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DTSTAMP:20230831T095755Z
LOCATION:Sanada I
DTSTART;TZID=Europe/Stockholm:20230628T140000
DTEND;TZID=Europe/Stockholm:20230628T160000
UID:submissions.pasc-conference.org_PASC23_sess138@linklings.com
SUMMARY:MS6G - Green Computing Architectures and Tools for Scientific Comp
 uting
DESCRIPTION:Minisymposium\n\nHigh-Performance Computing (HPC) data centers
  are facing a significant challenge in terms of energy efficiency as they 
 strive to operate within stringent power budgets and reduce environmental 
 pollution. The computing facilities that will be built for the Square Kilo
 metre Array Telescope (SKA) are a prime example of this challenge, as they
  must process massive quantities of data from thousands of antennas with a
  limited power budget. In addition, new hardware technologies with increas
 ing power footprints result in power-hungry supercomputers. In light of th
 is, new strategies, tools, and architectures are being explored to address
  these issues. This mini-symposium aims to gather experts from diverse res
 earch environments to present solutions and methods being investigated in 
 state-of-the-art research. The mini-symposium will dive into the energy ef
 ficiency and carbon footprint of SPH-EXA, a smoothed particle hydrodynamic
 s. Then, it will focus on an improved Kernel Tuner, a generic autotuning t
 ool for GPU applications that takes into account energy efficiency to find
  the best operational frequency for GPUs for SKA workloads. Finally, the m
 ini-symposium will address lower lever optimization by presenting "exotic"
  solutions such as approximate computing design of Artificial Intelligence
  accelerators and domain-specific accelerators for biomedical workloads.\n
 \nGoing Green: Optimizing GPUs for Energy Efficiency through Model-Steered
  Auto-Tuning\n\nGraphics Processing Units (GPUs) have revolutionized the c
 omputing landscape over the past decade. However, the growing energy deman
 ds of data centres and computing facilities equipped with GPUs come with s
 ignificant capital and environmental costs. The energy consumption of GPU 
 applications greatly...\n\n\nBram Veenboer (ASTRON) and Stefano Corda (EPF
 L)\n---------------------\nAccurately Measuring Energy Consumption of Larg
 e Cosmological Simulations\n\nSPH-EXA is a highly scalable and extendable 
 simulation framework for astrophysical and cosmological simulations, codes
 igned by computational scientists and computer scientists to achieve scien
 tific advances in astrophysics, cosmology, and high-performance computing.
  This talk discusses extending the...\n\n\nOsman Seckin Simsek (University
  of Basel), Jean-Guillaume Piccinali (ETH Zurich / CSCS), and Florina M. C
 iorba (University of Basel)\n---------------------\nDesigning Application-
 Specific Approximate Operators for Energy-Efficient AI Accelerators\n\nA p
 lethora of recent works has focused on the various optimization techniques
  to reduce machine learning (ML) models’ overall computational complexity 
 and memory footprints to implement them on resource-constrained embedded s
 ystems. These techniques mainly exploit the inherent error resilience...\n
 \n\nSalim Ullah, Siva Satyendra Sahoo, and Akash Kumar (TU Dresden)\n-----
 ----------------\nTradeoffs in Low-Power Accelerators Design for Large-Sca
 le Interferometers\n\nLarge-scale scientific infrastructures like SKAO—the
  world’s largest radio observatory for the coming decades—are generating m
 assive-scale data streams of multi-Tb/s to be processed using complex inte
 rferometry algorithms. Concretely, SKAO is expected to generate over 710 p
 etabytes...\n\n\nDenisa Constantinescu, Benoît Denkinger, Miguel Peon Quir
 os, and David Atienza (EPFL)\n\nDomain: Computer Science, Machine Learning
 , and Applied Mathematics 
\n\nSession Chair: Emma Tolley (EPFL)
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