Section: New Results

Structuring applications for scalability

Handling data consistency in parallel and distributed settings is a challenging task, in particular if we want to allow for an easy to handle asynchronism between tasks. Our publication [4] shows how to produce deadlock-free iterative programs that implement strong overlapping between communication, IO and computation.

A new implementation (ORWL) of our ideas of combining control and data management in C has been undertaken, see  5.2.1 . In 2014, work has demonstrated its efficiency for a large variety of platforms, see [20] . By using the example of dense matrix multiplication, we show that ORWL permits to reuse existing code for the target architecture, namely open source library ATLAS, Intel's compiler specific MKL library or NVidia's CUBLAS library for GPUs. ORWL assembles local calls into these libraries into efficient functional code, that combines computation on distributed nodes with efficient multi-core and accelerator parallelism.

Our next efforts will concentrate on the continuation of an implementation of a complete application (an American Option Pricer) that was chosen because it presents a non-trivial data transfer and control between different compute nodes and their GPU. ORWL is able to handle such an application seamlessly and efficiently, a real alternative to home made interactions between MPI and CUDA.