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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:Application Performance Modeling via Tensor Completion\n\nPerf
 ormance tuning, software/hardware co-design, and job scheduling are among 
 the many tasks that rely on models to predict application performance. We 
 propose and evaluate low-rank tensor decomposition for modeling applicatio
 n performance. We discretize the input and configuration domains of an app
 ...\n\n\nEdward Hutter and Edgar Solomonik (University of Illinois)\n-----
 ----------------\nHigh-Performance and Programmable Attentional Graph Neur
 al Networks with Global Tensor Formulations\n\nGraph attention models (A-G
 NNs), a type of Graph Neural Networks (GNNs), have been shown to be more p
 owerful than simpler convolutional GNNs (C-GNNs). However, A-GNNs are more
  complex to program and difficult to scale. To address this, we develop a 
 novel mathematical formulation, based on tensors th...\n\n\nMaciej Besta (
 ETH Zurich - Swiss Federal Institute of Technology); Paweł Renc (AGH Unive
 rsity of Science and Technology, Krakow, Poland; Sano Centre for Computati
 onal Medicine, Krakow, Poland); Robert Gerstenberger (ETH Zurich - Swiss F
 ederal Institute of Technology); Paolo Sylos Labini (Free University of Bo
 zen-Bolzano, Italy; ETH Zurich - Swiss Federal Institute of Technology); A
 lexandros Ziogas, Tiancheng Chen, Lukas Gianinazzi, Florian Scheidl, Kalma
 n Szenes, Armon Carigiet, and Patrick Iff (ETH Zurich - Swiss Federal Inst
 itute of Technology); Grzegorz Kwasniewski (NextSilicon Inc); Raghavendra 
 Kanakagiri (University of Illinois); Chio Ge and Sammy Jaeger (ETH Zurich 
 - Swiss Federal Institute of Technology); Jarosław Wąs (AGH University of 
 Science and Technology, Krakow, Poland); Flavio Vella (University of Trent
 o); and Torsten Hoefler (ETH Zurich - Swiss Federal Institute of Technolog
 y)\n---------------------\nAutomatic Generation of Distributed-Memory Mapp
 ings for Tensor Computations\n\nWhile considerable research has been direc
 ted at automatic parallelization for shared-memory platforms, little progr
 ess has been made in automatic parallelization schemes for distributed-mem
 ory systems. We introduce an innovative approach to automatically produce 
 distributed-memory parallel code for...\n\n\nMartin Kong, Raneem Abu Yosef
 , and Atanas Rountev (Ohio State University) and P. Sadayappan (University
  of Utah)\n\nTag: Artificial Intelligence/Machine Learning, Compilers, Per
 formance Measurement, Modeling, and Tools, Performance Optimization, Progr
 amming Frameworks and System Software, Tensors\n\nRegistration Category: T
 ech Program Reg Pass\n\nReproducibility Badges: Artifact Available, Artifa
 ct Functional, Results Reproduced\n\nSession Chair: Kazem Cheshmi (McMaste
 r University, Ontario, Canada)
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