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The Promise of GPUs for Big Data Apps

Advancements in GPU technology, artificial intelligence (AI), and high-performance computing (HPC) have changed the way a growing number of industries get value from their data and drive big data performance to new heights. Cloud GPUs are quickly becoming mainstream. Once reserved for computation-heavy workloads like AI and Machine Learning (ML), now it’s common to see GPUs being used for business applications. Examples of use cases for GPUs in big data include:

  • Real-time risk analytics in finance
  • Recommendation engines for next best offers in retail
  • Autonomous vehicles in transportation
  • Chatbots for better customer service in healthcare
  • Training AI/ML workloads in technology

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Learn More: Visibility for Cloud GPUs with Pepperdata Blog Post

Graphical Processing Units (GPUs) come with risks and rewards. On the one hand, they offer incredible processing power—often orders of magnitude higher than a typical CPU. However, this speed introduces complexity and makes it more challenging to monitor applications and control costs. Here at Pepperdata, our product suite now includes the ability to monitor cloud GPU instances running computation-heavy big data applications as well as deep learning and artificial intelligence (AI).

In this post, we’ll dive a little deeper into this new capability and explore why we believe it will be such a bonus for our customers.

Scalability for Data-Intensive Workloads

Big data companies looking for scalability, speed, lower costs, as well as the energy and rack-space footprint of big data systems are turning their attention and budgets to GPUs. 

Data-intensive workloads require a fast, secure, and cost-effective cloud infrastructure that incorporates the power of GPU computing. From analytics and graphics enhancement to energy exploration and ML, the added power of GPUs is undeniable:

  • GPUs allow data scientists to experiment and iterate more due to faster completion times.
  • ML modeling times are greatly reduced, and running large workloads won’t affect overall performance for smaller applications.
  • GPUs are easily scalable with the cloud, and they can minimize the cost of computational-heavy tasks.
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Register: Eliminate Waste and Lower Cloud Costs for GPU-Accelerated Big Data Applications

Cloud GPUs are becoming mainstream for big data applications like Spark on Kubernetes. Although the massively parallel computing power of GPUs significantly speeds up data-intensive ML and AI workloads, costs can spiral out of control without visibility. Join Pepperdata Field Engineer Alex Pierce for a webinar on gaining visibility into cloud GPU resource utilization at the application level and improving the performance of your GPU-accelerated big data applications.

Gain Visibility, Improve Performance, and Manage Costs with GPU Resource Utilization

The massive parallel computing power of GPUs significantly speeds up data-intensive applications and AI and ML workloads. However, costs can quickly spiral out of control. Observability, automation, and the ability to eliminate waste with GPU monitoring solutions can help you overcome these challenges.

Pepperdata big data performance management solutions can help you gain visibility into cloud GPU instances at both the resource utilization and application level. This unique level of observability enables you to:

  • Improve the performance of your GPU-accelerated big data application GPU usage and waste.
  • Fine-tune GPU usage using real-time metrics and end-user recommendations.
  • Manage costs at a granular level by attributing usage and spend to specific end users.
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Take the Pepperdata Interactive Demo to See What Big Data Success Looks Like

Sign up to see how automatic optimization and full-stack observability can improve performance across your entire big data stack. No data or installation is required. Simply log in to start playing in the Pepperdata sandbox.