Technologies


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