[hpc-announce] Call for Participation: Open Day at the Joint Laboratory on Extreme Scale Computing Virtual Workshop

[George Bosilca]

Dear HPC community,



We are delighted to announce the OpenDay of the 12th workshop of the
Joint-Laboratory on Extreme Scale Computing (JLESC): February 26, starting
@ 8:00AM EST via Zoom. Unlike the rest of the workshop, the participation
in the OpenDay is open to any attendee without registration or registration
fees.



The OpenDay is one of the 3 days of the JLESC workshop and features 3
invited talks from leaders in the field presented along with 3 success
stories from JLESC teams. The OpenDay invited speakers are Prof. Satoshi
Matsuoka (Riken), Dr. Lois Curfman McInnes (ANL) and Prof. Torsten Hoefler
(ETH):

   - Invited talk 1: Fugaku: the first 'Exascale' supercomputer

Prof. Satoshi Matsuoka, Riken CCS

   - Invited talk 2: How a Community Software Ecosystem Perspective Helps
   to Advance Science Goals in the Exascale Computing Project

Dr. Lois Curfman McInnes, Argonne National Laboratory

   - Invited talk 3: High-Performance Deep Learning

Prof. Torsten Hoefler, ETH <http://www.ethz.ch/> Zurich



Please see below for the abstracts and bios of the invited talks.



The timing of the OpenDay has been selected to accommodate a simultaneous
participation from America, Europe, and Asia. The schedule and virtual
conferencing link to attend the OpenDay will be published on:
http://icl.utk.edu/jlesc12/



About the JLESC:



The purpose of the JLESC is to be an international, virtual organization
whose goal is to enhance the ability of member organizations and
investigators to make the bridge between Petascale and Extreme computing.
The founding partners of the JLESC are INRIA and UIUC. Further members are
ANL, BSC, JSC and R-CCS. UTK is an associate member.

JLESC involves computer scientists, engineers and scientists from other
disciplines as well as from industry, to ensure that the research
facilitated by the Laboratory addresses science and engineering's most
critical needs and takes advantage of the continuing evolution of computing
technologies. Find out more at https://jlesc.github.io/.



The JLESC community is looking forward to welcoming you at the 12th JLESC
workshop OpenDay!





Invited talks:

Invited talk 1: Fugaku: the first 'Exascale' supercomputer

Prof. Satoshi Matsuoka, Riken CCS

Fugaku is the first ‘exascale’ supercomputer of the world, not due to its
peak double precision flops, but rather, its demonstrated performance in
real applications that were expected of exascale machines on their
conceptions 10 years ago, as well as reaching actual exaflops in new breed
of benchmarks such as HPL-AI. But the importance of Fugaku is “applications
first” philosophy under which it was developed, and its resulting mission
to be the centerpiece for rapid realization of the so-called Japanese
‘Society 5.0’ as defined by the Japanese S&T national policy. As such,
Fugaku’s immense power is directly applicable not only to traditional
scientific simulation applications, but can be a target of Society 5.0
applications that encompasses conversion of HPC & AI & Big Data as well as
Cyber (IDC & Network) vs. Physical (IoT) space, with immediate societal
impact with its technologies utilized as Cloud resources. In fact, Fugaku
is already in partial operation a year ahead of schedule, primarily to
obtain early Society 5.0 results including combatting COVID-19 as well as
resolving other important societal issues and also go into full production
in moments time.



Bio

Satoshi Matsuoka from April 2018 has become the director of Riken CCS, the
top-tier HPC center that represents HPC in Japan, developing and hosting
Japan’s tier-one ‘Fugaku’ supercomputer which has become the fastest
supercomputer in the world in all four major supercomputer rankings, along
with multitudes of ongoing cutting edge HPC research being conducted,
including investigating Post-Moore era computing.

He had been a Full Professor at the Global Scientific Information and
Computing Center (GSIC), the Tokyo Institute of Technology since 2000, and
the director of the joint AIST-Tokyo Tech. Real World Big Data Computing
Open Innovation Laboratory (RWBC-OIL) since 2017, and became a Specially
Appointed Professor at Tokyo Tech in 2018 along with his directorship at
R-CCS.

He has been the leader of the TSUBAME series of supercomputers that have
won many accolades such as world #1 in power-efficient computing. He also
leads various major supercomputing research projects in areas such as
parallel algorithms and programming, resilience, green computing, and
convergence of big data/AI with HPC.

He has written over 500 articles according to Google Scholar, and chaired
numerous ACM/IEEE conferences, including the Program Chair at the ACM/IEEE
Supercomputing Conference (SC13) in 2013. He is a Fellow of the ACM and
European ISC, and has won many awards, including the JSPS Prize from the
Japan Society for Promotion of Science in 2006, presented by his Highness
Prince Akishino; the ACM Gordon Bell Prize in 2011; the Commendation for
Science and Technology by the Minister of Education, Culture, Sports,
Science and Technology in 2012; the 2014 IEEE-CS Sidney Fernbach

Memorial Award, the highest prestige in the field of HPC; HPDC 2018
Achievement Award from ACM; and recently SC Asia 2019 HPC Leadership Award.



Invited talk 2: How a Community Software Ecosystem Perspective Helps to
Advance Science Goals in the Exascale Computing Project



Dr. Lois Curfman McInnes, Argonne National Laboratory



Teams in the U.S. Exascale Computing Project (ECP) are working toward
scientific advances on forthcoming exascale platforms, across a diverse
suite of applications in chemistry, materials, energy, Earth and space
science, data analytics, optimization, artificial intelligence, and
national security. In turn, these applications build on software
components, including programming models and runtimes, mathematical
libraries, data and visualization packages, and development tools that
comprise the Extreme-scale Scientific Software Stack (E4S).  E4S represents
a portfolio-driven effort to collect, test, and deliver the latest in
reusable open-source HPC software products, as driven by the common needs
of applications. E4S establishes product quality expectations and provides
a portal as a starting point for access to product documentation. This
presentation will discuss early experiences with how this software
ecosystem approach delivers the latest advances from ECP software
technology projects to applications, thereby helping to overcome software
collaboration challenges across distributed aggregate teams. A key lesson
learned is the need for close collaboration between teams developing
applications and reusable software technologies, as well as the need for
crosscutting strategies to increase developer productivity and software
sustainability, thereby mitigating technical risks by building a firmer
foundation for reproducible, sustainable science.



Bio

Lois Curfman McInnes is a senior computational scientist in the Mathematics
and Computer Science Division of Argonne National Laboratory. Her work
focuses on high-performance computational science and engineering, with
emphasis on scalable numerical libraries and community collaboration toward
productive and sustainable software ecosystems.  She serves as Deputy
Director of Software Technology in the DOE Exascale Computing Project.  She
also co-leads the IDEAS project, whose members are partnering with the
community to improve software productivity and sustainability as a key
aspect of advancing overall scientific productivity.  Lois has developed
numerical algorithms and software in the PETSc/TAO libraries. She is a SIAM
Fellow. She won the 2015 SIAM/ACM Prize in CSE and received an R&D 100
Award in 2009 (with collaborators); she also won an E.O. Lawrence Award in
2011 for outstanding contributions in research and development supporting
DOE and its missions.



Invited talk 3: High-Performance Deep Learning

Prof. Torsten Hoefler, ETH <http://www.ethz.ch/> Zurich

Abstract: Deep Learning is as computationally expensive as the most
challenging scientific computing applications. In this talk, we outline the
biggest challenges in training deep learning workloads and show how HPC
techniques can be used to improve the performance of training workloads. We
focus on model sparsity in the training process. This will be even more
important once the scientific computing community uses deep learning in
their workflows.

Bio

Torsten Hoefler directs the Scalable Parallel Computing Laboratory (SPCL)
<http://spcl.inf.ethz.ch/> at D-INFK <https://inf.ethz.ch/> ETH Zurich
<http://www.ethz.ch/>. He received his PhD degree in 2007 at Indiana
University <http://www.indiana.edu/> and started his first professor
appointment in 2011 at the University of Illinois at Urbana-Champaign
<http://www.uiuc.edu/>.

Torsten has served as the lead for performance modeling and analysis in the
US NSF Blue Waters project at NCSA/UIUC. Since 2013, he is professor of
computer science at ETH Zurich and has held visiting positions at Argonne
National Laboratories, Sandia National Laboratories, and Microsoft Research
Redmond (Station Q).

Dr. Hoefler's research aims at understanding the performance of parallel
computing systems ranging from parallel computer architecture through
parallel programming to parallel algorithms. He is also active in the
application areas of Weather and Climate simulations as well as Machine
Learning with a focus on Distributed Deep Learning. In those areas, he has
coordinated tens of funded projects and an ERC Starting Grant on
Data-Centric Parallel Programming.

He has been chair of the Hot Interconnects conference and technical program
chair of the Supercomputing and ACM PASC conferences. He is associate
editor of the IEEE Transactions of Parallel and Distributed Computing
(TPDS) and the Parallel Computing Journal (PARCO) and a key member of the
Message Passing Interface (MPI) Forum.

He has published more than 200 papers
<https://htor.inf.ethz.ch/publications> in peer-reviewed international
conferences and journals and co-authored the latest versions of the MPI
specification. He has received best paper awards at the ACM/IEEE
Supercomputing Conference in 2010, 2013, and 2014 (SC10, SC13, SC14),
EuroMPI 2013, IPDPS'15, ACM HPDC'15 and HPDC'16, ACM OOPSLA'16, and other
conferences. Torsten received ETH Zurich's Latsis Prize in 2015, the SIAM
SIAG/Supercomputing Junior Scientist Prize in 2012, the IEEE TCSC Young
Achievers in Scalable Computing Award in 2013, the Young Alumni Award 2014
from Indiana University, and the best student award 2005 of the Chemnitz
University of Technology. Torsten was elected into the first steering
committee of ACM's SIGHPC in 2013 and he was re-elected in 2016.