- Clemens Lutz | TU Berlin

PhD student and research associate at TU Berlin mentored by Volker Markl and cooperating with Tilmann Rabl and Steffen Zeuch.

My research interests are data management using modern hardware and distributed systems. Currently I am investigating how we can use fast, next-generation interconnects such as NVLink to scale data management on GPUs.

Previously, I worked as a researcher at DFKI. Before starting my PhD studies, I received my MSc degree from ETH Zurich in collaboration with IBM Research, Zurich under supervision of Thomas R. Gross and Animesh Trivedi. My Master's was preceded by an exchange at Imperial College London, where I wrote my BSc thesis under supervision of Peter Pietzuch and Paolo Costa, during my Bachelor's studies at ETH Zurich.

clemens.lutz@tu-berlin.de

+49 30 314 23515

DIMA
Faculty EECS (IV)
sec. E-N 7
Einsteinufer 17
10587 Berlin
Germany

TU Berlin E-N 709

News

15 June 2022: Our Triton join is now published at SIGMOD 2022. Check out our talk on YouTube. We've also released our code on GitHub!
7–10 June 2022: I was graciously invited to give a series of research talks on Scalable Data Processing using GPUs with Fast Interconnects at the MIT Data Systems Group, the Harvard Data Systems Laboratory, and the Complex Analytics & Scalable Processing Research Lab at Boston University.
18 Mar 2022: Apostolos Planas defended his MSc thesis on Cooperative Heterogeneous Query Execution. Congrats!
14 Dec 2021: Our paper on the Triton Join: Efficiently Scaling to a Large Join State on GPUs with Fast Interconnects has been accepted for SIGMOD 2022!
21 Jun 2021: Our short paper on An Energy-Efficient Stream Join for the Internet of Things has been published at DaMoN'21. My congrats to Adrian Michalke!
18 Jun 2021: Our paper on Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects has been awarded the SIGMOD Reproducibility badge. You can see the badge in the ACM DL. Thanks to Apostolos Planas for helping to prepare the reproducibility submission, and to the reviewers for their efforts in reproducing our work!
19 Apr 2021: We have received the BTW Best Paper award for our work on Fast CSV Loading Using GPUs and RDMA for In-Memory Data Processing.
16 Mar 2021: Our paper on Fast CSV Loading Using GPUs and RDMA for In-Memory Data Processing has been awarded the BTW Reproducibility Badge.

Publications

SIGMOD'22 Paper Code YouTube: Triton Join: Efficiently Scaling to a Large Join State on GPUs with Fast Interconnects Clemens Lutz, Sebastian Breß, Steffen Zeuch, Tilmann Rabl, Volker Markl, accepted for the ACM International Conference on Management of Data, June 12–17, 2022, Philadelphia, PA, USA.
DaMoN'21 Paper Code Slides: An Energy-Efficient Stream Join for the Internet of Things Adrian Michalke, Philipp M. Grulich, Clemens Lutz, Steffen Zeuch, Volker Markl, in the 17th ACM Int. Workshop on Data Management on New Hardware (DaMoN'21), held online with SIGMOD/PODS, June 21st, 2021.
BTW'21 Best Paper Reproducible Paper Slides: Fast CSV Loading Using GPUs and RDMA for In-Memory Data Processing Alexander Kumaigorodski, Clemens Lutz, Volker Markl, in Database Systems for Business, Technology and Web, April 19th - June 21st, 2021, Dresden, Germany.
SIGMOD'20 Best Paper Reproducible Paper Blog YouTube Slides: Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects Clemens Lutz, Sebastian Breß, Steffen Zeuch, Tilmann Rabl, Volker Markl, in the ACM International Conference on Management of Data, June 14–19, 2020, Portland, OR, USA.
PVLDB'19 Paper: Analyzing Efficient Stream Processing on Modern Hardware Steffen Zeuch, Bonaventura Del Monte, Jeyhun Karimov, Clemens Lutz, Manuel Renz, Jonas Traub, Sebastian Breß, Tilmann Rabl, Volker Markl, in The Proceedings of the VLDB Endowment, Vol. 12, No. 5, Los Angeles, CA, USA, August 26th-30th, 2019.
Datenbanken Spektrum Paper Blog Code: Efficient and Scalable k-Means on GPUs Clemens Lutz, Sebastian Breß, Tilmann Rabl, Steffen Zeuch, Volker Markl, in Datenbanken Spektrum 2018.
DaMoN'18 Paper Code Slides Poster: Efficient k-Means on GPUs Clemens Lutz, Sebastian Breß, Tilmann Rabl, Steffen Zeuch, Volker Markl, in the 14th ACM Int. Workshop on Data Management on New Hardware (DaMoN'18), colocated with SIGMOD/PODS, Houston, TX, USA, June 11th, 2018.
ICDCS'15 Paper: RStore: A Direct-Access DRAM-based Data Store Animesh Trivedi, Patrick Stuedi, Bernard Metzler, Clemens Lutz, Martin Schmatz, Thomas R. Gross, in the 35th IEEE Int. Conf. Distributed Computing Systems (ICDCS'15), Columbus, OH, USA, June 29th - July 2nd, 2015.
MSc 2014 Thesis: Carafe: High-Performance, In-Memory Graph Processing with RDMA. Clemens Lutz, MSc thesis, D-INFK, ETH Zurich, Zurich, Switzerland, October 2014

Theses & Teaching

Thesis Supervision

Thesis: Cooperative Heterogeneous Query Execution. Apostolos Planas, MSc thesis, Faculty IV (EECS), TU Berlin, Berlin, Germany, February 2022
Thesis: Fast CSV Loading Using GPUs and RDMA for In-Memory Data Processing. Alexander Kumaigorodski, MSc thesis, Faculty IV (EECS), TU Berlin, Berlin, Germany, July 2020
Thesis: Lock-based Data Structures on GPUs with Independent Thread Scheduling. Phillip Grote, BSc thesis, Faculty IV (EECS), TU Berlin, Berlin, Germany, February 2020

Teaching

In-Memory Databases On Modern Hardware, SS 2021, TU Berlin
Database Systems Seminar, SS 2021, TU Berlin
Big Data Analytics Seminar, WS 2020, TU Berlin
Big Data Analytics Project, WS 2020, TU Berlin
Information Systems and Data Analysis, SS 2020, TU Berlin
In-Memory Databases On Modern Hardware, SS 2020, TU Berlin
Databases Laboratory, WS 2019, TU Berlin
Information Systems and Data Analysis, SS 2019, TU Berlin
In-Memory Databases On Modern Hardware, SS 2019, TU Berlin
Databases Laboratory, WS 2018, TU Berlin
Information Systems and Data Analysis, SS 2018, TU Berlin
Databases Laboratory, WS 2017, TU Berlin
Information Systems and Data Analysis, SS 2017, TU Berlin
Databases Laboratory, WS 2016, TU Berlin
Information Management Seminar, SS 2016, TU Berlin
Databases Seminar, WS 2015, TU Berlin
Operating Systems and Networks, SS 2011, ETH Zurich