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Developing groundbreaking chips and devices is a difficult task. When you are constantly evolving to stay at the forefront of technology, you might find yourself dedicating a significant amount of time and computational power to creating unique designs and efficient architecture. High-performance computing can help you expedite this process.

Accomplishing groundbreaking work with outdated technology and tools is difficult. The creation of innovative designs relies upon the available tools. During the research and development process, you will often need to expand the current field, integrate new data, and develop the resolution of your models. Ensuring you have the computational capacity, performance, and tools for the next generation of design is essential. 


How Can High-Performance Computing Combat Current Technology Challenges?

As chip designers, we know there will always be unforeseen challenges and roadblocks during the design process. Computational barriers can include time restrictions–where on-premises processing for local systems is too slow or unfeasible–or limitations on computational capacity–where you can only simulate a few models at a time. 

Another common challenge is the management of computer systems. Many engineers don’t want to purchase and manage local computer systems, as they are more concerned with focusing on development.

Facing these aforementioned issues while developing a ground-breaking chip only serves to make the process even more difficult in ways that scientists and engineers aren’t looking for. You cannot rely on legacy services and applications if you truly want to push the barrier of what’s possible now. 

To combat these challenges, high-performance computing (HPC) utilizes a variety of interdisciplinary fields, generating immense computing power. These fields include network capabilities, parallel programming, digital electronics, computer architecture, programming languages, and system software.


How Can High-Performance Computing Drive Innovation?

The more data an engineer can access, the more equipped they will be in making groundbreaking discoveries and inventions, thus changing the quality of life for billions of people. High-performance computing is the foundational tool for working with large amounts of data to create simulations used for developing advantageous technologies. 

As the amount of data we produce and collect multiplies, the tools for managing this data become critical. To stay at the forefront, enterprises need fast, reliable infrastructure. HPC powers developments in the fields of geology, archeology, materials, graphics, genomics, economics, music, brain imaging, and chip design.

 

High-Performance Computing Structure

High-performance computing consists of three main components: power, network, and storage. Computer servers are networked together to form what's known as a cluster. HPC clusters consist of thousands of computational servers or nodes. Software algorithms can then run on these servers in a parallel fashion. The servers communicate with each other to seamlessly operate and accomplish tasks.

 

High-Performance Use Cases and Examples

Utilizing high-performance computing allows for an incredible amount of innovation, from finding sources of renewable energy to tracking storms and creating new materials. Through the use of Artificial Intelligence (AI) and Machine Learning (ML), HPC has taught self-driving vehicles and improved cancer screening.

HPC can also help you design new products, simulate test scenarios, and develop cures for diseases like diabetes by accelerating accurate patient diagnosis. Thanks to HPC, we can now make models of phenomena previously inaccessible, simulating galaxies, climate change, and fusion energy.

 

High-Performance Computing Features

HPC can accelerate innovation through ML and analytics. With on-demand access to immense computational capacity, businesses no longer need to wait. Instead, they save time by focusing on the problems they need to tackle without worrying about infrastructure or cost limitations.

Similarly, HPC can scale simulations with a variety of parameters and reduce wall-clock time, resulting in faster and more accurate results. HPC can process complex workloads and analyze massive amounts of data much more quickly than an on-premises computer. HPC’s capabilities are endless when it comes to solving complex math and science-based problems across any industry. 

In the past, HPC has involved on-premises infrastructure using supercomputers. With the recent growth of cloud computing, HPC is now more accessible than ever. It can reach the hands of researchers and engineers who lack the IT infrastructure to house on-premises infrastructure. With HPC in the cloud, organizations pay for only what they use without the risk of their infrastructure becoming obsolete.


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.

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