HPC User Budget Map Survey, Special Report: Machine Learning’s Impact on HPC Environments



Intersect360 Research surveyed the High Performance Computing (HPC) user community to complete its tenth Site Budget Allocation Map, a look at how
HPC sites divide and spend their budgets. We surveyed users on their spending in seven top-level categories: hardware, software, facilities, staffing,
services, cloud computing, and other. Each category was further divided into constituent subcategories, resulting in 27 unique items included in
the analysis. Additionally, the respondents were asked about their future budget expectations.

For this iteration of the annual Budget Map survey, Intersect360 Research incorporated questions to investigate the use of machine learning and its
interrelationship with HPC budgets, systems, and personnel. This report examines the use of machine learning, the impact on the HPC budget, the
systems used, and the personnel to manage the systems.

Our Budget Map survey series includes the following reports:

  • HPC Site Budget Allocation Map: Budget Expectations
  • HPC Site Budget Allocation Map: Budget Distribution
  • HPC Site Budget Allocation Map: Machine Learning’s Impact on HPC Environments

For this survey, machine learningis defined as an inclusive term. The survey questions focused on the current trends in deep neural networks
(DNNs), including specific use cases in deep learning. More generally, machine learning is the current manifestation of artificial intelligence
(AI). For the purpose of this survey and report, all are referred to as machine learning.

Almost two-thirds (61%) of all survey respondents reported they were running machine learning programs currently, as part of or in addition to their
HPC environments. Another 10% planned to implement machine learning programs in the next year. Most respondents viewed machine learning workloads
as a component of their HPC budget and shared personnel across HPC and machine learning. The machine learning trend is a key factor in the near-term
growth of HPC budgets, and it has already had an effect on HPC installations.

Despite the prevalence of machine learning, Intersect360 Research is cautious in describing the size of the machine learning “market.” At present,
investment in machine learning is a subset of the HPC and Hyperscale markets. For further reading, refer to the following additional reports from
Intersect360 Research:

  • Worldwide AI and Machine Learning Training Market Model: 2018 Spending and Future Outlook
  • Worldwide High Performance Computing 2018 Total Market Model and 2019–2023 Forecast: Products and Services
  • Worldwide Hyperscale Market Model: 2018 Revenue and Future Outlook


  • HPC system elements
  • Systems, clusters
  • Processor elements
  • Accelerators
  • Software elements
  • Application software
  • In-house developed applications
  • Cloud computing, grid computing, utility computing
  • Public cloud technologies
  • Other Technologies
  • Artificial Intelligence/Machine Learning/Deep Learning