Observe and Manage Infrastructure and Application-Level GPU Usage

Pepperdata, the leader in big data performance management, announced that the Pepperdata product portfolio now includes the ability to monitor Graphics Processing Units (GPUs) running big data applications like Spark on Kubernetes.

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Workloads that harness tremendous amounts of data, such as machine learning (ML) and artificial intelligence (AI) applications, require GPUs, which were originally designed to accelerate graphics rendering. That extra processing power comes with a high price tag, and it requires near constant monitoring for resource waste to get the best performance at the lowest possible cost.

Pepperdata now monitors GPU performance, providing the visibility needed for Spark applications running on Kubernetes and utilizing the processing power of GPUs. With this new visibility, companies can improve the performance of their Spark apps running on those GPUs and manage costs at a granular level.

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Unlike traditional infrastructure monitoring, which is limited to the platform, the Pepperdata solution provides visibility into GPU resource utilization at the application level. Pepperdata also provides instant recommendations for optimization. Features include:

• Visibility into GPU memory usage and waste
• Fine-tuning of GPU usage through end-user recommendations
• Ability to attribute usage and cost to specific end-users

“Spark on Kubernetes is quickly becoming a dominant part of the compute infrastructure as data-intensive ML and AI applications proliferate,” said Ash Munshi, CEO, Pepperdata. “GPUs can handle these workloads, but they are expensive to buy and are power-intensive. Until now, there hasn’t been a way to view and manage the infrastructure and applications, which can lead to unnecessary waste and overspending for big data workloads. With Pepperdata, organizations can properly size their GPU hardware investments and have the confidence that they are utilizing them well.”

There are products on the market for monitoring GPUs, but they typically lack long-term storage, the ability to scale, and often do not correlate infrastructure metrics to applications. Pepperdata solves these problems with insight for data center operators, data scientists, and ML/AI developers. They can now understand who is using what resources, optimize to eliminate waste so jobs can be tuned and prioritized, and make sure costs are assigned appropriately to the right users or groups across the enterprise.

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