[hpc-announce] Extension of submission deadline for HCW'22

[Friese, Ryan D]

Dear all

The paper submission deadline for has been extended for HCW (Heterogeneity in Computing Workshop --http://hcw.oucreate.com/ ) to Feb 18,
2022. Please see below for more details.

Thanks!

Ryan Friese
General Chair, HCW 2022


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HCW 2022 Call for Papers

In conjunction with IPDPS 2022, May 30, 2022, Lyon, France
Sponsored by the IEEE Computer Society
through the Technical Committee on Parallel Processing (TCPP)

Most modern computing systems are heterogeneous, either for organic
reasons because components grew independently, as it is the case in
desktop grids, or by design to leverage the strength of specific
hardware, as it is the case in accelerated systems. In any case, all
computing systems have some form of hardware or software heterogeneity
that must be managed, leveraged, understood, and exploited. The
Heterogeneity in Computing Workshop (HCW) is a venue to discuss and
innovate in all theoretical and practical aspects of heterogeneous
computing: design, programmability, efficient utilization, algorithms,
modeling, applications, etc. The 2022 HCW is the 31st annual gathering

of this workshop.


TOPICS
Topics of interest include but are not limited to the following areas:

!!! SPECIAL TOPIC 1 !!!  Heterogenous Integration of Quantum
Computing: Future of computers will lead to a system that consists of
both classical and Quantum computers for accelerated performance.
Design, exploration, and analysis of architectures and software
frameworks that enables and needs the heterogeneous integration of
classical computing and Quantum computing. (e.g., heterogeneous
quantum computers, error correction, heterogeneous applications that
use both classical and quantum logic, benchmarks for heterogeneous
quantum computers.)

!!! SPECIAL TOPIC 2 !!!  Heterogeneity and Interoperability in SW &
Data systems:  Design, exploration, and analysis of architectures and
software frameworks for interoperability in SW and Data systems.
(e.g., semantic based frameworks, interoperability for heterogeneous
IoT systems, model-driven frameworks.)

Heterogeneous multicore systems and architectures: Design,
exploration, and experimental analysis of heterogeneous computing
systems such as GPGPUs, heterogeneous systems-on-chip (SoC),
accelerator systems (e.g., Intel Xeon Phi, AI chips such as Google's
TPUs), FPGAs, big.LITTLE, and application-specific architectures.
Heterogeneous parallel and distributed systems: Design and analysis of
computing grids, cloud systems, hybrid clusters, datacenters,
geo-distributed computing systems, and supercomputers.
Deep-memory hierarchies: Design and analysis of memory hierarchies
with SRAM, DRAM, Flash/SSD, and HDD technologies; NUMA architectures;
cache coherence strategies; novel memory systems such as phase-change
RAM, magnetic (e.g., STT) RAM, 3D Xpoint/crossbars, and memristors.
On-chip, off-chip and heterogeneous network architectures:
Network-on-chip (NoC) architectures and protocols for heterogeneous
multicore applications; energy, latency, reliability, and security
optimizations for NoCs; off-chip (chip-to-chip) network architectures
and optimizations; heterogeneous networks (combination of NoC and
off-chip) design, evaluation, and optimizations; large scale parallel
and distributed heterogeneous network design, evaluation, and
optimizations.
Programming models and tools: Programming paradigms and tools for
heterogeneous systems; middleware and runtime systems;
performance-abstraction tradeoff; interoperability of heterogeneous
software environments; workflows; dataflows. Resource management and
algorithms for heterogeneous systems: Parallel algorithms for solving
problems on heterogeneous systems (e.g., multicores, hybrid clusters,
grids or clouds); strategies for scheduling and allocation on
heterogeneous 2D and 3D multicore architectures; static and dynamic
scheduling and resource management for large-scale and parallel
heterogeneous systems. Modeling, characterization, and optimizations:
Performance models and their use in the design of parallel and
distributed algorithms for heterogeneous platforms, characterizations
and optimizations for improving the time to solve a problem (e.g.,
throughput, latency, runtime), modeling and optimizing electric
consumption (e.g., power, energy); modeling for failure management
(e.g., fault tolerance, recovery, reliability); modeling for security
in heterogeneous platforms.
Applications on heterogeneous systems: Case studies; confluence of Big
Data systems and heterogeneous systems; data-intensive computing; deep
learning; scientific computing.

IMPORTANT DATES
Paper submission: ~February 18, 2022


Author notification: ~March 1, 2022
Camera Ready: ~March 14, 2022

PAPER SUBMISSIONS
Papers are to be submitted through https://ssl.linklings.net/conferences/ipdps/
Submissions for the Special Topic Session on DSAs: Please add (Special
Topic Submission) to your paper title during the submission process.
Papers submitted to HCW 2021 should not have been previously published
or be under review for a different workshop, conference, or journal.
It is required that all accepted papers will be presented at the
workshop by one of the authors.

WORKSHOP ORGANIZATION
General Chair: Ryan D. Friese, Pacific Northwest National Laboratory, USA

Program Chair: Jong-Kook Kim, Korea University, Korea

Steering Committee:
Behrooz Shirazi, Washington State University, USA (Chair)
H. J. Siegel, Colorado State University, USA (Past Chair)
John Antonio, University of Oklahoma, USA
David Bader, New Jersey Institute of Technology, USA
Anne Benoit, École Normale Supérieure de Lyon, France
Jack Dongarra, University of Tennessee, USA
Alexey Lastovetsky, University College Dublin, UK
Sudeep Pasricha, Colorado State University, USA
Viktor K. Prasanna, University of Southern California, USA
Yves Robert, École Normale Supérieure de Lyon, France
Erik Saule, University of North Carolina at Charlotte, USA
Uwe Schwiegelshohn, TU Dortmund University, Germany

Technical Program Committee:
Jong-Kook Kim, Korea University, Korea (TPC Chair)
Mohsen Amini, University of Louisiana Lafayette, USA
David Bader, New Jersey Institute of Technology, USA
Burcu Mutlu, Pacific Northwest National Laboratory, USA
Louis-Claude Canon, Université de Franche-Comté, France
Daniel Cordeiro, University of São Paulo, Brazil
Matthias Diener, University of Illinois at Urbana-Champaign, USA
Diana Göhringer, Technische Universität Dresden, Germany
Mahzabeen Islam, AMD, USA
Krishna Kavi, University of North Texas, USA
Georgios Keramidas, Aristotle University, Greece
Joongheon Kim, Korea University, Korea
Sung Il Kim, SK Hynix, Korea
Hyoukjun Kwon, Facebook, USA
Alexey Lastovetsky, University College Dublin, Ireland
Hatem Ltaief, KAUST, Saudi Arabia
Tirthak Patel, Northeastern University, USA
Dana Petcu, West University of Timisoara, Romania
Laercio Lima Pilla, CNRS, France
Sridhar Radhakrishnan, University of Oklahoma, USA
Srishti Srivastava, University of Southern Indiana, USA
Achim Streit, Karlsruhe Institute of Technology, Germany
Samuel Thibault, LaBRI, Université Bordeaux, France
Cheng Wang, Microsoft, USA
__________________________________________
Ryan D. Friese Ph.D.
Pacific Northwest National Laboratory
High Performance Computing Group

902 Battelle Boulevard
Richland, WA  99352 USA
Tel:  509-375-2903
ryan.friese at pnnl.gov<mailto:ryan.friese at pnnl.gov>