High-Performance Architecture (HPA)
- (Supercomputer, Lawrence Livermore National Laboratory)
- Overview
Artificial intelligence (AI) solutions require purpose-built architecture to enable rapid model development, training, tuning and real-time intelligent interaction.
High Performance Architecture (HPA) is critical at every stage of the AI workflow, from model development to deployment. Without the right foundation, companies will struggle to realize the full value of their investments in AI applications.
HPA strategically integrates high-performance computing workflows, AI/ML development workflows, and core IT infrastructure elements into a single architectural framework designed to meet the intense data requirements of advanced AI solutions.
Key components of HPA include:
- High-Performance Computing (HPC): Training and running modern AI engines requires the right amount of combined CPU and GPU processing power.
- High-performance storage: Training AI/ML models requires the ability to reliably store, clean, and scan large amounts of data.
- High-performance networks: AI/ML applications require extremely high-bandwidth and low-latency network connections.
- Automation, orchestration, software and applications: HPA requires the right mix of data science tools and infrastructure optimization.
- Policy and governance: Data must be protected and easily accessible to systems and users.
- Talent and skills: Building AI models and maintaining HPA infrastructure requires the right mix of experts.
[More to come ...]
