Zobrazeno 1 - 10
of 124
pro vyhledávání: '"Gansterer, Wilfried"'
Autor:
Alves, João N. F., Moustafa, Samir, Benkner, Siegfried, Francisco, Alexandre P., Gansterer, Wilfried N., Russo, Luís M. S.
The inference and training stages of Graph Neural Networks (GNNs) are often dominated by the time required to compute a long sequence of matrix multiplications between the sparse graph adjacency matrix and its embedding. To accelerate these stages, w
Externí odkaz:
http://arxiv.org/abs/2409.02208
Prior attacks on graph neural networks have mostly focused on graph poisoning and evasion, neglecting the network's weights and biases. Traditional weight-based fault injection attacks, such as bit flip attacks used for convolutional neural networks,
Externí odkaz:
http://arxiv.org/abs/2311.01205
Message passing neural networks iteratively generate node embeddings by aggregating information from neighboring nodes. With increasing depth, information from more distant nodes is included. However, node embeddings may be unable to represent the gr
Externí odkaz:
http://arxiv.org/abs/2310.04190
Quantization is a technique for reducing deep neural networks (DNNs) training and inference times, which is crucial for training in resource constrained environments or applications where inference is time critical. State-of-the-art (SOTA) quantizati
Externí odkaz:
http://arxiv.org/abs/2107.13490
Autor:
Agullo, Emmanuel, Altenbernd, Mirco, Anzt, Hartwig, Bautista-Gomez, Leonardo, Benacchio, Tommaso, Bonaventura, Luca, Bungartz, Hans-Joachim, Chatterjee, Sanjay, Ciorba, Florina M., DeBardeleben, Nathan, Drzisga, Daniel, Eibl, Sebastian, Engelmann, Christian, Gansterer, Wilfried N., Giraud, Luc, Goeddeke, Dominik, Heisig, Marco, Jezequel, Fabienne, Kohl, Nils, Li, Xiaoye Sherry, Lion, Romain, Mehl, Miriam, Mycek, Paul, Obersteiner, Michael, Quintana-Orti, Enrique S., Rizzi, Francesco, Ruede, Ulrich, Schulz, Martin, Fung, Fred, Speck, Robert, Stals, Linda, Teranishi, Keita, Thibault, Samuel, Thoennes, Dominik, Wagner, Andreas, Wohlmuth, Barbara
This work is based on the seminar titled ``Resiliency in Numerical Algorithm Design for Extreme Scale Simulations'' held March 1-6, 2020 at Schloss Dagstuhl, that was attended by all the authors. Naive versions of conventional resilience techniques w
Externí odkaz:
http://arxiv.org/abs/2010.13342
As computers reach exascale and beyond, the incidence of faults will increase. Solutions to this problem are an active research topic. We focus on strategies to make the preconditioned conjugate gradient (PCG) solver resilient against node failures,
Externí odkaz:
http://arxiv.org/abs/2007.04066
Publikováno v:
Proceedings of the 2020 SIAM Conference on Parallel Processing for Scientific Computing (2020) 81-92
The observed and expected continued growth in the number of nodes in large-scale parallel computers gives rise to two major challenges: global communication operations are becoming major bottlenecks due to their limited scalability, and the likelihoo
Externí odkaz:
http://arxiv.org/abs/1912.09230
Publikováno v:
Proceedings of the 48th International Conference on Parallel Processing (2019) 67:1-67:10
We study algorithmic approaches for recovering from the failure of several compute nodes in the parallel preconditioned conjugate gradient (PCG) solver on large-scale parallel computers. In particular, we analyze and extend an exact state reconstruct
Externí odkaz:
http://arxiv.org/abs/1907.13077
Publikováno v:
In Parallel Computing September 2016 57:167-184
Publikováno v:
In Computer Networks 8 May 2016 100:28-44