Designing a GPU-accelerated SQL engine is not trivial, but it is required to achieve the best performance. As data grows in size and complexity, the conventional way of processing it becomes the bottleneck of the pipelines that businesses depend on to make fast, informed decisions.
The GPU Query Engine (GQE) is a reference architecture for that problem: a GPU-accelerated SQL engine designed, developed and benchmarked to push complex analytics query processing to speed-of-light. Built on top of libcudf and NVCOMP, it is a blueprint for database developers on how to design a best-in-class GPU-accelerated SQL engine for quantitative research — with minimal to zero CUDA development of their own.
1. Use cases
GQE supports two storage strategies from the same engine. Which path a query takes is a single decision at the source; both converge on the same GPU-accelerated execution core.
In-CPU-memory query processing
Cache data tables in CPU memory for faster processing. Data is ingested into host memory and stored in GQE's custom data layout, and query execution reads directly from host memory. This is faster than reading from disk, and the custom layout allows partition pruning on compressed data.
Parquet-on-disk query processing
Query directly from Parquet files stored on disk, leveraging disk capacity to process very large datasets. This path is slower than the in-memory case and does not currently support partition pruning — the trade-off is capacity over latency.
2. Key benefits
- Reduced latency. GPU acceleration lowers the execution time of complex analytics queries.
- Reduced cost and power consumption. Lower latency lowers overall cost and power draw.
- Flexible evaluation and integration. Use Substrait to test your own plans, or a different SQL front-end, to observe the benefits for your specific workload.
- Ease of development. Built on libcudf, GQE shows how to build a GPU-accelerated database with minimal to zero CUDA development.
- Stay at the frontier. GQE is backed by database researchers and CUDA experts actively integrating new ideas.
3. Key features
- Data pipeline acceleration. Complex analytics drives the latency and cost of many data pipelines; GQE delivers significant speedups through GPU acceleration.
- Multi-storage. Query directly from Parquet on disk, or from CPU and/or GPU memory, enabling flexible caching scenarios.
- Multi-GPU support. Scale out to improve performance further.
- Native hardware decompression. Leverage Blackwell's hardware decompression engine to lower I/O requirements while offloading decompression to dedicated hardware.
- Substrait compatible. Bring your own plan or front-end framework.
4. Minimum system requirements
| Requirement | Detail |
|---|---|
| Hardware (HW DE) | B100 / B200 / B300 for hardware decompression |
| Memory | Sized to input data (≈ 8 GB recommended per 100 GB dataset) |
| Storage | Varies with input data size |
| OS | Any Docker Engine–capable Linux distribution (e.g. Ubuntu 24.04) |
| Dependencies | Docker Engine — all others installed via the Dockerfile |
5. Software used
NVIDIA technology: cuCollections, cuDF, NVCOMP, NVSHMEM.
Third-party software: Arrow Flight SQL, Boost, DataFusion, Substrait.
6. Ethical considerations
Trustworthy AI is a shared responsibility. When downloaded or used in accordance with the relevant terms of service, developers should work with their supporting model team to ensure the models meet the requirements for the relevant industry and use case, and to address unforeseen product misuse.