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DTSTAMP:20240116T190007Z
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DTSTART;TZID=America/Denver:20231115T153000
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UID:submissions.supercomputing.org_SC23_sess174@linklings.com
SUMMARY:Tensor Computation
DESCRIPTION:Paper\n\nAutomatic Generation of Distributed-Memory Mappings f
 or Tensor Computations\n\nWhile considerable research has been directed at
  automatic parallelization for shared-memory platforms, little progress ha
 s been made in automatic parallelization schemes for distributed-memory sy
 stems. We introduce an innovative approach to automatically produce distri
 buted-memory parallel code for...\n\n\nMartin Kong, Raneem Abu Yosef, and 
 Atanas Rountev (Ohio State University) and P. Sadayappan (University of Ut
 ah)\n---------------------\nApplication Performance Modeling via Tensor Co
 mpletion\n\nPerformance tuning, software/hardware co-design, and job sched
 uling are among the many tasks that rely on models to predict application 
 performance. We propose and evaluate low-rank tensor decomposition for mod
 eling application performance. We discretize the input and configuration d
 omains of an app...\n\n\nEdward Hutter and Edgar Solomonik (University of 
 Illinois)\n---------------------\nHigh-Performance and Programmable Attent
 ional Graph Neural Networks with Global Tensor Formulations\n\nGraph atten
 tion models (A-GNNs), a type of Graph Neural Networks (GNNs), have been sh
 own to be more powerful than simpler convolutional GNNs (C-GNNs). However,
  A-GNNs are more complex to program and difficult to scale. To address thi
 s, we develop a novel mathematical formulation, based on tensors th...\n\n
 \nMaciej Besta (ETH Zurich - Swiss Federal Institute of Technology); Paweł
  Renc (AGH University of Science and Technology, Krakow, Poland; Sano Cent
 re for Computational Medicine, Krakow, Poland); Robert Gerstenberger (ETH 
 Zurich - Swiss Federal Institute of Technology); Paolo Sylos Labini (Free 
 University of Bozen-Bolzano, Italy; ETH Zurich - Swiss Federal Institute o
 f Technology); Alexandros Ziogas, Tiancheng Chen, Lukas Gianinazzi, Floria
 n Scheidl, Kalman Szenes, Armon Carigiet, and Patrick Iff (ETH Zurich - Sw
 iss Federal Institute of Technology); Grzegorz Kwasniewski (NextSilicon In
 c); Raghavendra Kanakagiri (University of Illinois); Chio Ge and Sammy Jae
 ger (ETH Zurich - Swiss Federal Institute of Technology); Jarosław Wąs (AG
 H University of Science and Technology, Krakow, Poland); Flavio Vella (Uni
 versity of Trento); and Torsten Hoefler (ETH Zurich - Swiss Federal Instit
 ute of Technology)\n\nTag: Artificial Intelligence/Machine Learning, Compi
 lers, Performance Measurement, Modeling, and Tools, Performance Optimizati
 on, Programming Frameworks and System Software, Tensors\n\nRegistration Ca
 tegory: Tech Program Reg Pass\n\nSession Chair: Kazem Cheshmi (McMaster Un
 iversity)
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