CHICAGO--(BUSINESS WIRE)--Univa®, a leading innovator in enterprise-grade workload management and optimization solutions for on-premise and hybrid cloud high-performance computing (HPC), today announced that eSilicon selected Univa’s Grid Engine® enterprise-class workload scheduling and optimization solution to help manage its complex FinFET ASIC and IP designs and scale its environment, as well as better manage its ever-increasing workload and demand for capacity.
eSilicon is an application-specific integrated circuit (ASIC) developer that designs and produces high-end chips and semiconductor IP. The company delivers complex integrated circuits that help power hardware in industries such as 5G infrastructure, networking and artificial intelligence. To handle this level of complexity and scale, they looked to Univa’s Grid Engine platform to create a more efficient environment where all resources are optimized. Working with Univa helped eSilicon gain maximum utilization of their compute infrastructure and licenses, which can translate to more analysis and verification that can be run and therefore create a higher chance to achieve first-time-right silicon.
“Given our intricate electronic design needs, we were looking for a workload scheduling and optimization solution that could help us maximize our throughput and performance of our applications,” said Naidu Annamaneni, chief information officer and vice president of Global IT at eSilicon. “We ultimately chose Univa Grid Engine, since it was able to better help our team optimize our shared resources and provide enterprise-grade dependability to help us improve ROI and deliver better results. As we look to extend our workloads to the cloud, we look forward to working with Univa to start the cloud migration process along with Google Cloud’s service-on-demand model.”
Univa Grid Engine helped eSilicon manage its complex ASIC design workloads automatically, maximize shared resources and accelerate the execution of any container, application or service. The solution can be deployed in any technology environment: on-premise, hybrid cloud or cloud-native HPC. With Univa Grid Engine, workloads are efficiently shared across machines in a datacenter to optimize the use of the computing infrastructure. Scheduling policies can be applied to all work submitted to the cluster, ensuring high-priority jobs are completed on time, while simultaneously maintaining maximum utilization of all cluster machines. Along with Navops Launch, Grid Engine can dynamically schedule workload to a hybrid cloud.
“In the highly competitive business of electronic design, getting to market quickly is paramount,” said Gary Tyreman, president and CEO of Univa. “Engineers are under extreme pressure to design, test and deliver accurate results in tight timeframes. Univa’s solutions are designed to help solve these challenges and help companies like eSilicon deliver products and results faster, more efficiently and with lower overall costs. Our Univa Grid Engine platform was designed to support the unique requirements of an EDA environment, from providing a framework to optimize the utilization of computing infrastructure to the details of interacting with the EDA licensing. Additionally, the Univa team has strong backgrounds in the technology that powers many EDA environments that, when combined with our unparalleled ability to optimize workloads along with our management capabilities for virtual and cloud environments, is unmatched.”
About Univa Corporation
Univa is the leading innovator of workload management solutions that optimize throughput and performance of applications, containers, and services. Univa manages workloads automatically by maximizing shared resources and enabling enterprises to scale compute resources across on-premise, hybrid and cloud infrastructures. Univa's solutions help hundreds of companies to manage thousands of applications and run billions of tasks every day to obtain actionable insights and achieve faster time-to-results. Univa is headquartered in Chicago, with offices in Canada and Germany.