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Clemens Lutz

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Senior Developer Technology Engineer NVIDIA

Clemens is investigating large-scale data management on GPU platforms. He specializes on NVLink C2C due to its high potential to speed-up database workloads. His work on this topic has been recognized with research awards at the SIGMOD and BTW conferences. Prior to joining NVIDIA, Clemens earned his PhD at TU Berlin on GPU-enabled data management, for which he received the GI DBIS Dissertation Award 2025.

Bio from: Data + AI Summit 2025

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Tracing the Path of a Row Through a GPU-Enabled Query Engine on the Grace-Blackwell Architecture

Grace-Blackwell is NVIDIA’s most recent GPU system architecture. It addresses a key concern of query engines: fast data access. In this session, we will take a close look at how GPUs can accelerate data analytics by tracing how a row flows through a GPU-enabled query engine.Query engines read large data from CPU memory or from disk. On Blackwell GPUs, a query engine can rely on hardware-accelerated decompression of compact formats. The Grace-Blackwell system takes data access performance even further, by reading data at up to 450 GB/s across its CPU to GPU interconnect. We demonstrate full end-to-end SQL query acceleration using GPUs in a prototype query engine using industry standard benchmark queries. We compare the results to existing CPU solutions.Using Apache Spark™ and the RAPIDS Accelerator for Apache Spark, we demonstrate the impact GPU acceleration has on the performance of SQL queries at the 100TB scale using NDS, a suite that simulates real-world business scenarios.