Cloud Workloads: Can the Cloud Support EDA? | Synopsys Cloud
Table of Contents

Introducing Synopsys Cloud

Cloud native EDA tools and pre-optimized hardware platforms. Experience unlimited EDA licenses with true pay-per-use on an hourly or per-minute basis.

With so many cloud computing services to choose from, organizations have the option to rely on different platforms for different workloads. When IT teams match their resources to cloud workloads, they can increase application performance, reduce consumption costs, and avoid vendor lock-in.

Cloud workloads are applications, services, or capabilities that consume cloud resources. When you view and edit a photo on a laptop, a computer must process software instructions. When you search something online, a data center processes the workload, resulting in a set of links. These operations are both examples of cloud workloads. 

The most popular enterprise cloud workloads include: 

  • databases
  • analytics
  • web/content hosting

Classifying Cloud Workloads

By classifying workloads based on their resource requirements and usage patterns, you can decide whether they suit the cloud.

 

Resource Requirements

Cloud workloads fit into categories based on resource requirements:

  • A general computing workload runs on the cloud's default configuration without requiring specific computation. Web apps, servers, and data storage containers all fall into this category.
  • A CPU-intensive workload contains many concurrent users and requires a high level of computing power. Examples of these cloud workloads include multiplayer online games and deep learning applications that perform processor-intensive operations, such as video encoding, big data analytics, and 3D modeling.
  • A memory-intensive workload requires a large amount of processing power and memory. This category includes distributed databases, caching, and real-time streaming data.
  • GPU-accelerated workloads, such as speech recognition, self-driving cars, navigation systems, and computational fluid dynamics, have exceptionally high processing requirements.
  • Storage-optimized workloads include in-memory databases, highly scalable NoSQL databases, and data warehouses.

 

Usage Patterns

We can also classify cloud workloads by usage patterns:

  • A static workload has fairly known resource requirements, demand, and uptime. Static workloads can include core enterprise services like customer relationship management, enterprise resource planning, or email.
  • For periodic workloads, traffic spikes at certain times of the day, week, month, or year. A bill payment tool or a tax and accounting software are both periodic workloads. 
  • With unpredictable workloads, traffic grows exponentially with little predictability. Cloud auto-scaling can handle such spikes by dynamically adding instances when needed.

EDA Workloads in the Cloud

Due to the CPU-, memory-, and storage-intensive nature and unpredictability of EDA workloads, chip companies should seriously consider using the public cloud. This model offers increased flexibility and scalability

It is essential to make the transition to the cloud thoughtfully. The key to unlocking the value of the cloud is to take a strategic approach. You should review each current deployment to fully understand compute and memory footprints and cost basis and dependencies. You also need to identify the workloads that will benefit from the public cloud. 

You should not rely on standard cloud instances for EDA workloads simply because they are easily accessible. To achieve optimal performance, the workload must match the appropriate cloud instance. One of the key metrics to use when choosing the instance is the memory per core ratio needed for your workload. Typically, front-end simulation or library characterization tools require less memory to core while backend applications, such as static timing analysis or physical verification, require larger memory to core instances. 

In addition, the cost of infrastructure and EDA licenses to run the workloads needs to be considered.

Synopsys Cloud has developed the FlexEDA model to address the unique requirements of EDA workloads. The solution offers unlimited EDA licenses on a pay-per-use hourly or per-minute basis. 

You can design, schedule, and run a specific EDA job based on its place in the design cycle. You can also tailor jobs to your power, performance, and area goals. Synopsys Cloud will automatically scale the EDA software based on the elastic cloud-scale infrastructure you enable for each job.

Synopsys Cloud leverages the following elements to ensure you can meet EDA workload requirements in the cloud:

  • Metering technology to enable unlimited on-demand EDA tool availability without changing the EDA software code.
  • Pre-optimized compute for each type of EDA workload. Synopsys has tested and benchmarked hundreds of compute virtual machines in the public cloud to provide a subset optimized for specific EDA workloads.
  • Integration with the cloud stack to leverage the cloud’s auto-scaling, fault-tolerance, and metering capabilities.
  • Browser-based access for EDA workloads, which require advanced scheduling, virtual desktop, and project and user management capabilities native to the platform.
  • Security based on the cloud provider’s shared responsibility model to ensure customer data is secured at all levels.

With the Synopsys Cloud FlexEDA model, your design needs dictate how you use chip design and verification tools.


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.

 

Take a Test Drive!

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.

Continue Reading