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Computing Systems Group (CSG) - Prof. Dr. Holger Fröning

(formerly Computer Engineering Group)

The Computing Systems Group (CSG) at the Institute of Computer Engineering at Ruprecht-Karls University of Heidelberg is focussing on improving the performance, energy-efficiency and programmability of heterogeneous computing systems. The group’s expertise covers parallel computer architecture, high-performance computing, deep learning, high-performance analytics and reconfigurable logic. The group’s research focus in the last years has been driven by application-specific computing, with projects including specialized communication models and methods for thread-parallel processors (Mantaro project), data acquisition for high-energy physics under hard real-time constraints (CERN ATLAS collaboration), deep learning techniques for resource-constrained embedded systems (DeepChip project), advanced compilation techniques for multi-GPU systems (Mekong project), and data analytics using columnar in-memory database systems (Graphite project).

The group has gained international notice for designing the first communication model that is completely in-line with a thread-collaborative processor’s execution model (GGAS), yielding substantial savings for clustered GPUs in terms of energy and time. Other efforts on GPU Computing include compilation techniques for automated partitioning and resource aggregation, and performance and power modeling of scalable heterogeneous computing systems. The group leads the GPU Education and Research Center at Heidelberg University, sponsored by NVIDIA. The group has a rich history on reconfigurable logic for specialization, including interconnection networks and memory architectures. Within the DeepChip project, we collaborate with Graz University of Technology and Materials Center Leoben to optimize Deep Learning for resource-constrained embedded platforms, and to automate the use of hybrid processors like Advanced RISC (Reduced Instruction Set Computing) Machines (ARM) and FPGA for such tasks. Similarly, our collaboration with CERN explores real-time data acquisition using commodity technology. Also, related efforts include GPU-accelerated ray-tracing for real-time treatment planning and optimizing graph processing for high-performance analytics by extending columnar in-memory databases.


  • 06/2020 Congrats to Felix for an ICPP paper, and to Laura and Alexander for one P2S2 Workshop paper each!
  • 05/2020 Call for Papers for ITEM Workshop is open: http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=104511
  • 05/2020 WEML coverage in the most recent HiPEAC Magazine: https://www.hipeac.net/magazine/7154/ 
  • 05/2020 Holger Fröning has been accepted as Visiting Scientist at the Chinese Academy of Sciences (CAS), respectively their President’s International Fellowship Initiative (PIFI 2020). Jointly with Bo Li from CAS Beijing, they will explore emerging materials for resistive RAM and its use for demanding applications such as embedded machine learning.
  • 04/2020 Jointly with colleagues from Graz University of Technology, University of Duisburg-Essen, and XILINX Research we will organize the ITEM Workshop (IoT, Edge, and Mobile for Embedded Machine Learning), collocated with ECML-PKDD2020!
  • 04/2020 Presentations of the 3rd Workshop on Embedded Machine Learning are online: https://www.deepchip.org/weml2020
  • 03/2020 Group renaming: due to organizational changes, the group is renamed to Computing Systems Group (CSG). This addresses the systems aspect that is prevailing in our research, ranging from HW/SW co-design, over compilers, runtime systems and other tools to selected applications and their frameworks. Furthermore, it avoids the long-time naming conflict in between the group (Computer Engineering Group) and the Institute of Computer Engineering (ZITI).
  • 02/2020 New ICASSP paper on Real-Time Single-Channel Single-Voice Separation!
  • 02/2020 New group member: Vahdaneh Kiani, working on scheduling of multi-GPU systems for radiation therapy
  • 01/2020 New TPDS paper on Effective Co-Scheduling of Concurrent Kernels on GPUs!
  • 08/2019 A first prototype of our Mekong Compiler is available here. Mekong gears to automatically partition single-CUDA programs for a multi-GPU execution, relying on a polyhedral analysis and transformations on the LLVM IR.
  • 03/2019 Congrats to Matthias for an accepted IPDPS paper, and to Alexander for an accepted GPPGU@ASPLOS paper! 
  • 11/2018 Alumna Lena Oden is now assistant professor (Juniorprofessorin) at FernUniversität Hagen. Congratulations!
  • 11/2018 Congrats to Kazem for getting his work on "Metric Selection for GPU Kernel Classification" accepted at TACO/HIPEAC2019!
  • 11/2018 Second Workshop on Embedded Machine Learning (WEML2018) to take place at Heidelberg University, Nov 8, 2018
  • 10/2018 Invited talk: Loving the picoJoule at all Scales, 5th Seminar on Energy Efficiency, Computing Center, Heidelberg University, Oct 31, 2018
  • 07/2018 Guest lecture by Heiner Litz on "Architecture for ML and ML for Architecture" (past of Advanced Parallel Computing)
  • 07/2018 Congrats to Günther for his first conference paper (ECML 2018)!
  • 06/2018 Congrats to Tommaso who successfully defended his PhD thesis!
  • 05/2018 Congrats to Alejandro for his first conference paper at ACM DEBS!
  • 05/2018 Invited talk: Machine Learning Driving Innovative Computing Concepts, Basel University, Switzerland, May 18, 2018
  • 04/2018 Relocation complete, find us in our new lab: Heidelberg University, INF368, 5th floor
  • 03/2018 Invited talk: Post-Dennard Processor Architectures in their Teenage Decade - Observations and Trends, Symposium on Modern Database Platforms, Fachgruppe Datenbanksysteme der Gesellschaft für Informatik e.V. (GI), March 1–2, 2018, SAP SE, St. Leon-Rot, Germany
  • 02/2018 Invited talk: Post-Dennard Performance Scaling in its Teenage Decade: picoJoule replaces nanosecond, ORB lab, Heidelberg University, Germany
  • 01/2018 ICASSP paper accepted for publication: Matthias Zöhrer, Lukas Pfeifenberger, Günther Schindler, Holger Fröning, and Franz Pernkopf, Resource Efficient Deep Eigenvector Beamforming, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 15–20 April 2018, Calgary, Alberta, Canada.
  • 01/2018 Invited talk by Johannes Doerfert and Sebastian Hack (Saarland University) on "Polyhedral Value & Memory Analysis", Feb 2, 2018, 10am, Room A3.04
  • 01/2018 Congrats to Felix for getting his CCPE journal paper on energy-proportional link width scaling accepted!
  • 01/2018 Congrats to Benjamin who successfully defended his PhD thesis!
  • 01/2018 Invited talk: Post-Dennard Performance Scaling in its Teenage Decade: picoJoule replaces nanosecond, Copenhagen University, Denmark
  • 01/2018 Guest lecture on “GPUs (and Hybrid Processors)”, part of the course “Computer Systems”, Copenhagen University, Denmark

Main Research Projects

  • Mekong (formerly GCUDA): the main objective of (GPU) Mekong is to provide a simplified path to scale out the execution of GPU programs from one GPU to almost any number, independent of whether the GPUs are located within one host or distributed at the cloud or cluster level. Unlike existing solutions, this work proposes to maintain the GPU’s native programming model, which relies on a bulk-synchronous, thread-collective execution; that is, no hybrid solutions like OpenCL/CUDA programs combined with message passing are required. As a result, we can maintain the simplicity and efficiency of GPU computing in the scale-out case, together with a high productivity and performance. Besides receiving a Google Faculty Research Award in 2014, a BMBF proposal for follow-up funding has been granted. Since 2014. More: http://www.gpumekong.org
  • Deep learning on resource-constrained systems (DeepChip): is a framework that supports the implementation of deep convolutional networks for machine learning on embedded platforms. To maximize energy efficiency, it relies on hybrid processor architectures that typically embrace ARM cores in combination with reconfigurable logic. DeepChip heavily relies on extreme forms of quantization and related unsafe optimizations to match the computational and memory requirements of deep neural networks to available hardware resources. By integrating these techniques in existing tool stacks for machine learning, it allows specialists in machine learning to leverage the ubiquitous availability and high energy-efficiency of advanced embedded systems for an improved use of classification and regression methods. This D-A-CH (DFG/FWF) project is a collaboration with the group of Franz Pernkopf from Technical University of Graz. While we already started our efforts with the help of research assistants and master students earlier, the funded project has just started in December 2016. Since 2015. More: http://www.deepchip.org
  • Integrated Power Modeling: while various related work explores modeling power consumption for processors, the interconnection network is yet poorly addressed. However, for a comprehensive understanding of power consumption is necessary to design optimizations that increase the energy efficiency, in particular as data movements increasingly dominate overall power consumption. In this project we collaborate with Pedro Garcia from the University of Castilla-La Mancha (Spain) to explore such aspects using simulations and to derive suitable models that help understanding power and energy consumption. Since 2015.
  • Graphite: while common approaches try to optimally support graph computations by dedicated software stacks (e.g. graph database management systems), in this work we explore how existing columnar databases can be extended to optimally support graph queries. Direct advantages include reduced data movements, in addition other aspects like attributes, updates, concurrency and NUMA effects can be much better addressed. This is a joint project with SAP, which is also funding this work, and Technical University of Dresden. Since 2015.
  • CERN ATLAS/LHCb:  for the ATLAS and LHCb high-energy physics experiments at CERN we are contributing to the data acquisition system, in particular the data collection manager. In this project, a commodity Ethernet network is used and upper-level software layers like the data collection manager guarantee minimal collection latencies by traffic shaping techniques. In addition, a complete data-flow messaging library is designed and optimized for this special application. This is a collaboration with colleagues from CERN and University of Castilla-La Mancha, Spain, and currently being extended by system modeling and data compression techniques. Since 2013.


We gratefully acknowledge the generous support that we are receiving. Recent sponsors include Helmholtz, BMBF, DFG, Google, NVIDIA, Xilinx, and Carl-Zeiss Stiftung.

Public code repository

Please find our public code here: https://github.com/UniHD-CEG

Principal Investigator

  • Prof. Dr. Holger Fröning
    Full Professor
    Fon: +49 6221 54-16442 (email preferred)
    Office: INF368, Room 529
    Office hours: by appointment

PhD Students

  • Felix Zahn - INF368, Room 526 - +49 6221 54-16425, felix.zahn(at)ziti.uni-heidelberg.de
  • Günther Schindler - INF368, Room 530 - +49 6221 54-16443, guenther.schindler(at)ziti.uni-heidelberg.de
  • Lorenz Braun - INF368, Room 528 - +49 6221 54-16441, lorenz.braun(at)ziti.uni-heidelberg.de
  • Bernhard Klein - INF368, Room 530 - +49 6221 54-16443, bernhard.klein(at)ziti.uni-heidelberg.de
  • Vahdaneh Kiani - INF368, Room 528 - +49 6221 54-16441, vahdaneh.kiani(at)ziti.uni-heidelberg.de

External PhD Students

  • Matthias Hauck, SAP, matthias.hauck(at)sap.com
  • Dennis Rieber, Bosch, DennisSebastian.Rieber(at)de.bosch.com
  • Laura Promberger, CERN, laura.promberger(at)cern.ch
  • Jonas Dann, SAP, jonas.dann(at)sap.com

Research Assistants and Graduate Students - INF368, Room 531/527

  • Lukas Eisert
  • Jan Metzger
  • Hendrik Borras, borras(at)stud.uni-heidelberg.de
  • David Sprengel, d.sprengel(at)stud.uni-heidelberg.de
  • Georg Weisert, weisert(at)stud.uni-heidelberg.de
  • Florian Nowak, f.nowak(at)stud.uni-heidelberg.de
  • Lisa Kuhn, kuhn(at)cl.uni-heidelberg.de

Internships and research stays of group members

  • Felix Zahn, University of Castilla-La Mancha, Spain, 01-02/2017 (sponsored)
  • Holger Fröning, NVIDIA Research, Santa Clara, CA, US, 05-10/2016 (sponsored)
  • Benjamin Klenk, NVIDIA Research, Santa Clara, CA, US, 02-07/2016 (sponsored)
  • Felix Zahn, University of Castilla-La Mancha, Spain, 10/2015 (sponsored)
  • Holger Fröning, Technical University of Graz, Austria, 03/2015 (sponsored)
  • Benjamin Klenk, NVIDIA Research, Santa Clara, CA, US, 03-06/2015 (sponsored)
  • Alexander Matz, Intel Labs, Hillsboro, OR, US, 01-05/2015 (sponsored)

Former members & visitors (date of leave/visit)

  • Kevin Stehle (MSc student, leaving as PhD candidate to CERN, 03/2020)
  • Paul Bethge (MSc student, 02/2020)
  • Dilan Canpolat (MSc student, 01/2020)
  • Berra Sayin (visiting student, Sabancı University, Turkey, 05-08/2019)
  • Shreyas Ravishankar (visiting student, Birla Institute of Technology and Science Pilani (BITS Pilani), India, 05-08/2019)
  • Alexander Matz (PhD student, 2019), first appointment at IMC (Amsterdam, Netherlands)
  • Alejandro Santos (PhD student, 2019)
  • Florian Nowak (BSc student, 2019)
  • David Marquant (MSc student, 2019), first appointment at HD Vision Systems GmbH
  • Michael Harbarth (MSc student, 2019)
  • Himanshu Tiwari (MSc student, 2018)
  • Antsa Andriamboavonjy (MSc student, 2018), first appointment at SAP (Walldorf, Germany)
  • Sven Nobis (MSc student, 2018)
  • Andreas Melzer (MSc student, 2018), first appointment at SAP (Walldorf, Germany)
  • Armin Schäffer (MSc student, 2018), first appointment at HMS Analytical Software (Heidelberg, Germany)
  • Klaus Neumann (MSc student, 2018)
  • Dennis Rieber (MSc student, 2017), first appointment at Robert Bosch GmbH (Renningen, Germany)
  • Benjamin Klenk, (PhD student, 2017), first appointment at NVIDIA Research, Santa Clara (US)
  • Steffen Lammel (MSc student and research assistant, 2017), first appointment at SAP (Walldorf, Germany)
  • Julian Schwing (MSc student, 2017), first appointment at SAP (Walldorf, Germany)
  • Kazem Shekofteh, PhD student at Ferdowsi University of Mashhad, Mashhad, Iran, 11/2016-05/2017
  • Artur Kühlwein (MSc student, 2017)
  • Tommaso Colombo (PhD student, 2016), first appointment at CERN
  • Dominik Sterk, (MSc student, 2016)
  • Christoph Klein, (MSc student, 2016), first appointment as PhD student at Heidelberg University
  • Daniel Schlegel (MSc student, 2016), first appointment at EVS Broadcast Equipment
  • Benjamin Baumann (MSc student, 2016)
  • Eugen Rusakov (MSc student, 2016), first appointment as PhD student at TU Dortmund
  • Julian Romera (MSc student, 2016)
  • Lena Oden (PhD, defended 04/2015), first appointment at Postdoc at Argonne National Labs, Chicago, IL, US.
  • Francisco Andujar (visiting PhD student, Universidad Castilla-La Mancha, Spain, 2014)
  • Pedro Garcia (visiting scientist, Universidad Castilla-La Mancha, Spain, 2013)
  • Hector Montaner (PhD student, defended 04/2013 with highest degrees, Technical University of Valencia, Spain, 2013)
  • Jesus Escudero Sahuquillo (visiting scientist, Universidad Castilla-La Mancha, Spain, 2012)
  • Manuel Dewald (MSc student, 2013), first appointment at SAP
  • Elena Kuss (MSc student, 2012), first appointment as PhD student at University of Mannheim




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