[hpc-announce] Scientific Programming Journal - Special Issue: Methodologies for Highly Scalable and Parallel Scientific Programming on High Performance Computing Platforms

Antonio J. Peña antonio.pena at bsc.es
Tue Sep 10 03:28:25 CDT 2019

Methodologies for Highly Scalable and Parallel Scientific Programming on 
High Performance Computing Platforms
Call for Papers


We are currently at a point where the gap between the scientific 
programming and the computing platforms, on which the scientific codes 
must be computed, is growing bigger and bigger. The importance of 
scientific programming for High Performance Computing (HPC) is 
increasing and has become as one of the foremost fields of research. 
This has raised many issues, such as computing architectures, memory, 
networking, and programming models. In turn, this forces us to adapt our 
codes, or implement new ones, to take advantage of the latest 
computational features.

This special issue focuses on these challenges, which arise when 
programming scientific codes over massively parallel HPC platforms 
composed of a high number of cores dealing with communication, 
programming, specialized architectures, load balancing, benchmarking, 
etc. We hope to provide a platform to discuss how modern scientific 
programming, which has important challenges and high computational 
requirements, can be efficiently mapped on current and upcoming 
high-performance platforms.

The aim of this special issue is to bridge the gap between the theory of 
scientific problems (machine learning, scientific simulations, image 
processing, computational fluid dynamics, bioinformatics, linear 
algebra, big data computing, etc.) and their implementation on HPC 
platforms by exploring, proposing, and evaluating new trends/directions 
in scientific programming. Authors are invited to submit original 
research and review articles. Works focused on emerging programming 
models and computer architectures are especially welcome.

Potential topics include but are not limited to the following:

    Scientific programming on specialized hardware (GPUs, Tensor Core, 
FPGAs, Heterogeneous Memory, etc.)
    Numerical modeling of scientific applications to increase 
performance (novel methodologies to achieve more scalability, less 
synchronization-communication, less memory occupancy, mixed-precision, etc.)
    Programming techniques for communication, synchronization, and load 
    Evaluation of scientific applications including scalability, 
benchmarking, performance, and numerical accuracy analysis
    Use of programming models for scientific applications (OpenMP, MPI, 
CUDA, etc.)
    Auto-tuning in scientific programming

Authors may submit their manuscripts through the Manuscript Tracking 
System at https://mts.hindawi.com/submit/journals/sp/mhsp.


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