 come with risks and rewards. On the one hand,...)
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.
The Rise of GPUs
As every big data operator knows, enterprises that rely on big data computing and analytics are including GPUs in their architecture. Why? Because these units deliver superior processing power, vast memory bandwidth, and better efficiency compared to their CPU counterparts for heavy computational applications. The big limiting factor that engineers hit is compute. With GPUs, the ceiling is set much higher.
GPUs can run tasks that need multiple parallel processes 50 to 100 times faster than CPUs, including big data analysis, AI, and machine learning (ML). GPUs boast thousands of computational cores and an application throughput that is 10 to 100 times better than what CPUs can deliver.
GPUs are the first choice for big data professionals that process large volumes of big data. GPU instances integrate with other instances without friction and enable users to explore both the volume and velocity of data.
In short, GPUs are here to stay, and we can expect to see more and more on-prem and cloud GPU instances running big data applications in the coming years.
Optimizing Cloud GPU Big Data Applications
GPUs are more complex and more demanding than CPUs. They use a lot of power, and they generate an awful lot of heat.
All of this adds up to GPUs being a lot more expensive. In a CPU-versus-GPU performance experiment designed to simulate large-scale ETL from a retail company, the users conducted a performance run on CPU cluster and a GPU cluster with 3TB TPC-DS data stored on AWS S3. The experimenters found that the CPU cluster costs $3.91 per hour and the GPU cluster fetches $6.24 per hour, a big 62% difference. Anecdotally, we have heard of much bigger differences.
This means that with GPUs and cloud GPU big data applications, it is more important than ever to get optimization right. If you aren’t on top of power output, heat, and application-level performance, you are almost guaranteed to overspend.
New Pepperdata Powers
Computation-heavy applications that harness tremendous amounts of data, such as ML and AI applications, demand GPUs.
Our latest GPU visibility extends the powers of the broader Pepperdata portfolio to cloud GPU big data applications. Crucially, we help operators go beyond the platform level and observe and optimize at the application level.
This new Pepperdata feature allows enterprises to dive deep into their applications that run on GPU accelerators for close monitoring of usage and wastage. This application-level visibility enables our solution to gather and analyze performance data from GPUs, tasks, clusters, and more, to generate end-user recommendations for performance optimization.
The ability to monitor and view GPU usage and wastage helps users identify which applications and users are wasting GPU resources. This not only allows our clients to reduce waste but also attributes usage and cost to end users. This means fast and accurate chargeback for business units.
We believe this makes us the leading visibility and optimization solution for big data applications and workloads that use GPUs. As part of broader IT transformation trends, both AI and ML have revolutionized how modern business operates. Our GPU visibility and monitoring give organizations the confidence to adopt and integrate AI and ML into their big data stacks.
Until now, there hasn’t been a way to view and manage heavy, expensive GPU infrastructure and applications. This has meant waste and overspending. Now, we are empowering organizations to properly size their GPU hardware investments and have the confidence that they are utilizing them well.
Learn more about the importance of observability for the big data stack.