Altair > Case Studies > Powering Drug Discovery: High-Performance Computing at Johnson & Johnson

Powering Drug Discovery: High-Performance Computing at Johnson & Johnson

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Technology Category
  • Application Infrastructure & Middleware - Data Exchange & Integration
  • Infrastructure as a Service (IaaS) - Cloud Computing
Applicable Industries
  • Consumer Goods
  • Pharmaceuticals
Applicable Functions
  • Product Research & Development
  • Quality Assurance
Use Cases
  • Construction Management
  • Infrastructure Inspection
Services
  • Cloud Planning, Design & Implementation Services
  • System Integration
About The Customer
Johnson & Johnson is the world's largest healthcare business and a top-50 Fortune 500 company. It produces pharmaceuticals, medical devices, and consumer goods that benefit a billion people worldwide. Johnson & Johnson owns Janssen Pharmaceuticals, a company that uses science to combat diseases. Janssen created the 1-dose COVID-19 vaccine that's preventing infection and saving lives in over 100 countries around the world. This research-driven organization relies on high-performance computing (HPC) to power the discovery and production of effective, broadly available pharmaceuticals.
The Challenge
Johnson & Johnson, a global healthcare giant, required on-demand compute capacity at scale for its research, particularly for Janssen Pharmaceuticals, which was working on the COVID-19 vaccine. The company needed to easily scale down this capacity when not in use, a feat only achievable with cloud infrastructure. Janssen was operating over 10 production High-Performance Computing (HPC) clusters on Amazon Web Services (AWS), used by scientists and developers worldwide. However, they were seeking an off-the-shelf solution to replace the open-source Grid Engine and a cloud management tool that no longer supported their preferred vendor. The challenges included accommodating existing infrastructure and systems that had evolved over a decade, managing a complex networking setup, and integrating into configuration and change management systems. Furthermore, each cluster was configured differently, adding to the complexity.
The Solution
To address these challenges, Janssen's workload management software was upgraded to Altair® Grid Engine® and Altair® NavOps® was deployed to manage the company's complex cloud deployments. This solution integrated seamlessly with AWS cloud services. The package met Janssen's requirements for automated creation and scaling of clusters, compliance with internal security policies and networking, compatibility with both commercial and homegrown applications, and GXP compliance. It included cgroups for resource allocation, Docker integration for containerized workloads, and REST API. The result was a simplified, automated, and extensible Janssen HPC infrastructure. With the Altair orchestration layer, Johnson & Johnson can scale on demand in a complex multi-tenant environment. The team can use current configuration components to quickly and easily create additional clusters as needed. Altair’s infrastructure-as-code solution included complete UI/CLI/API configurability, flexible configuration and integration options, and the ability to template necessary components and easily edit configuration differences.
Operational Impact
  • The implementation of Altair's solution has allowed Johnson & Johnson to continue innovating. With increased scalability and improved agility in operations, deployment, and management of HPC infrastructure, the team can perform science on demand. This flexibility enables them to tackle events like the COVID-19 pandemic at an enterprise level. The solution has also simplified the management of their complex cloud deployments, making it easier to create and scale clusters as needed. This has resulted in a more efficient and effective use of resources, enabling the company to focus more on its core mission of developing life-saving pharmaceuticals.
Quantitative Benefit
  • Scalability limits from Janssen’s previous configuration were removed with the Altair solution
  • Their clusters are now running at 3X the size of the previous implementation

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