The objective of this WP is the Identification of the current developments in the available software by the partners and under development by third parties, and its associated algorithms and techniques in other to assess its scalability to simulation codes exascale.
At the end of this WP it is expected to have a detailed map of the most representative ingredients addressed to the exascale, that is, relevant codes and libraries on progress (i.e. Trilinos), algorithms, techniques and tendencies related the recommended use of accelerators, GPU and ARM processors, linked to other research projects, the already mentioned DEEP and MONTBLANC, attending to their hardware related goals, CRESTA, on the software side and to mention the ones related to the FP7, but also non-European ones, such as Interoperable Technologies for Advanced Petascale Simulations (ITAPS, http://www.itaps.org).
In this task we will design scalability tests for the parallel algorithms on a parallel architecture as a measure of their capability to effectively utilize an increasing number of processors. The most relevant parameters will be defined for the benchmarking that will be used through the whole project, to compare the effective use of many-core, the flexibility working with hybrid architectures, load transfer, speed, how certain software strategies affect the power saving and error propagation (which is an important issue when working with this so huge amount of data).
The numerical and parallel scalability of an algorithm on a parallel architecture will be studied under different sets of conditions. The scalability analysis will be used to select the best algorithm/architecture combination for a problem under different constraints related to the problem size and the number of processors. It will also be used to predict the performance of a parallel algorithm and a parallel architecture for a large number of processors from the known performance on fewer processors.
In the scalability analysis we will introduce hardware cost factors (in addition to speed up and efficiency) so that that overall cost effectiveness can be determined.
Task leader: CESCA. Partners involved: CIMNE, CESCA, LUH-HLRN, NTUA
Lead beneficiary: CESCA
In this task we will look in detail at the parallelization features of the codes made available by the partners as well as their potential features addressed to the exascale expected to be implemented in them.
Although the codes that will be surveyed have been already briefly described in section 1.3.3., below are some additional comments about their specific role in the project and towards the exascale:
Task leader: NTUA. Partners involved: CIMNE, LUH-IKM, NTUA, QUANTECH
Lead beneficiary: NTUA