NVIDIA's Sirius Breaks ClickBench Records with GPU-Acceleration

NVIDIA, in collaboration with the University of Wisconsin-Madison, has introduced Sirius, a GPU-accelerated SQL engine that has set a new performance benchmark on ClickBench. The open-source Sirius engine integrates with DuckDB, a database renowned for its simplicity and speed, to enable high-performance analytics by leveraging GPU technology.

Partnership and Innovation

The adoption of DuckDB has surged among companies like DeepSeek, Microsoft, and Databricks, thanks to its versatile and efficient nature. Recognizing the potential for enhanced performance, NVIDIA and the University of Wisconsin-Madison developed Sirius to provide GPU-accelerated analytics without the need to reconstruct database systems from the ground up. By utilizing NVIDIA's CUDA-X libraries, Sirius accelerates query execution, offering significant improvements in performance and throughput compared to traditional CPU-based systems.

Architectural Highlights

Sirius operates as a GPU-native execution backend for DuckDB, requiring no modifications to DuckDB’s codebase. It leverages NVIDIA's high-performance libraries, including cuDF and RAPIDS Memory Manager, to build its execution engine. This integration allows Sirius to reuse DuckDB's advanced subsystems while enhancing them with GPU acceleration, facilitating efficient execution of SQL operations.

Record-Setting Performance

On the ClickBench analytics benchmark, Sirius demonstrated record-breaking performance. Running on NVIDIA's GH200 Grace Hopper Superchip, Sirius outperformed other top systems, achieving superior cost-efficiency and speed. In tests, Sirius delivered at least 7.2 times higher cost-efficiency than CPU-only systems, underscoring its capability to handle complex queries with ease.

Future Developments

Looking forward, NVIDIA and its partners aim to advance GPU data processing capabilities. Efforts will focus on enhancing GPU memory management, developing GPU-native file readers, and evolving the execution model into a scalable, multi-node architecture. These advancements aim to streamline data processing and extend the scalability of Sirius to handle petabyte-scale datasets.

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