Articles & Publications
Exchanges on the Road to SegWit Adoption: on Pioneers, Stragglers and Holdouts
Feb 4, 2022. Proposes method to accurately measure SegWit utilization on the Bitcoin network. Quantifies SegWit adoption and utilization by exchanges.
J. Hofmann: libtxsize—a library for automated Bitcoin transaction-size estimates arXiv:2102.12796
On Bitcoin transaction sizes
Aug 10, 2020. Investigates Bitcoin transaction sizes. Discusses models for input, output, and witness sizes. Validates models using empirical data. Presents libtxsize, a library for transaction-size estimates.
On Bitcoin's Schnorr signatures & Taproot script and witness sizes
Jun 24, 2020. Presents Schnorr signature theory. Discusses use of Schnorr signatures in Taproot. Investigate models for Pay-to-Taproot input, output, and witness sizes.
On Bitcoin script and witness sizes
Jun 14, 2020. Investigates script and witness formats. Presents analytic models for locking script, unlocking script, and witness sizes. Validates models using empirical data.
Designing a Bitcoin node crawler
Mar 27, 2020. Investigates basics of Bitcoin's node discovery protocol. Describes efficient way to crawl Bitcoin nodes. Presents serpent, a Bitcoin node crawler.
J. Hofmann, C. L. Alapatt, G. Hager, D. Fey, G. Wellein: Bridging the Architecture Gap: Abstracting Performance-Relevant Properties of Modern Server Processors. Supercomputing Frontiers and Innovations 7(2), 54-78, July 2020. DOI: 10.14529/jsfi200204
C. L. Alappat, J. Hofmann, G. Hager, H. Fehske, A. R. Bishop, G. Wellein: Understanding HPC Benchmark Performance on Intel Broadwell and Cascade Lake Processors. High Performance Computing: 35rd International Conference, ISC High Performance 2020, Frankfurt, Germany, June 21-25, 2020, Proceedings. DOI: 10.1007/978-3-030-50743-5_21
J. Hofmann: A First-Principles Approach to Performance, Power, and Energy Models for Contemporary Multi- and Many-Core Processors. Dissertation. University Erlangen-Nuremberg, Dr. Hut Verlag, October 9, 2019. ISBN: 978-3-8439-4187-7 🏆 Received Honorable Mention in SPEC Kaivalya Dixit Distinguished Dissertation Award 2019
ISC Gauss Award 2018
Presents ISC18 paper On the Accuracy and Usefulness of Analytic Energy Models for Contemporary Multicore Processors.
Student Cluster Competition at ISC18
Presents wrap-up of the student cluster competition at ISC18. Discusses strategies. Includes empirical data on energy-efficiency optimizations of Nvidia's V100 GPU.
Evolution of Cache Replacement Strategies
Investigates cache replacement strategies of recent Intel processors. Presents measurement pitfalls and workarounds. Discusses results.
J. Hofmann, G. Hager, D. Fey: On the accuracy and usefulness of analytic energy models for contemporary multicore processors. High Performance Computing: 33rd International Conference, ISC High Performance 2018, Frankfurt, Germany, June 24-28, 2018, Proceedings. DOI: 10.1007/978-3-319-92040-5_2 Preprint: arXiv:1803.01618 🏆 Received Gauss Award at ISC 2018
J. Laukemann, J. Hammer, J. Hofmann, G. Hager, and G. Wellein: Automated Instruction Stream Throughput Prediction for Intel and AMD Microarchitectures. PMBS 2018: Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems, held as part of ACM/IEEE Supercomputing 2018 (SC18), Dallas, TX, November 12, 2018, Proceedings. DOI: 10.1109/PMBS.2018.8641578 Preprint: arXiv:1809.00912
A First Glimpse at Intel's new Skylake microarchitecture
Investigates microarchitectural improvements of Intel's new Skylake microarchitecture: Front-end improvements, fused multiply-add, memory-hierarchy bandwidths, instruction latencies.
J. Hofmann, G. Hager, G. Wellein, D. Fey: An analysis of core- and chip-level architectural features in four generations of Intel server processors. High Performance Computing: 32nd International Conference, ISC High Performance 2017, Frankfurt, Germany, June 18-22, 2017, Proceedings. DOI: 10.1007/978-3-319-58667-0_16 Preprint: arXiv:1702.07554
Student Cluster Competition at ISC17
Presents wrap-up of the student cluster competition at ISC17. Discusses strategies. Includes empirical data on energy-efficiency optimizations of Nvidia's P100 GPU.
J. Hofmann, D. Fey, M. Riedmann, J. Eitzinger, G. Hager, and G. Wellein: Performance analysis of the Kahan-enhanced scalar product on current multi- and manycore processors. Concurrency and Computation: Practice and Experience, ISSN: 1532-0634. DOI: 10.1002/cpe.3921 Preprint: arXiv:1604.01890
Manually setting the Uncore frequency on Intel CPUs
Discusses separate Uncore frequency domain on Intel Processors. Highlights implications of Uncore domain using empirical data. Presents methods and tools to manually set the Uncore frequency.
J. Hofmann, D. Fey, J. Eitzinger, G. Hager, and G. Wellein: Performance analysis of the Kahan-enhanced scalar product on current multicore processors. 11th International Conference on Parallel Processing and Applied Mathematics, PPAM 2015, Krakow, Poland, September 6-9, 2015. Revised Selected Papers, Part I. DOI: 10.1007/978-3-319-32149-3_7 Preprint: 1505.02586
J. Hofmann and D. Fey: An ECM-based energy-efficiency optimization approach for bandwidth-limited streaming kernels on recent Intel Xeon processors. 4th International Workshop on Energy Efficient Supercomputing, Salt Lake City, UT, USA, November 14, 2016, Proceedings. DOI: 10.1109/E2SC.2016.16 Preprint: arXiv:1609.03347
J. Hofmann, D. Fey, J. Eitzinger, G. Hager, and G. Wellein: Analysis of Intel's Haswell Microarchitecture Using the ECM Model and Microbenchmarks. Architecture of Computing Systems - ARCS 2016: 29th International Conference, Nuremberg, Germany, April 4-7, 2016, Proceedings. DOI: 10.1007/978-3-319-30695-7_16 Preprint: arXiv:1511.03639
S. Vaas, M. Reichenbach, T. Stadelmayer, J. Hofmann, D. Fey: Embedded Parallel Computing Accelerators for Smart Control Units of Frequency Converters. 12th Workshop on Parallel Systems and Algorithms - PASA 2016, Nuremberg, Germany, April 4-7, 2016, VDE VERLAG GMBH, Berlin, Offenbach, 2016, pp. 1-5. - ISBN 978-3-8007-4157-1
J. Hofmann, J. Treibig, G. Hager, and G. Wellein: Comparing the Performance of Different x86 SIMD Instruction Sets for a Medical Imaging Application on Modern Multi- and Manycore Chips. WPMVP 2014, the Workshop on Programming Models for SIMD/Vector Processing at PPoPP 2014, Orlando, FL, Feb 16, 2014. DOI: 10.1145/2568058.2568068 Preprint: arXiv:1401.7494
Memory Bandwidth on Haswell-EP
Discusses implications of separate Uncore frequency domain on memory bandwidth. Compares empirical data gathered on different generations of Intel server processors.
J. Hofmann, J. Eitzinger, and D. Fey: Execution-Cache-Memory Performance Model: Introduction and Validation. Technical Report arXiv:1509.03118
On Correctly Measuring Runtimes
Discusses pitfalls and workarounds concerning the measurement of runtime of code. Supports arguments by presenting empirical data.
J. Hofmann, J. Treibig, G. Hager, and G. Wellein: Performance Engineering for a Medical Imaging Application on the Intel Xeon Phi Accelerator. ARCS 2014 - 27th International Conference on Architecture of Computing Systems, Workshop Proceedings, February 25-28, 2014, Luebeck, Germany. IEEE Archive Preprint: arXiv:1401.3615
J. Hofmann: Performance Evaluation of the Intel Many Integrated Core Architecture for 3D Image Reconstruction in Computed Tomography. Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2013, Erlangen, Germany. urn:nbn:de:bvb:29-opus4-41174
On the Benefits of Thread Pinning
Introduces concept of thread pinning. Presents empirical data showing the benefit of thread pinning.
J. Hofmann, S. Limmer, and D. Fey: Performance investigations of genetic algorithms on graphics cards. Swarm and Evolutionary Computation, Volume 12, October 2013, Pages 33–47 DOI: 10.1016/j.swevo.2013.04.003
J. Hofmann and D. Fey: Fast Evolutionary Algorithms: Comparing High Performance Capabilities of CPUs and GPUs. Parallel-Algorithmen und Rechnerstrukturen, Erlangen, 11./12.4.2013. Bd. 1, 1. Aufl. 2013, S. 15-24.
J. Hofmann: Leistungsanalyse Evolutionärer Algorithmen auf GPUs im Vergleich zu Multikern-CPUs. Informatiktage 2012, Bonn, 23./24. März 2012. Bd. S-11, 1. Aufl. 2012, S. 19-22. ISBN 978-3-88579-445-5
J. Hofmann: Evolving Neural Networks on GPUs. GPUs for Genetic and Evolutionary Computation, Competition at 2011 Genetic and Evolutionary Computation Conference July 12-16 2011, Dublin, Ireland