GPU-Powered SPICE – Understanding the Cost

To deploy a GPU-based SPICE solution, you need to understand the costs involved. To get your hands on this new report analyzing this specific issue, all you need to do is attend Empyrean’s upcoming webinar, “GPU-Powered SPICE:  The Way Forward for Analog Simulation,” which will be held on Thursday, August 8, 2019, at 10:00 am (PDT). This webinar is the first webinar in the SemiWiki Webinar series. Click here to sign up using your work email information.

SPICE (Simulation Program with Integrated Circuit Emphasis) was initially developed at the Electronics Research Laboratory of the University of California, Berkeley by Laurence Nagel in the early 1970s. SPICE1, the version of SPICE first presented at a conference in 1973, was largely an effort to have a circuit simulator without ties to the Department of Defense, essentially keeping the electrical engineering department in step with the rest of the anti-war movement at UC Berkeley. SPICE1 was coded in FORTRAN. I believe it ran on the IBM mainframe computer available to the department.

In 1989, SPICE3 was released, the first version of SPICE written in C. As the code has long been available under the standard BSD license, SPICE has been available via open-source, and it was ported to many CPU-based systems as commercial versions started popping up from the emerging EDA industry during the 1990s. There are still many versions of SPICE out there, commercial, academic, and proprietary.

Unfortunately, SPICE being ported to newer computers was still insufficient to keep up with the increasingly large circuits engineers wanted to simulate. Simulation times kept getting longer. Less accurate versions of SPICE were then invented, generally referred to as fast-SPICE. They traded off some accuracy for improved speed of analysis. This has continued to be the state of the market until just recently.

For the last 15 years or so, companies have been trying to find ways of harnessing the incredible computation powers of GPUs (Graphics Processing Units) as an alternative to CPUs. CPU-based computers have typically one-to-eight processors at their disposal. GPUs have hundreds of processors, though they cannot do general-purpose computing well. The idea when programming a GPU is to give it a large sequence of calculations to do with little branching (e.g., avoid IF statements). GPUs are data throughput engines. Think of them as the dragsters of processing units – they go extremely fast but do not turn very well. So, the longer you can only feed the GPU data and let it just calculate it runs fast. When you ask a GPU to branch, it slows down. It takes experienced GPU programming skills to re-write code written for a general-purpose processor (CPU) and make it perform well on the GPU. Not all types of algorithms can be ported to a GPU and run faster, but matrix solving, which is critical in SPICE, will be one of them. I have seen this before in photo-lithography simulations. It makes sense it can be done with SPICE as well.

Empyrean is working on a paper which goes into comparing the cost differences between its GPU-accelerated ALPS-GT™ SPICE simulator and CPU-based simulators. Keep in mind that Empyrean already boasts the fasted CPU-based SPICE simulator Empyrean ALPS™, which was voted “Best of DAC 2018” for being the fastest SPICE simulator with True SPICE accuracy.  Empyrean ALPS™ has displayed 3X – 8X faster performance than the next fastest SPICE simulator in the market. Empyrean claims ALPS-GT is an order of magnitude faster with the same True SPICE accuracy. There are the so-called fast-SPICE simulators that sacrifice some accuracy to achieve faster throughput. That is not what Empyrean’s tools are. They provide true SPICE accuracy.

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