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DTSTART;TZID=America/Denver:20231114T133000
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UID:submissions.supercomputing.org_SC23_sess165@linklings.com
SUMMARY:Graph Frameworks and Databases
DESCRIPTION:MBFGraph: An SSD-Based External Graph System for Evolving Grap
 hs\n\nThe challenge of executing extensive graph analyses in-memory intens
 ifies with growing graph sizes. This has given rise to disk-based external
  graph analytics systems that prioritize cost-effective HDDs/SSDs over pri
 cier memory solutions. In response to this issue, our paper introduces and
  assesses ...\n\n\nChun-Yi Liu (Micron Technology Inc), Wonil Choi (Hanyan
 g University), and Soheil Khadirsharbiyani and Mahmut Kandemir (Pennsylvan
 ia State University)\n---------------------\nThe Graph Database Interface:
  Scaling Online Transactional and Analytical Graph Workloads to Hundreds o
 f Thousands of Cores\n\nGraph databases (GDBs) are crucial in academic and
  industry applications. The key challenges in developing GDBs are achievin
 g high performance, scalability, programmability, and portability. To tack
 le these challenges, we harness established practices from the HPC landsca
 pe to build a system that ou...\n\n\nMaciej Besta, Robert Gerstenberger, a
 nd Marc Fischer (ETH Zurich - Swiss Federal Institute of Technology); Mich
 ał Podstawski (TCL Eagle Lab, Warsaw University of Technology); Nils Blach
 , Berke Egeli, and Georgy Mitenkov (ETH Zurich - Swiss Federal Institute o
 f Technology); Wojciech Chlapek (ICM UW); Marek Michalewicz (Sano Centre f
 or Computational Medicine, Krakow, Poland); Hubert Niewiadomski (Cledar); 
 Jürgen Müller (BASF SE); and Torsten Hoefler (ETH Zurich - Swiss Federal I
 nstitute of Technology)\n---------------------\nGraph3PO: A Temporal Graph
  Data Processing Method for Latency QoS Guarantee in Object Cloud Storage 
 System\n\nObject cloud storage systems are deployed with diverse applicati
 ons that have varying latency service level objectives (SLOs), posting cha
 llenges for supporting quality of service with limited storage resources. 
 Existing methods provide prediction-based recommendations for dispatching 
 requests from ...\n\n\nWang Zhang, Zhan Shi, Ziyi Liao, and Yiling Li (Hua
 zhong University of Science and Technology (HUST)); Yu Du (Alibaba Group);
  and Yutong Wu, Fang Wang, and Dan Feng (Huazhong University of Science an
 d Technology (HUST))\n\nTag: Cloud Computing, Data Analysis, Visualization
 , and Storage, Graph Algorithms and Frameworks\n\nRegistration Category: T
 ech Program Reg Pass\n\nAward Finalist: Best Paper Finalist\n\nReproducibi
 lity Badges: Artifact Available, Artifact Functional, Results Reproduced\n
 \nSession Chair: Kasimir Gabert (Sandia National Laboratories)
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