5 HPC Optimization Techniques

For many organizations, decisions about whether to run HPC workloads in the cloud or in on-premises datacenters are less all-encompassing and more about leveraging both infrastructures strategically to optimize HPC workloads across hybrid environments. From multi-clouds to on-premises, dark, edge, and point of presence (PoP) datacenters, data comes from all directions and in all forms while HPC workloads run in every dimension of modern datacenter schemes. HPC has become multi-dimensional and must be managed as such.

While cloud has made access to HPC easier, increasing demand for it among new and distributed teams introduces allocation and cost challenges.

There can be cost and funding challenges in using HPC and cloud resources, and these must be balanced against other issues such as massive AI requirements, new security considerations and mitigation tactics, the increasing speed of analytics-based automation, software licensing cost and complexity, and more.

Fortunately, the dynamism of today’s multi-dimensional HPC landscape provides more optimization opportunities than ever before. Today’s most competitive HPC systems are tuning across new dimensions of the modern HPC infrastructure to achieve breakthrough results both in their own datacenters and in the cloud.

This white paper explores several of these new strategies and tools for optimizing HPC workloads across all dimensions to achieve breakthrough results in Microsoft Azure.

 

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