Action Objectives and Grant Periods Goals

Action Aim – Primary Objective


The Action MoU Aim


The main objective of the Action is to structure and co-ordinate research activity on HPC-enabled Modelling and Simulation for Big Data problems across Europe.

Action (Secondary) Objectives


MoU Action Secondary Objectives

SO1 To build an effective, durable and active working community of European researchers in the area, with a span of about 60 research institutions and companies, more than 15 COST Countries
SO2 Constantly strive to expand the Action’s activities to other participants, increasing both the number of research institutions and companies, and that of COST Countries
SO3 To foster the formation of new multi-disciplinary expertise of competent researchers exploiting HPC-enabled MS, and contribute, in particular, to the formation of the new generations of such researchers
SO4 To disseminate obtained research results, identify best practices, and develop prototypes and supporting tools
SO5 Strengthen the collaboration with European companies in order to establish efficient technological transfer of the latest HPC-enabled MS techniques, methods, and tools, encouraging their industrial adoption
SO6 To establish the Action itself as a reference point of competence that can provide advisory support to industries and can offer informed expertise to policy makers in the strategic field of HPC-enabled MS

Action Goals


ID Grant Agreement Period Goal MoU Objective(s) it relates to
GAPG 1-1 Constructively aggregate the participants’ regionally and technically dispersed expertise and contacts to foster HPC routine.  PO, SO1, SO2
GAPG  1-2 Formal description of the state-of-the-art implementations and concepts supporting modelling and simulation models for Big Data. PO, SO1, SO3, SO5, SO6


ID  Grant Agreement Period Goal MoU Objective(s) it relates to
 GAPG  2-1  IT-based map the expertise of the Action members to foster HPC and MS routines– expertise database  PO, SO1, SO2, SO5
 GAPG  2-2  Specify testbeds and technological requirements and enhancements for HPC-based models, simulators and practical applications in Big Data. PO, SO3, SO4, SO6
 GAPG  2-3  Increase awareness on the COST Action, actively reaching out to academics, practitioners, and public in general via scientific articles, position papers, press releases, and keynote talks. PO, SO2, SO4, SO5
 GAPG  2-4  Improvement of the knowledge transfer with industrial partners invited to the Action PO, SO2, SO5


ID Grant Agreement Period Goal MoU Objective(s) it relates to
 GAPG  3-1  Provide a world-class European MS & HPC expertise exchange, where best practices are developed for the benefit of a broad range of academic and industry stakeholders.  PO, SO2, SO4, SO6
  GAPG  3-2  Compile and release a consistent set of case studies on HPC & MS, which can be used as reference source by researchers and practitioners. PO, SO3, SO6
  GAPG  3-3  Reach out to industry by contacting industrial research facilities and competence centers, providing expertise and services on HPC and MS applications and software development. PO, SO3, SO4, SO5
 GAPG  3-4  Establish a pan-European dissemination network to reach out to all stakeholders (academia, industry, government and the general public) and increase efficiency through the strategic use of joint forums for dissemination. PO, SO4, SO5, SO6


ID Grant Agreement Period Goal MoU Objective(s) it relates to
 GAPG  4-1 Create an integrated Expertise Map of the cHiPSet
Action members by systematically combining all
developed expertise databases, search engines and
Web services.
 PO, SO1, SO3
  GAPG  4-2 Continue industrial outreach endeavours and
potential path-to-market exploration of the Action
PO, SO2, SO5
  GAPG  4-3 IRelease the final set of dissemination publications
and map all Action publications into the cHiPSet
collection. This collection will serve as a reference
point for practitioners and academics. It shall include
an index map to allow the seamless navigation.
 GAPG  4-4 Produce a CHIPSET manifesto (alas Berkeley
dwarves report) based on the cases of studies which
correlates data-intensive HPC techniques with the
corresponding modelling and simulation cases.
PO,  SO6