Special issue on High Performance and Distributed Computing in Astronomy and Astrophysics
摘要截稿:
全文截稿: 2018-04-30
影响因子: 1.854
期刊难度:
CCF分类: 无
中科院JCR分区:
• 大类 : 物理与天体物理 - 4区
• 小类 : 天文与天体物理 - 4区
• 小类 : 计算机:跨学科应用 - 4区
Overview
High Performance Computing is an essential tool in almost all branches of science today and astronomy and astrophysics are no exceptions. For example, large scale computations of complex systems of equations that are either intractable with analytic techniques or require a huge amount of time are one of the key reasons why astrophysicists need HPC. Furthermore, the exponential increase in the amount of data that will be gathered by future experiments such as LSST, CTA, SKA (more data than the Internet?) is posing a serious challenge to astronomers’ ability to analyze them. The consequence is even increasing interest in parallelizing algorithms and analysis techniques or porting legacy applications to clusters and supercomputers, e.g. those for cosmological simulations, and the use of distributed technologies, Cloud computing, GPUs and other massively parallel architectures as Xeon Phi for the management and processing of large datasets/big data.
The aim of this special issue is to present the latest efforts in the development of parallel/distributed systems and algorithms and to foster the integration of researchers interested in HPC, astronomy and astrophysics. The main focus must be on the results achievable (e.g. acceleration of analytics and/or simulation) more than on the mere technology adoption, but also papers presenting algorithmic improvements are well accepted.
We seek original, high quality papers related to (but not limited to) one or more of the following topics:
- Cosmological simulations
- Modelling protoplanetary disks and planetary systems
- 3D simulations of supernova explosions and stellar mergers
- Big Data challenges: access, (real-time) reduction, analysis, modelling, data mining
- Simulation of plasma phenomena (solar and stellar physics; accretion processes; shocks and jets at all scales)