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Martin Pagel

SIGMOD 2020 Best Paper Award for the Paper "Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects"

The Paper "Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects" by Clemens Lutz, Sebastian Breß, Steffen Zeuch, Tilmann Rabl and Volker Markl received the Best Paper Award of the 2020 ACM SIGMOD/PODS International Conference on the Management of Data.
The annual ACM SIGMOD/PODS Conference is a leading international forum for database researchers, practitioners, developers, and users to explore cutting-edge ideas and results, and to exchange techniques, tools, and experiences in all aspects of data management. The SIGMOD 2020 Best Paper Award is an honouring for the outstanding cutting-edge research in the Database Systems and Information Management Group (DIMA) at TU Berlin and the Intelligent Analytics for Massive Data (IAM) group at DFKI.

GPUs have long been discussed as accelerators for database query processing because of their high processing power and memory bandwidth. However, two main challenges limit the utility of GPUs for large-scale data processing: (1) the onboard memory capacity is too small to store large data sets, yet (2) the interconnect bandwidth to CPU main-memory is insufficient for ad-hoc data transfers. As a result, GPU-based systems and algorithms run into a transfer bottleneck and do not scale to large data sets. In practice, CPUs process large-scale data faster than GPUs with current technology. In this paper, we investigate how a fast interconnect can resolve these scalability limitations using the example of NVLink 2.0. NVLink 2.0 is a new interconnect technology that links dedicated GPUs to a CPU. The high bandwidth of NVLink 2.0 enables us to overcome the transfer bottleneck and to efficiently process large data sets stored in main-memory on GPUs. We perform an in-depth analysis of NVLink 2.0 and show how we can scale a no-partitioning hash join beyond the limits of GPU memory. Our evaluation shows speedups of up to 18× over PCI-e 3.0 and up to 7.3× over an optimized CPU implementation. Fast GPU interconnects thus enable GPUs to efficiently accelerate query processing.

A preprint version is available here.

An in-depth blog post by the author is available here.

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