Zobrazeno 1 - 10
of 130
pro vyhledávání: '"Ben-Nun, Tal"'
Autor:
Baumann, Yves, Ben-Nun, Tal, Besta, Maciej, Gianinazzi, Lukas, Hoefler, Torsten, Luczynski, Piotr
Publikováno v:
IEEE International Parallel and Distributed Processing Symposium, IPDPS 2024, San Francisco, CA, USA, May 27-31 (2024) 180-192
Contemporary accelerator designs exhibit a high degree of spatial localization, wherein two-dimensional physical distance determines communication costs between processing elements. This situation presents considerable algorithmic challenges, particu
Externí odkaz:
http://arxiv.org/abs/2404.12953
Autor:
Gianinazzi, Lukas, Ziogas, Alexandros Nikolaos, Huang, Langwen, Luczynski, Piotr, Ashkboos, Saleh, Scheidl, Florian, Carigiet, Armon, Ge, Chio, Abubaker, Nabil, Besta, Maciej, Ben-Nun, Tal, Hoefler, Torsten
Publikováno v:
PPoPP'24: Proceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (2024) 404-416
We propose a novel approach to iterated sparse matrix dense matrix multiplication, a fundamental computational kernel in scientific computing and graph neural network training. In cases where matrix sizes exceed the memory of a single compute node, d
Externí odkaz:
http://arxiv.org/abs/2402.19364
Autor:
Castro, Roberto L., Ivanov, Andrei, Andrade, Diego, Ben-Nun, Tal, Fraguela, Basilio B., Hoefler, Torsten
The increasing success and scaling of Deep Learning models demands higher computational efficiency and power. Sparsification can lead to both smaller models as well as higher compute efficiency, and accelerated hardware is becoming available. However
Externí odkaz:
http://arxiv.org/abs/2310.02065
Autor:
Grossman, Aiden, Paehler, Ludger, Parasyris, Konstantinos, Ben-Nun, Tal, Hegna, Jacob, Moses, William, Diaz, Jose M Monsalve, Trofin, Mircea, Doerfert, Johannes
Code is increasingly becoming a core data modality of modern machine learning research impacting not only the way we write code with conversational agents like OpenAI's ChatGPT, Google's Bard, or Anthropic's Claude, the way we translate code from one
Externí odkaz:
http://arxiv.org/abs/2309.15432
GPUs are increasingly adopted for large-scale database processing, where data accesses represent the major part of the computation. If the data accesses are irregular, like hash table accesses or random sampling, the GPU performance can suffer. Espec
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A79459
https://tud.qucosa.de/api/qucosa%3A79459/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A79459/attachment/ATT-0/
Autor:
Bazinska, Julia, Ivanov, Andrei, Ben-Nun, Tal, Dryden, Nikoli, Besta, Maciej, Shen, Siyuan, Hoefler, Torsten
Graph Neural Networks (GNNs) are a powerful tool for handling structured graph data and addressing tasks such as node classification, graph classification, and clustering. However, the sparse nature of GNN computation poses new challenges for perform
Externí odkaz:
http://arxiv.org/abs/2308.12093
Execution graphs of parallel loop programs exhibit a nested, repeating structure. We show how such graphs that are the result of nested repetition can be represented by succinct parametric structures. This parametric graph template representation all
Externí odkaz:
http://arxiv.org/abs/2307.08420
Autor:
Schaad, Philipp, Schneider, Timo, Ben-Nun, Tal, Calotoiu, Alexandru, Ziogas, Alexandros Nikolaos, Hoefler, Torsten
The current hardware landscape and application scale is driving performance engineers towards writing bespoke optimizations. Verifying such optimizations, and generating minimal failing cases, is important for robustness in the face of changing progr
Externí odkaz:
http://arxiv.org/abs/2306.16178
With the rise of specialized hardware and new programming languages, code optimization has shifted its focus towards promoting data locality. Most production-grade compilers adopt a control-centric mindset - instruction-driven optimization augmented
Externí odkaz:
http://arxiv.org/abs/2306.00366
As deep learning models grow, sparsity is becoming an increasingly critical component of deep neural networks, enabling improved performance and reduced storage. However, existing frameworks offer poor support for sparsity. Specialized sparsity engin
Externí odkaz:
http://arxiv.org/abs/2304.07613