Data Discipline: Managing Engineering Data from AI-powered Design
The advancements in the fields of AI and ML, combined with the increased availability of robust
simulation, testing, and field data sets has made engineering data science a critical component of the
modern product development lifecycle, but in order to extract maximum value from these exciting tools,
companies need a plan to store, manage, and utilize their data efficiently. They need data discipline
Silicon Design in the Cloud: Mellanox Scales Up EDA Cluster Capacity With On-demand Hybrid Cloud
Mellanox Technologies is a leading supplier of end-to-end Ethernet and InfiniBand intelligent interconnect solutions for servers, storage, and hyper-converged infrastructure. Half the world’s top 500 fastest supercomputers employ Mellanox solutions including high-performance network and multicore processors, network adapters, switches, cables, software, and silicon.
Mellanox needed a robust cluster management and scheduling solution from a trusted partner, leveraging hybrid cloud. In assessing potential solutions, the Mellanox team analyzed performance, features, and costs, ultimately selecting Altair NavOps™ and Altair® Grid Engine®, proven solutions that offered the shortest implementation effort with leading price/performance metrics. Navops Launch seamlessly enabled Mellanox’s existing on-premises infrastructure and workflows to encompass the cloud.
Wharton School Scales Up HPC: High-performance Computing Upgrade Extends Research to the Cloud
Founded in 1881 as the world’s first collegiate business school, the Wharton School at the University of Pennsylvania is known for leadership and innovation in business education. Staying on top as a leader among world-class business schools necessitates an HPC infrastructure that can support a vast number of users.
Wharton needed to extend its HPC environment in a cost-effective way — without impacting its large roster of users, who need ready access to the latest tools, infrastructure, and technical expertise. Since Wharton was already using Altair® Grid Engine® as its go-to solution for HPC job management, the team looked to Altair NavOps™, software designed for organizations experiencing increasing volumes of high-priority workloads, where response speed and accuracy is critical.
Managing TCO in HPC Hybrid Cloud Environments
Enabled by improvements in security, new instance types, and fast interconnects, high-performance computing (HPC) users are increasingly shifting workloads to the cloud. With cloud usage increasing, however, managing and containing costs is a growing concern. As organizations become more reliant on cloud, they are also concerned with staying portable and flexible, and avoiding lock-in to a single cloud ecosystem or provider.
In this paper, we make a case for HPC hybrid cloud and explain how operators can manage total cost of ownership (TCO) more effectively. We present a simple TCO model that can help users estimate the cost of hybrid cloud deployments and describe various Altair solutions that can help organizations quickly and cost-effectively implement private and hybrid multi-cloud HPC environments. Using our TCO model, we illustrate how Altair cloud automation and spend management solutions such as Altair Control™ and Altair NavOps™ can boost productivity and reduce cloud-related expenses while delivering a compelling return on investment (ROI).
Altair NavOps for Cloud Automation and Spend Management
Altair NavOps™ is an automation and spend management platform for migrating compute-intensive HPC workloads to the cloud. It provides organizations with insights into spending against budgets and end-to-end visibility into HPC cloud resources and applications. Using NavOps, enterprises can easily deploy hybrid and dedicated cloud clusters running Altair® Grid Engine® or other workload managers.