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MPI applications matter. However, with the advent of many-core processors, traditional MPI applications are challenged to achieve satisfactory performance. This is due to the inability of these applications to respond to load imbalances, to reduce serialization imposed by synchronous communication patterns, to overlap communication with computation and finally to deal with increasing memory overheads. The MPI specification provides asynchronous calls to mitigate some of these factors. However, application developers rarely make the effort to apply them efficiently. In this work, we present a methodology to develop hybrid applications called Hierarchical Domain Over-decomposition with Tasking (HDOT), that reduces programming effort by emphasizing the reuse of data partition schemes from process-level and applying them on task-level, allowing a top-down approach to express concurrency and allowing a natural coexistence between MPI and shared-memory programming models. Our integration of MPI and OmpSs-2 shows promising results in terms of programmability and performance measured on a set of applications.
We have extended the Falkon lightweight task execution framework to make loosely coupled programming on petascale systems a practical and useful programming model. This work studies and measures the performance factors involved in applying this appro
The Preconditioned Conjugate Gradient method is often employed for the solution of linear systems of equations arising in numerical simulations of physical phenomena. While being widely used, the solver is also known for its lack of accuracy while co
Distributed applications, such as database queries and distributed training, consist of both compute and network tasks. DAG-based abstraction primarily targets compute tasks and has no explicit network-level scheduling. In contrast, Coflow abstractio
Containers are an emerging technology that hold promise for improving productivity and code portability in scientific computing. We examine Linux container technology for the distribution of a non-trivial scientific computing software stack and its e
This paper tries to reduce the effort of learning, deploying, and integrating several frameworks for the development of e-Science applications that combine simulations with High-Performance Data Analytics (HPDA). We propose a way to extend task-based