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UID:submissions.supercomputing.org_SC24_sess521@linklings.com
SUMMARY:Doctoral Showcase II Presentations
DESCRIPTION:Designing Efficient Data Reduction Approaches for Multi-Resolu
 tion Simulations on HPC Systems\n\nAs supercomputers advance towards exasc
 ale capabilities, computational intensity increases significantly, and the
  volume of data requiring storage and transmission experiences exponential
  growth. Multi-resolution methods, such as Adaptive Mesh Refinement (AMR),
  have emerged as an effective solution ...\n\n\nDaoce Wang (Indiana Univer
 sity)\n---------------------\nData Layout Optimizations for Tensor Applica
 tions\n\nThe performance of tensor applications is often bottlenecked by d
 ata movement across the memory subsystem. This dissertation contributes do
 main-specific programming frameworks (compilers and runtime systems) that 
 optimize data movement in tensor applications. We develop novel execution 
 reordering an...\n\n\nMahesh Lakshminarasimhan (University of Utah)\n-----
 ----------------\nEffects of Lossy Compression Data on Machine Learning Mo
 dels\n\nMachine learning is a fundamental tool that is incorporated in fie
 lds across academia and industry. Due to the large amounts of data needed 
 for training machine learning models, compression is utilized because it r
 educes the data footprint playing a critical role in storage. Machine lear
 ning involve...\n\n\nMax Faykus (Clemson University)\n--------------------
 -\nFFT-Based Spherical Harmonics and Radial Transforms on GPU\n\nModern hi
 gh-performance computing clusters switch to the GPUs, as opposed to CPUs, 
 as the source of their computational power. GPUs are tailored for data-par
 allel algorithms where multiple cores perform the same operations on diffe
 rent memory locations. However, making CPU code run within GPU constr...\n
 \n\nDmitrii Tolmachev (ETH Zürich)\n---------------------\nQ-NFSO: Explori
 ng Quantum Applications, Noise Management, Fault Injection, Resource Sched
 uling and Optimization in the NISQ Era\n\nQuantum computing has achieved s
 ignificant milestones in recent years, underscoring its potential benefits
  for NP-hard applications both currently and in the future. Despite these 
 advancements, contemporary quantum computers are hindered by noise and a l
 imited number of qubits. These limitations pos...\n\n\nBetis Baheri (Kent 
 State University)\n\nRegistration Category: Tech Program Reg Pass\n\nSessi
 on Chair: Will Killian (NVIDIA Corporation)
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