HPC vs. Cloud Computing: Enabling New Opportunities For Chip Designers

You’ve heard the term high-performance computing (HPC) and wondered how it differs from cloud computing. Actually, it doesn’t have to be high-performance computing vs. cloud computing; often, HPC and cloud computing can work together to supercharge your computing power.

Some of the more significant benefits of HPC in the cloud include the following:

Workload Capacity 

HPC today focuses on computationally challenging tasks like running machine learning algorithms or neural networks, sequencing genomic data, or analyzing terabytes of data. HPC in the cloud supports the workloads that make these tasks possible.

Cloud Bursting 

Workloads aren't all the same, and tasks don't require the same resources every day or every hour. Using cloud environments, you can rapidly provision cloud resources when you need them and then throttle them back when you don't.

Backup and Recovery

HPC workloads rely heavily on data. HPC in the cloud supports high-performance computation, backups, and recovery. The cloud can automatically back up your data to hot and cold storage for long-term protection. HPC in the cloud can provide immediate recovery in an emergency or catastrophe with hot backups.

HPC is often required to support electronic design automation (EDA) workloads, such as a cluster of compute nodes, job scheduling software, and a high-performance shared file system. Because the cloud has virtually unlimited CPU and GPU resources, chip designers can run many EDA jobs in parallel. Using this process, designers get results faster and gain added business value.

Key performance improvements for EDA using HPC in the cloud include: 

• Efficient license use EDA tools are often among the most expensive line items for chip makers, so more efficient utilization reduces costs and speeds up time-to-market.
• Increased productivity: By reducing job wait and run times and delivering products faster, HPC in the cloud increases engineers' productivity.
• Costs: Lower infrastructure costs allow you to allocate more resources to research and development and chip design.

Organizations may run multiple chip projects at once, so resource allocation can be tricky. An on-premises compute cluster that's too small can slow time-to-market, while an oversized cluster can waste resources. 

Cloud HPC services often offer elastic clusters that can grow and shrink as your workload changes. By scaling compute resources anytime, you can reduce the risk of licenses becoming unavailable. 

The global market is growing as more industries turn to HPC. It's easier to optimize chip design with cloud performance becoming more reliable, secure, and robust. Chip makers can optimize license utilization, engineering productivity, and costs with elastic clusters in cloud-based HPC, allowing efficient innovation in the next generation of chips.

Synopsys Cloud combines HPC in the cloud with unlimited access to EDA software licenses. By partnering with top cloud providers, we have optimized infrastructure configurations, removing the guesswork so that EDA can deploy in the cloud rapidly and securely.