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SPE-SI-HPC 2021 : Software: Practice and Experience Special Issue on: New Trends in High Performance Computing - Software Systems and Applications | |||||||||||||||
Link: https://onlinelibrary.wiley.com/pb-assets/assets/page/journal/1097024x/SPE-SI-HPC-1607014410373.pdf | |||||||||||||||
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Call For Papers | |||||||||||||||
Special Issue on:
New Trends in High Performance Computing: Software Systems and Applications Software: Practice and Experience (Wiley Press) https://onlinelibrary.wiley.com/pb-assets/assets/page/journal/1097024x/SPE-SI-HPC-1607014410373.pdf Submission due: May 31, 2021 High performance computing (HPC) offers the computing power to continuously support the world’s most important discoveries in various scientific and business domains such as chemistry, physics, biology, material science, drug discovery and financial investment risk analysis. The exascale computing era is soon coming. A number of exaflops-capable supercomputers are scheduled to be fully operational in the time frame of 2021 and 2022. Researchers from across the HPC community are developing software systems, tools, libraries, frameworks, application packages, and methods that can fully exploit these extremely powerful computing resources. Extreme-scale computing will enable the solution of vastly more accurate predictive models and the analysis of massive quantities of data, producing quantum advances in areas of science and technology that are essential to the scientific community. New computational approaches such as machine learning/deep learning have also been heavily explored in recent years and shown promising evidence for many problems that cannot be resolved by traditional computational simulation and engineering. Training deep neural networks with massive data is an extremely computing intensive task that heavily relies on the HPC power. The upcoming exascale computing era will be the essential basis for supporting new innovations in machine learning/deep learning based exploration and lead to new sciences in directions such as smart manufacturing, laboratory-automation, and automatic programming. While the hardware architecture can generate extreme computing power, renovation in software stack plays the essential role for effective performance delivery. The exascale and pre-exascale systems such as Aurora, Frontier, El Capitan and LUMI continue to move into the heterogeneous space, while the current fastest ARM-based system, Fugaku and the many core Sunway Taihulight type of systems are marching towards the heterogeneous space. With systems equipped with GPUs, ARM SVEs and many cores, there is a dire need for innovative software frameworks that can seamlessly migrate scientific code to these systems equipped with rich compute resources. We need innovation at different levels including compiler tools and techniques, performance analysis tools, novel abstractions at the programming model, redesign of application-level algorithms and so on. Furthermore, co-design of applications and low-level software frameworks can lead to more efficient use of the opportunities of exascale in many contexts. Topics of interests include but are not limited to: + HPC for AI and AI for HPC - AI/ML/DL performance optimizations at applications or system frameworks levels on HPC systems - Innovations of system, compiler, language, debugging and profiling tools for parallel AI/ML/DL - Performance modeling for AI/ML/DL applications - Visualization for parallel AI/ML/DL - AI/ML/DL driven approaches for scientific computing and performance optimization at scale + Programming Challenges in HPC - Exposing parallelism at different levels including application, architecture, system and algorithmic level - Enabling combinations of multi physics models and capabilities - Adopting novel algorithmic/mathematical approaches - Developing portable applications yet preserving performance - Exploring programming extensions and auto-vectorization capabilities - Exploration of application and system software co-design for addressing performance challenges - Programming language and compilation techniques for reducing energy and data movement + HPC Systems Software and Middleware - System software support for data management - Scalable data analytics - System software for resource management - Fault tolerance techniques and implementations - Synchronization and concurrency control + HPC Performance Measurement and Modeling - Scalable tools and instrumentation infrastructure for measurement, monitoring, and/or visualization of performance - Workload characterization and benchmarking techniques - Novel and broadly applicable performance optimization techniques + HPC Applications - Computational science and scalable methods - Using HPC for scalable multi-scale, multi-physics, and high-fidelity computational science - Structured and unstructured meshes using extreme scale computing - Computational biology, earth sciences, cosmology, fluid dynamics, plasma modeling among others Important Dates - Submission: May 31, 2021 - Notification: July 31, 2021 - Revision due: Aug 20, 2021 - Notification of final acceptance: Sept 20, 2021 - Final revised paper due: October 15, 2021 Special Issue Paper Submission Submission site: https://mc.manuscriptcentral.com/spe Please see detailed guidance at the online Call For Paper: https://onlinelibrary.wiley.com/pb-assets/assets/page/journal/1097024x/SPE-SI-HPC-1607014410373.pdf Guest Editors Sunita Chandrasekaran University of Delaware, USA Email: schandra@udel.edu Min Si Argonne National Laboratory, USA Email: msi@anl.gov Jidong Zhai Tsinghua University, China Email: zhaijidong@tsinghua.edu.cn Lena Oden University of Hagen, Germany Email: lena.oden@fernuni-hagen.de |
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