Topic 14: High Performance and Scientific Applications


The availability of an abundance of computing resources worldwide has substantially impacted the way that research is nowadays conducted both in the industry and in the academy. The new ways of doing science, rooted on the unprecedented processing, communication and storage infrastructure that became available to researchers encompass activities such as computational modeling and simulation, processing of large amounts of data, often geographically spread, and the visualization of complex datasets. The constant technological advances that make computers faster and storage more plentiful, are not enough to cope with the increased demand generated by more accurate and complex modeling, and an ever increasing quantity of data being generated. There is thus a growing need for a range of high performance applications which can utilize parallel compute systems effectively, and which have efficient data I/O strategies.

This topic will highlight recent progress in the use of high-performance parallel and scientific computing, with an emphasis on successes, advances, and lessons learned in the development and implementation of novel scientific, engineering, and industrial applications. We welcome papers that describe new applications, as well as existing applications ported to new environments for increasing performance (e.g. from CPU to GPGPU), reduce cost (e.g. from dedicated servers to cloud computing infrastructures), better balance workloads (e.g. from local systems to federated ones), or to achieve other benefits.


  • Advances in science and engineering modeling and simulation of complex systems
  • Hybrid shared memory and message passing tools
  • New applications in non-traditional areas such as health care, social sciences, financial modeling, transportation, and economics
  • Large-scale data analysis in high-performance applications
  • Management of high data volumes
  • Scientific workflows for high-performance and scientific applications
  • Success and lessons learned in petascale computing and beyond

Topic Committee

Global Chair
Francisco Brasileiro, Univ. Federal Campina Grande, Brazil

Local Chair
Pedro Medeiros, Nova University of Lisbon, Portugal

Further Members
Gilles Fedak, University of Lyon, France
Walfredo Cirne, Google, USA
Adélia Sequeira, IST-University of Lisbon, Portugal