Find Out What Your q Code Could Do On GPU

Book a one-to-one session with a KX engineer. They read the q you run today, point to the joins, sorts, and aggregations that gain the most from NVIDIA GPUs, and tell you where to start.

What You Get From The Session

A ranked list of workloads to move first, directional speedup ranges for each, and a plain list of what to leave on CPU. The engineer names the lines of q involved, so your team can plan the work and size the business case.

Tested On Production Code

A tier-1 bank ran the assessment against CPU code it had already ported to GPU. It found the bottlenecks the bank had fixed and the changes the bank had made.

Estimates are directional. Measured results depend on data volumes, hardware, memory movement, and the proportion of GPU-supported operations.

Schedule a workload assessment

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How The One-To-One Session Works

Share a representative script

Send the q for the workload that runs longest, or point the engineer at the codebase. Data volumes and hardware details help but are optional.

Walk through the findings

The engineer reviews each bottleneck with you, shows the expected uplift, and names the lines of q to change.

Leave with a ranked plan

Priorities, directional ranges, and a list of what to keep on CPU, ready to take to your team and your business.

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