Dave Link is CEO and Cofounder of ScienceLogic. Read Dave Link's full executive profile here.
You’re not alone in the challenge of achieving ultra-high expectations for innovation supported by the new compute architectures and applications leveraging graphics processing units for enterprise artificial intelligence workloads. GPUs have been a staple of enterprise IT since the '90s, used in synthetic vision, robotics and automation.
But this boom in usage comes with core business challenges around cost, including the premium pricing for GPU hardware components and the tremendous power consumption needed to run them. This, in turn, obligates CIOs and CFOs alike to take great care in shaping their GPU investments—a continual balancing act to weigh processing power against cost.
GPU monitoring should ideally be accessible to analyst-level users via an abstraction layer that puts GPU processes in the context of the business activities involved. Rather than simply mapping the performance of compute storage and networking devices, this “service-centric” approach to monitoring renders GPU operations in the context of the services they perform.
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