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DTSTAMP:20181221T160904Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181115T083000
DTEND;TZID=America/Chicago:20181115T170000
UID:submissions.supercomputing.org_SC18_sess324_post236@linklings.com
SUMMARY:MATEDOR: MAtrix, TEnsor, and Deep-Learning Optimized Routines
DESCRIPTION:Poster\nTech Program Reg Pass, Exhibits Reg Pass\n\nMATEDOR: M
 Atrix, TEnsor, and Deep-Learning Optimized Routines\n\nAbdelfattah, Dongar
 ra, Tomov, Yamazaki, Haidar\n\nThe MAtrix, TEnsor, and Deep-learning Optim
 ized Routines (MATEDOR) project develops software technologies and standar
 d APIs, along with a sustainable and portable library, for large-scale com
 putations that can be broken down into very small matrix or tensor computa
 tions. The main target of MATEDOR is to accelerate applications from impor
 tant fields that fit this profile, including deep learning, data mining, a
 strophysics, image and signal processing, hydrodynamics, and more.\n\nMATE
 DOR is a high-performance numerical library for batched linear algebra sub
 routines autotuned for modern processor architectures and system designs. 
 The MATEDOR library includes LAPACK-compliant routines that target many sm
 all dense problems, tensor, and application-specific operations, e.g., for
  deep-learning. These routines are constructed as much as possible out of 
 calls to batch BLAS routines and their look-alikes required in sparse comp
 utation context.
URL:https://sc18.supercomputing.org/presentation/?id=post236&sess=sess324
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