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DTSTAMP:20211207T055402Z
LOCATION:Second Floor Atrium
DTSTART;TZID=America/Chicago:20211116T083000
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UID:submissions.supercomputing.org_SC21_sess256@linklings.com
SUMMARY:Doctoral Showcase Posters Display
DESCRIPTION:Doctoral Showcase, Posters\n\nParallel Algorithms for Scalable
  Graph Mining :  Applications on Big Data and Machine Learning\n\nSattar, 
 Arifuzzaman\n\nParallel computing plays a crucial role in processing large
 -scale graph data. Complex network analysis is an exciting area of researc
 h for many applications in different scientific domains, e.g., sociology, 
 biology, online media, recommendation systems, and many more. Machine/Deep
  learning plays a s...\n\n---------------------\nPerformance Profiling, An
 alysis, and Optimization of GPU-Accelerated Applications\n\nZhou, Mellor-C
 rummey\n\nGPUs have emerged as a key component for accelerating applicatio
 ns in various domains, including deep learning, data analytics, and scient
 ific simulations. While GPUs provide superior compute power and higher mem
 ory bandwidth than CPUs, writing efficient GPU code to achieve maximum pos
 sible performa...\n\n---------------------\nIntelligent Job Scheduling for
  Next Generation HPC Systems\n\nFan, Lan, Papka\n\nBoth high performance c
 omputing (HPC) infrastructures and applications are undergoing significant
  changes. The emerging HPC applications are not only compute-intensive, bu
 t also data- and memory-intensive. To meet the diverse workload demands, n
 ew hardware components, such as GPU and burst buffer, a...\n\n------------
 ---------\nHolistic Performance Analysis and Optimization of Unified Virtu
 al Memory\n\nAllen, Ge\n\nHigh-performance computing systems have seen tre
 mendous growth in theoretical performance with the inclusion of Graphics P
 rocessing Units (GPUs) and other accelerators. The difficulty of programmi
 ng these systems has grown alongside the performance as programmers are re
 quired to manage separate prog...\n\n---------------------\nMemory-Centric
  3D Image Reconstruction with Hierarchical Communications on Multi-GPU Nod
 e Architecture\n\nHidayetoglu, Hwu\n\nX-ray computed tomography is a commo
 nly used technique for noninvasive imaging at synchrotron facilities. Iter
 ative tomographic reconstruction algorithms are often preferred for recove
 ring high quality 3D volumetric images from 2D X-ray images, however, thei
 r use has been limited to small/medium dat...\n\n---------------------\nTo
 ward Complete Emulation of Quantum Algorithms Using High-Performance Recon
 figurable Computing\n\nMahmud, El-Araby\n\nQuantum computing, at its curre
 nt nascent stage, has many critical problems that require investigation. F
 or instance, solving real-world problems require quantum circuits with lar
 ge depth which are difficult, often impossible to implement on a quantum c
 omputer due to decoherence noise. More specific...\n\n--------------------
 -\nAccelerating I/O for Traditional HPC and Modern ML Workloads on Emergin
 g HPC Systems\n\nChien, Markidis, Podobas\n\nIn recent years, HPC systems 
 have emerged as an attractive option to speed up large-scale Machine Learn
 ing (ML) workloads. HPC systems can tremendously improve learning speed wi
 th lots of GPUs and fast interconnect. However, ML workloads that are data
 -intensive differ significantly from traditional ...\n\n------------------
 ---\nTIGRA: A Tightly Integrated Generic RISC-V Accelerator Interface\n\nG
 reen, Smith\n\nField programmable gate array (FPGA) usage in HPC applicati
 ons is growing with the need for energy efficient and application specific
  accelerators. Currently, FPGAs are used to accelerate algorithms using Op
 enCL with communication over a CPU bus (loosely coupled accelerators) or b
 y modifying existin...\n\n---------------------\nProgram Transformation fo
 r Automatic GPU-Offloading with OpenMP\n\nMishra, Chapman, Malik\n\nThanks
  to its ability to manage large data parallelism with low power consumptio
 n, heterogeneous GPU computing has risen in the past decade. However, writ
 ing an application for GPUs is an intensive manual effort that may include
  re-engineering data structures, as well as modifying large regions of c..
 .\n\n---------------------\nOperator Splittings on GPUs\n\nKlein, Strzodka
 \n\nOperator splittings are a successful method for the solution of parabo
 lic partial differential equations. In this thesis, the same ideas are app
 lied for general sparse linear equation systems. For general graphs, which
  are induced by the sparse matrix of the equation system, a parallel segme
 ntation ...\n\n\nTag: In-Person Only\n\nRegistration Category: Tech Progra
 m Reg Pass, Exhibit Hall Only
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