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pro vyhledávání: '"Georganas A"'
Autor:
Golin, Renato, Chelini, Lorenzo, Siemieniuk, Adam, Madhu, Kavitha, Hasabnis, Niranjan, Pabst, Hans, Georganas, Evangelos, Heinecke, Alexander
This work proposes a compilation flow using open-source compiler passes to build a framework to achieve ninja performance from a generic linear algebra high-level abstraction. We demonstrate this flow with a proof-of-concept MLIR project that uses in
Externí odkaz:
http://arxiv.org/abs/2404.15204
Autor:
Georganas, Evangelos, Kalamkar, Dhiraj, Voronin, Kirill, Kundu, Abhisek, Noack, Antonio, Pabst, Hans, Breuer, Alexander, Heinecke, Alexander
During the past decade, Deep Learning (DL) algorithms, programming systems and hardware have converged with the High Performance Computing (HPC) counterparts. Nevertheless, the programming methodology of DL and HPC systems is stagnant, relying on hig
Externí odkaz:
http://arxiv.org/abs/2304.12576
Publikováno v:
EFSA Supporting Publications. Aug2024, Vol. 21 Issue 8, p1-42. 42p.
Rapid advances in artificial intelligence (AI) technology have led to significant accuracy improvements in a myriad of application domains at the cost of larger and more compute-intensive models. Training such models on massive amounts of data typica
Externí odkaz:
http://arxiv.org/abs/2204.10943
Autor:
European Food Safety Authority, European Centre for Disease Prevention and Control, European Union Reference Laboratory for Avian Influenza, Alice Fusaro, José L. Gonzales, Thijs Kuiken, Gražina Mirinavičiūtė, Éric Niqueux, Karl Ståhl, Christoph Staubach, Olov Svartström, Calogero Terregino, Katriina Willgert, Francesca Baldinelli, Roxane Delacourt, Alexandros Georganas, Lisa Kohnle
Publikováno v:
EFSA Journal, Vol 22, Iss 3, Pp n/a-n/a (2024)
Abstract Between 2 December 2023 and 15 March 2024, highly pathogenic avian influenza (HPAI) A(H5) outbreaks were reported in domestic (227) and wild (414) birds across 26 countries in Europe. Compared to previous years, although still widespread, th
Externí odkaz:
https://doaj.org/article/dba70efdece3449880aca5196cf54607
Autor:
Chaudhary, Narendra, Misra, Sanchit, Kalamkar, Dhiraj, Heinecke, Alexander, Georganas, Evangelos, Ziv, Barukh, Adelman, Menachem, Kaul, Bharat
Convolutional neural networks (CNNs) have found many applications in tasks involving two-dimensional (2D) data, such as image classification and image processing. Therefore, 2D convolution layers have been heavily optimized on CPUs and GPUs. However,
Externí odkaz:
http://arxiv.org/abs/2104.08002
Autor:
Md, Vasimuddin, Misra, Sanchit, Ma, Guixiang, Mohanty, Ramanarayan, Georganas, Evangelos, Heinecke, Alexander, Kalamkar, Dhiraj, Ahmed, Nesreen K., Avancha, Sasikanth
Full-batch training on Graph Neural Networks (GNN) to learn the structure of large graphs is a critical problem that needs to scale to hundreds of compute nodes to be feasible. It is challenging due to large memory capacity and bandwidth requirements
Externí odkaz:
http://arxiv.org/abs/2104.06700
Autor:
Georganas, Evangelos, Kalamkar, Dhiraj, Avancha, Sasikanth, Adelman, Menachem, Aggarwal, Deepti, Anderson, Cristina, Breuer, Alexander, Bruestle, Jeremy, Chaudhary, Narendra, Kundu, Abhisek, Kutnick, Denise, Laub, Frank, Md, Vasimuddin, Misra, Sanchit, Mohanty, Ramanarayan, Pabst, Hans, Retford, Brian, Ziv, Barukh, Heinecke, Alexander
During the past decade, novel Deep Learning (DL) algorithms, workloads and hardware have been developed to tackle a wide range of problems. Despite the advances in workload and hardware ecosystems, the programming methodology of DL systems is stagnan
Externí odkaz:
http://arxiv.org/abs/2104.05755
Autor:
Kalamkar, Dhiraj, Georganas, Evangelos, Srinivasan, Sudarshan, Chen, Jianping, Shiryaev, Mikhail, Heinecke, Alexander
During the last two years, the goal of many researchers has been to squeeze the last bit of performance out of HPC system for AI tasks. Often this discussion is held in the context of how fast ResNet50 can be trained. Unfortunately, ResNet50 is no lo
Externí odkaz:
http://arxiv.org/abs/2005.04680
Autor:
Yelick, Katherine, Buluc, Aydin, Awan, Muaaz, Azad, Ariful, Brock, Benjamin, Egan, Rob, Ekanayake, Saliya, Ellis, Marquita, Georganas, Evangelos, Guidi, Giulia, Hofmeyr, Steven, Selvitopi, Oguz, Teodoropol, Cristina, Oliker, Leonid
Genomic data sets are growing dramatically as the cost of sequencing continues to decline and small sequencing devices become available. Enormous community databases store and share this data with the research community, but some of these genomic dat
Externí odkaz:
http://arxiv.org/abs/2001.06989