Max Heimel
Technical University of Berlin
18 Papers
175 Citations
Max Heimel is an academic researcher from Technical University of Berlin. The author has contributed to research in topics: Query optimization & Computer science. The author has an hindex of 11, co-authored 18 publications.
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Papers
Hardware-oblivious parallelism for in-memory column-stores
Max Heimel,Michael Saecker,Holger Pirk,Stefan Manegold,Volker Markl +4 more
- 01 Jul 2013
TL;DR: This work proposes an alternative design for a parallel database engine, based on a single set of hardware-oblivious operators, which are compiled down to the actual hardware at runtime, which reduces the development overhead for parallel database engines, while achieving competitive performance to hand-tuned systems.
Self-Tuning, GPU-Accelerated Kernel Density Models for Multidimensional Selectivity Estimation
Max Heimel,Martin Kiefer,Volker Markl +2 more
- 27 May 2015
TL;DR: This paper substantially expand the state-of-the-art in KDE-based selectivity estimation by improving along three dimensions, and develops methods to continuously adapt the estimator to changes in both the database and the query workload.
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GPU-Accelerated Database Systems: Survey and Open Challenges
Sebastian Breß,Max Heimel,Norbert Siegmund,Ladjel Bellatreche,Gunter Saake +4 more
- 01 Jan 2014
TL;DR: A reference architecture is proposed, indicating how GPU acceleration can be integrated in existing DBMSs, and key properties, important trade-offs and typical challenges of GPU-aware database architectures are presented.
Estimating join selectivities using bandwidth-optimized kernel density models
Martin Kiefer,Max Heimel,Sebastian Breß,Volker Markl +3 more
- 01 Sep 2017
TL;DR: This paper introduces a modern, self-tuning selectivity estimator for range scans based on KDE that out-performs state-of-the-art multidimensional histograms and is efficient to evaluate on graphics cards and proposes two approaches to building a KDE model from a sample drawn from the join result.
Massively parallel data analysis with PACTs on Nephele
Alexander Alexandrov,Max Heimel,Volker Markl,Dominic Battré,Fabian Hueske,Erik Nijkamp,Stephan Ewen,Odej Kao,Daniel Warneke +8 more
- 01 Sep 2010
TL;DR: Large-scale data analysis applications require processing and analyzing of Terabytes or even Petabytes of data, particularly in the areas of web analysis or scientific data management.