No. 92
2026-10-19
the AI pulse — brewed, not raw
 VIDEO
Turkish subtitles
  1. So why is GPU changing analytical workloads?
  2. AI has relied on GPU for years to accelerate complex workloads.
  3. Now that same technology is beginning to change analytics.
  4. As teams analyze more data and support more AI workloads, traditional CPU-based analytics are becoming more and more expensive to scale.
  5. GPU acceleration introduces a new approach using both CPUs and GPUs to work better together.
  6. In this video, we'll explore why the cost and performance of analytical workloads are changing, how GPUs fit into modern analytics, and why this hints at a broader shift in the future of data processing.
  7. Modern analytical workloads are fundamentally changing.
  8. Teams are processing billions of records, supporting thousands of users and preparing data for AI applications all on the same platform.
  9. But everyone working with large sets of data experience that as the size of your data grows, The query execution speed will slow down.
  10. Therefore, the industry is beginning to rethink how analytical workloads are executed.
  11. GPU acceleration offers a new approach by improving both performance and infrastructure efficiency without changing how users write SQL or analyze data.
  12. GPU accelerated analytics doesn't replace the CPU, and depending on the size and shape of the data, CPU might still be faster, as you can see right here in this graph.
  13. Or using an approach called heterogeneous computing, CPUs and GPUs can work together.
  14. There would be a host working on CPU.
  15. Which processes the incoming query.
  16. And returns the outcome in result.
  17. Depending on the nature of the workload, it might use CPU.
  18. Or a GPU to execute the query.
  19. The CPU continues coordinating query execution and managing your overall workflow, while GPU accelerate the highly parallel portions of analytical workloads that benefit from this parallel computation.
  20. The key question is, why are GPUs suddenly becoming relevant for analytics?
  21. The answer comes down to parallelism.
  22. Many analytic workloads perform the same operation across enormous amounts of data.
  23. And it turns out to be exactly the kind of problem GPUs were designed to solve.
  24. Let's look at three reasons why.
  25. The first reason is that GPU and CPU have different strengths by design.
  26. CPUs are incredibly powerful and are relying on a small number of cores.
  27. They execute complex instructions, manage applications, and coordinate workloads extremely well.
  28. GPUs are different.
  29. They contain thousands of smaller processing units that are great at handling things in parallel.
  30. Rather than replacing CPUs, GPUs complement them, as you can see in this overview right here.
  31. Overall, GPU will give you a better compute density and therefore better performance per unit of compute costs, depending on the nature of your workloads, of course.
  32. The second reason is that analytical workloads are highly parallel.
  33. Many SQL operations repeat the same calculations across millions or billions of rows.
  34. You might be filtering, grouping, or aggregating.
  35. Assume you are selecting data, you're selecting it from a number of tables.
  36. And you might be having some classes such as where to filter.
  37. In between, you might have additional things like ons or joins.
  38. And at the end, you may be grouping.
  39. Or ordering your data as well.
  40. Depending on your SQL engine, these operations can be executed at the same time instead of one by one.
  41. And this would make them well suited for GPU acceleration.
  42. The third reason is better performance changes the economics.
  43. The real benefit isn't just making your query execute faster, it's also making sure your cost will stay low.
  44. Because the longer your query takes to execute, the higher the cost will be.
  45. It's changing compute efficiency and execution characteristics when queries complete more quickly.
  46. Because your infrastructure runs for less time, more users can share the same compute resources, and your compute cost per query execution decreases.
  47. Because your insights are arriving sooner, performance and cost improve together.
  48. This is an architectural shift in compute execution models.
  49. GPU acceleration isn't about replacing existing analytic platforms.
  50. It's about evolving how analytics engines execute work.
  51. Workloads will continue to grow but compute capacity and cost constraints won't grow at the same pace.
  52. That's why the future of analytics won't rely on CPUs alone.
  53. Instead, we will have CPUs and GPUs working together to allow teams to deliver faster.
Subtitles generated on my computer; they follow the video — click a line to jump there.

GPUs Accelerate Data and Analytics with AI

As data volumes and artificial intelligence workloads increase, driving up the cost of traditional CPU-based analytics, GPU acceleration offers a new approach to modern data processing by enhancing both performance and infrastructure efficiency.

This video is published on youtube.com; the player here is YouTube’s. The Turkish summary and subtitles are prepared for AiPulse.
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