A minisymposium is a two-hour session on a topic of current importance in computational science that showcases research related to domain science, applied mathematics, computer science and software engineering, and is an ideal platform for promoting interdisciplinary communication.
Forty-eight minisymposia have been accepted for PASC18. Typically, the format of a minisymposium is four 30-minute presentations, although in some cases the final 30-minute slot may consist of a panel discussion or open discussion forum.
The focus will be on aspects concerning (i) capability computing: how to scale to several hundreds/thousands of compute nodes, including the use of communication optimal algorithms and asynchronous communication; (ii) performance portability: how to address the growing diversity in hardware on a compute node, including generic software design, and auto-tuning; and (iii) co-design in these projects: how to engage with vendors to optimally exploit current hardware, and to provide feedback that has or will influence next-generation hardware.
Topics include: side by side comparisons of multi-core, many core, and GPU compute nodes; optimization techniques for flops, memory bandwidth, or network performance; JIT compilation of machine specific kernels; programming approaches such as the use of domain specific languages (DSLs), remote memory access (RMA), task based programming.
In this session we bring together leading young researchers in the field to present alternative approaches to the computation of equilibria in dynamic stochastic models with heterogeneous agents and/or with financial frictions. Three of the papers directly propose new methods for the solution of models with a continuum of ex post heterogeneous agents.
One way to realise these opportunities is to use machine learning approaches. As machine learning in weather and climate is a relatively new topic this minisymposium introduces the audience to how machine learning could be used in weather and climate and outlines its implications in terms of computing costs. To ground the ideas in concrete examples it also illustrates the use of machine learning in the weather and climate domain with practical examples.
Although there has been great progress in this field and computational power has dramatically increased over the years, many important MIPs remain intractable and the use of massive parallelization appears to be a promising means to address this great need. However, many challenges lie ahead. This minisymposium will elucidate some of these challenges, while highlighting progress in this field. It includes a round table discussion with Michael Chan, Sharlee Climer, Daniel Jacobson, Sarah Powers, and Daniel Rehfeldt, and is open to conference participants. The goal of the discussions will be to explore and integrate high-performance expertise with domain-specific insights with an aim to identify strategies that may resolve these pressing challenges.