Self-adaptive OmpSs tasks in heterogeneous environments

As new heterogeneous systems and hardware accelerators appear, high performance computers can reach a higher level of computational power. Nevertheless, this does not come for free: the more heterogeneity the system presents, the more complex becomes the programming task in terms of resource management. OmpSs is a task-based programming model and framework focused on the runtime exploitation of parallelism from annotated sequential applications. This paper presents a set of extensions to this framework: we show how the application programmer can expose different specialized versions of tasks (i.e. pieces of specific code targeted and optimized for a particular architecture) and how the system can choose between these versions at runtime to obtain the best performance achievable for the given application. From the results obtained in a multi-GPU system, we prove that our proposal gives flexibility to application's source code and can potentially increase application's performance.This work has been supported by the European Commission through the ENCORE project (FP7-248647), the TERAFLUX project (FP7-249013), the TEXT project (FP7-261580), the HiPEAC-3 Network of Excellence (FP7-ICT 287759), the Intel-BSC Exascale Lab collaboration project, the support of the Spanish Ministry of Education (CSD2007- 00050 and FPU program), the projects of Computación de Altas Prestaciones V and VI (TIN2007-60625, TIN2012-34557) and the Generalitat de Catalunya (2009-SGR-980).Peer Reviewe