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DTSTAMP:20260422T000711Z
LOCATION:DEF Concourse
DTSTART;TZID=America/Denver:20231114T100000
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UID:submissions.supercomputing.org_SC23_sess291_rpost140@linklings.com
SUMMARY:GPU-Accelerated Dense Covariance Matrix Generation for Spatial Sta
 tistics Applications
DESCRIPTION:Zipei Geng, Sameh Abdulah, Hatem Ltaief, Ying Sun, Marc Genton
 , and David Keyes (King Abdullah University of Science and Technology (KAU
 ST))\n\nLarge-scale parallel computing is crucial in Gaussian regressions 
 to reduce the complexity of spatial statistics applications. The log-likel
 ihood function is utilized to evaluate the Gaussian model for a set of mea
 surements in N geographical locations. Several studies have shown a utiliz
 ation of modern hardware to scale the log-likelihood function for handling
  large numbers of locations. ExaGeoStat is an example of software that all
 ows parallel statistical parameter estimation from the log-likelihood func
 tion. However, generating a covariance matrix is mandatory and challenging
  when estimating the log-likelihood function. In ExaGeoStat, the generatio
 n process was performed on CPU hardware due to missing math functions in C
 UDA libraries, e.g., the modified Bessel function of the second kind. This
  study aims to optimize the generation process using GPU with two proposed
  generation schemes: pure GPU and hybrid. Our implementations demonstrate 
 up to 6X speedup with pure GPU and up to 1.5X speedup with the hybrid sche
 me.\n\nTag: Artificial Intelligence/Machine Learning, Architecture and Net
 works, Heterogeneous Computing, I/O and File Systems, Performance Measurem
 ent, Modeling, and Tools, Post-Moore Computing, Programming Frameworks and
  System Software, Quantum Computing\n\nRegistration Category: Tech Program
  Reg Pass, Exhibits Reg Pass\n\n
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