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DTSTAMP:20191206T142523Z
LOCATION:Analog 1\, 2
DTSTART;TZID=Europe/Stockholm:20180626T110000
DTEND;TZID=Europe/Stockholm:20180626T123000
UID:isc_hpc_ISC High Performance 2018_sess110@linklings.com
SUMMARY:Research Paper Session -  AI & Machine Learning / HPC Algorithms
DESCRIPTION:Research Paper\n\nBayesian Optimization of HPC Systems for Ene
 rgy Efficiency\n\nMiyazaki, Sato, Shimizu\n\nEnergy efficiency is a crucia
 l factor in developing large supercomputers and cost-effective datacenters
 . However, tuning a system for energy efficiency is difficult because the 
 power and performance are conflicting demands. We applied Bayesian optimiz
 ation (BO) to tune a graphics processing unit (GP...\n\n------------------
 ---\nCombining HTM with RCU to Speed Up Graph Coloring on Multicore Platfo
 rms\n\nGiannoula, Goumas, Koziris\n\nAbstract. Graph algorithms are hard t
 o parallelize, as they exhibit\nvarying degrees of parallelism and perform
  irregular memory accesses.\nGraph coloring is a well studied problem, tha
 t colors the vertices of a\ngraph, such that no adjacent vertices have the
  same color. This is a necessity for a large...\n\n---------------------\n
 Distributed Deep Reinforcement Learning: Learn how to Play Atari Games in 
 21 Minutes\n\nGrel, Michalewski, Kaczmarek, Jędrych, Adamski...\n\nWe
  present a study in Distributed Deep Reinforcement Learning (DDRL) focused
  on scalability of a state-of-the-art Deep Reinforcement Learning algorith
 m known as Batch Asynchronous Advantage Actor-Critic (BA3C). We show that 
 using the Adam optimization algorithm with a batch size of up to 2048 is a
  v...\n\n\nPasses: Conference Pass
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