Rethinking Analytical Processing in the GPU Era

2025-08-06

AI Summary

To address the limited adoption of GPU-accelerated data analytics due to insufficient hardware-software co-design, this paper proposes Sirius—a native GPU-accelerated SQL engine that fundamentally rearchitects analytical database systems with GPU as the primary execution engine. Methodologically, Sirius implements GPU-native relational operators atop libcudf and leverages the Substrait standard for portable, cross-system query plan exchange, enabling decoupled, plug-and-play integration with systems such as DuckDB and Apache Doris. Its key contributions are: (1) eliminating CPU-GPU hybrid execution in favor of end-to-end GPU-native processing; and (2) establishing a standardized, portable heterogeneous acceleration interface via Substrait. Experiments demonstrate up to 7× speedup for single-node DuckDB integration and 12.5× peak throughput improvement for distributed Doris integration—both at equivalent hardware cost—significantly advancing the practical deployment of GPUs in interactive analytical workloads.

Technology Category

• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Intelligent Query Processing
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

The era of GPU-powered data analytics has arrived. In this paper, we argue that recent advances in hardware (e.g., larger GPU memory, faster interconnect and IO, and declining cost) and software (e.g., composable data systems and mature libraries) have removed the key barriers that have limited the wider adoption of GPU data analytics. We present Sirius, a prototype open-source GPU-native SQL engine that offers drop-in acceleration for diverse data systems. Sirius treats GPU as the primary engine and leverages libraries like libcudf for high-performance relational operators. It provides drop-in acceleration for existing databases by leveraging the standard Substrait query representation, replacing the CPU engine without changing the user-facing interface. On TPC-H, Sirius achieves 7x speedup when integrated with DuckDB in a single node at the same hardware rental cost, and up to 12.5x speedup when integrated with Apache Doris in a distributed setting.

Problem

• GPU-native SQL engine for faster data analytics
• Overcoming hardware and software barriers in GPU adoption
• Drop-in acceleration for existing database systems

Innovation

• GPU-native SQL engine for analytics
• Leverages libcudf for relational operators
• Uses Substrait for drop-in acceleration