Zobrazeno 1 - 10
of 99
pro vyhledávání: '"Holger Fröning"'
Publikováno v:
International Workshop on OpenCL.
Publikováno v:
International Workshop on OpenCL.
Publikováno v:
IEEE Transactions on Parallel and Distributed Systems. 31:766-778
While GPUs are meantime omnipresent for many scientific and technical computations, they still continue to evolve as processors. An important recent feature is the ability to execute multiple kernels concurrently via queue streams. However, experimen
Publikováno v:
GRADES-NDA@SIGMOD
Recent advances in reprogrammable hardware (e. g., FPGAs) and memory technology (e. g., DDR4, HBM) promise to solve performance problems inherent to graph processing like irregular memory access patterns on traditional hardware (e. g., CPU). While se
Publikováno v:
ACM Transactions on Architecture and Code Optimization. 15:1-27
Graphics Processing Units (GPUs) are vastly used for running massively parallel programs. GPU kernels exhibit different behavior at runtime and can usually be classified in a simple form as either “compute-bound” or “memory-bound.” Recent GPU
Autor:
Bernhard Klein, Lisa Kuhn, Johannes Weis, Arne Emmel, Yannik Stradmann, Johannes Schemmel, Holger Fröning
Publikováno v:
Communications in Computer and Information Science ISBN: 9783030937355
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::180cf87a9e48f7895a6655be6499bdee
https://doi.org/10.1007/978-3-030-93736-2_32
https://doi.org/10.1007/978-3-030-93736-2_32
The ever increasing amount of generated data makes it more and more beneficial toutilize compression to trade computations for data movement and reduced storagerequirements.Lately,dedicatedacceleratorshavebeenintroducedtooffloadcompres-sion tasks fro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::23845ec548f6655798ec95459e6dda6b
http://cds.cern.ch/record/2809706
http://cds.cern.ch/record/2809706
Publikováno v:
ICPR
We present two methods to reduce the complexity of Bayesian network (BN) classifiers. First, we introduce quantization-aware training using the straight-through gradient estimator to quantize the parameters of BNs to few bits. Second, we extend a rec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::10a87771725a8c22cb548cda9e2c81f5
http://arxiv.org/abs/2010.11773
http://arxiv.org/abs/2010.11773
Autor:
Holger Fröning, Felix Zahn
Publikováno v:
ICPP
Data movements through interconnection networks exceed local memory accesses in terms of latency as well as energy by multiple orders of magnitude. While many optimizations make great effort to improve memory accesses, large distances in the network
Autor:
Irena Koprinska, Paolo Mignone, Riccardo Guidotti, Szymon Jaroszewicz, Holger Fröning, Francesco Gullo, Pedro M. Ferreira, Damian Roqueiro, Gaia Ceddia, Slawomir Nowaczyk, João Gama, Rita Ribeiro, Ricard Gavaldà, Elio Masciari, Zbigniew Ras, Ettore Ritacco, Francesca Naretto, Andreas Theissler, Przemyslaw Biecek, Wouter Verbeke, Gregor Schiele, Franz Pernkopf, Michaela Blott, Ilaria Bordino, Ivan Luciano Danesi, Giovanni Ponti, Lorenzo Severini, Annalisa Appice, Giuseppina Andresini, Ibéria Medeiros, Guilherme Graça, Lee Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Diego Saldana, Konstantinos Sechidis, Arif Canakoglu, Sara Pido, Pietro Pinoli, Albert Bifet, Sepideh Pashami
This volume constitutes the papers of several workshops which were held in conjunction with the International Workshops of ECML PKDD 2022 on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022, held in Gre