Pepperdata, a provider of big data performance management, is adding the ability to monitor Graphics Processing Units (GPUs) running big data applications like Spark on Kubernetes into the Pepperdata product portfolio.
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, according to the vendor.
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.”
Pepperdata offers insight for data center operators, data scientists, and ML/AI developers with this update. 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.
For more information about this news, visit www.pepperdata.com.