You can apply HPC to many fields, including computational fluid dynamics, building, transaction processing, and virtual prototype testing.
HPC is especially useful in this final example of reducing the need for physical tests. HPC enables advanced, complex simulations rather than performing them in real life. In the automotive industry, for example, this process allows for cheaper and faster simulation setups instead of physical automotive crash accident tests. This convenience is also applicable to circuit boards and EDA.
HPC drives EDA-based innovation. It is the groundbreaking force behind scientific discoveries that improve the quality of life for individuals around the world. HPC is also often cheaper, as it provides faster answers. It is a reasonable choice for small businesses and startups that can afford to run HPC workloads, scaling up and down as needed.
All of these aforementioned benefits make HPC a key element in the feature of chip design and EDA usage. Chip design is both computationally and memory intensive, requiring multiple design phases. Frontend tools may be bound to single threads and CPUs, while backend tools may rely on optimized storage and bountiful memory. EDA simulations may also utilize 3D modeling, fluid dynamics, and other computational intensive processes that necessitate high-performance computing data solutions.
Compute farms with a fixed-size model can result in jobs waiting in queue for a license or the right-sized computational node. Key performance improvements through HPC for EDA include:
- Efficient license utilization. As EDA tools are often one of the most expensive line items, more efficient utilizations reduce cost and accelerate time-to-market for new chips.
- High productivity. As opposed to hiring and training more engineers, HPC increases current engineers’ productivity by reducing job wait time and run time and delivering products to market faster.
- Infrastructure cost. With lower infrastructure costs, you can allot resources for research and development, driving innovation.
Since organizations often run multiple IC projects at the same time, resource allocation can be complicated. An on-premises cluster that is too small can result in slower time-to-market, while oversizing can lead to wasted resources. Many cloud HPC services offer an elastic cluster that grows and shrinks to fit an organization’s workload. The ability to scale compute resources at any time enables chip design companies to reduce the risk of unavailable licenses. Furthermore, when tasks are complete and instances are idle, elastic scaling can terminate them to optimize costs.
As more industries, including chip design, are turning to HPC, the global HPC market continues to expand. With cloud performance becoming more reliable, secure, and powerful, companies can optimize their chip design process. Through elastic clusters in cloud-based HPC, IC enterprises can optimize license utilization, engineering productivity, and costs, allowing for efficient innovation in the next generation of integrated circuits.