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High-performance computing clusters facilitate the processing of large amounts of data faster than a normal compute cluster, enabling enterprises to run complex workloads, such as chip design and verification.
With high-performance computing (HPC) clusters, computing power can be aggregated faster and more efficiently than traditional infrastructure. High-speed data processing is achieved by combining numerous compute servers and storage devices.
HPC clusters consist of hundreds or thousands of computer servers, called nodes, connected through a network. Each node works in parallel, improving overall processing capability. Here we’ll dive into the advantages of using HPC clusters in the cloud instead of on-premises, which include cost efficiency, scalability, and optimized workloads.
High-Performance Computing Clusters and EDA Workflows
Chip design and electronic design automation (EDA) tools use HPC clusters extensively. Chip design requires multiple design phases because it is computationally and memory intensive.
In EDA simulations, for instance, RTL or circuit simulations are computationally intensive processes that require HPC solutions. Many simulations run on large-scale server farms with hundreds of servers and thousands of cores.
Cloud service providers offer elastic HPC clusters that can grow and shrink based on an organization's workload. Companies that design chips can reduce the risk of unavailable licenses by scaling compute resources anytime. Elastic scaling can terminate instances when tasks are complete, and instances are idle, optimizing costs.
HPC users, such as verification engineers, have an insatiable need for running hundreds of thousands to millions of jobs simultaneously. A small on-premises HPC cluster can slow time-to-market, while an oversized cluster can waste resources.
Benefits of High-Performance Computing Clusters in the Cloud
Innovations and advancements in cloud computing now make it possible to run HPC workloads more efficiently than ever. With unlimited scalability, the cloud can be scaled up to meet a jump in demand and scaled down when demand wanes.
Lower Infrastructure Costs
On-demand, pay-per-use HPC infrastructure dynamically provisions and de-provisions, reducing upfront capital expenditures. With HPC in the cloud, computing capacity can be burst for temporary peaks in workloads or distributed across multiple data centers to reach geographically dispersed users.
Optimized Workloads
You can design optimized cloud infrastructure for your workloads using flexible cloud infrastructure. Servers and storage can be allocated on demand or a schedule based on specific project phases, starting with a minimum of provisioned resources. Cloud service providers offer various procurement options for accommodating different types of workloads. One example is the on-demand pricing model, where organizations pay for computing capacity by the hour or minute for running short-term, spiky workloads.
Scalability
Cloud infrastructure allows you to scale up or down in minutes in the case of virtual servers or a few hours for bare metal servers. A genuinely scalable infrastructure is based on flexibility with application programming interfaces and automation. New nodes can be provisioned in a cloud data center, linked to the cluster manager, and used to run computational tasks. Cloud resources can be considered unlimited.
Shorter Technology Refresh Cycles
For HPC clusters, the refresh cycle should be 24 months or less. With HPC in the cloud, workloads can be shifted to new technology, and customers will always have the most current platform. Cloud service providers also offer access to analytics, artificial intelligence, and machine learning services that help drive innovation.
Reduced Time-to-Market
You can access unlimited computing and storage resources in the cloud for HPC workloads without the long procurement cycles, capital expenditures, and overhead associated with on-premises HPC systems. Chip designers can dramatically reduce time-to-market by rapidly provisioning and de-provisioning HPC clusters in minutes.
Cloud computing is becoming more reliable, secure, and powerful, which makes it possible for companies to optimize their chip design process. Chip makers can optimize license utilization, engineering productivity, and costs using elastic clusters in cloud-based HPC, allowing for efficient innovation in the next generation of chips.
Synopsys Cloud helps chip makers leverage HPC in the cloud by providing unlimited access to EDA software licenses on-demand. We have partnered with the top cloud providers to optimize infrastructure configurations, removing the guesswork so EDA can rapidly deploy in the cloud.
Synopsys, EDA, and the Cloud
Synopsys is the industry’s largest provider of electronic design automation (EDA) technology used in the design and verification of semiconductor devices, or chips. With Synopsys Cloud, we’re taking EDA to new heights, combining the availability of advanced compute and storage infrastructure with unlimited access to EDA software licenses on-demand so you can focus on what you do best – designing chips, faster. Delivering cloud-native EDA tools and pre-optimized hardware platforms, an extremely flexible business model, and a modern customer experience, Synopsys has reimagined the future of chip design on the cloud, without disrupting proven workflows.
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Synopsys technology drives innovations that change how people work and play using high-performance silicon chips. Let Synopsys power your innovation journey with cloud-based EDA tools. Sign up to try Synopsys Cloud for free!
About The Author
Sridhar Panchapakesan is the Senior Director, Cloud Engagements at Synopsys, responsible for enabling customers to successfully adopt cloud solutions for their EDA workflows. He drives cloud-centric initiatives, marketing, and collaboration efforts with foundry partners, cloud vendors and strategic customers at Synopsys. He has 25+ years’ experience in the EDA industry and is especially skilled in managing and driving business-critical engagements at top-tier customers. He has a MBA degree from the Haas School of Business, UC Berkeley and a MSEE from the University of Houston.
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