GPU-Accelerated Query Execution in KDB-X

See how KDB-X GPU Acceleration uses GPU-resident data structures and CUDA-accelerated execution to run time-series queries across NVIDIA GPUs. Explore the module’s architecture, q-level API, execution model, and performance across joins, sorts, windowed aggregations, filters, and scans.

Understand Where GPU Acceleration Delivers The Most Value

Large time-series workloads often combine operations with very different execution characteristics. Joins, sorts, and windowed aggregations can map efficiently to thousands of parallel GPU threads. Other operations may remain faster on a CPU, particularly when they depend on index shortcuts or require frequent transfers between host and device memory.

This whitepaper examines both sides of that performance equation. It explains how the KDB-X GPU module works, where it delivers substantial speedups today, and why workload shape and data residency determine the result.

What The Whitepaper Covers

How the GPU module integrates CUDA with KDB-X through the C API
How to move data between CPU and GPU memory
How KDB-X executes qSQL-style queries against GPU-resident tables
The GPU execution model behind joins, sorts, reductions, sliding
windows, and filters
Benchmark results from the KX NYSE TAQ query suite
Where GPU execution outperforms the CPU today
How transfer costs and current indexing limitations affect performance
How performance scales from one GPU to four GPUs

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