Section: Research Program

Axis 4: Parallelism

Together with the design of new data structures and new algorithms, our research program aims to propose efficient hardware implementation. Even if not explicitly mentioned in the three previous axes, we have constantly in mind to exploit the parallelism of current processors. Practically, and depending of the nature of the computation to perform, three levels of parallelism are addressed: the use of vector instructions of today processors, the multithreading offered by multi-core systems, and the cluster (or cloud) infrastructures.

Consequent bioinformatics treatments, from the processing of raw HTS data to high-level analysis, are generally performed within a workflow environment and executed on cluster systems. Automating the parallelization of such treatments directly from a graphical capture of the workflow is a necessity for end-users that are generally not expert in parallelism. The challenge here is to hide, as much as possible, the different transformations to go from a high level workflow description to an efficient parallel execution that exploits both task-level and data-level parallelism[25] .

Another research activity of this axe is the design of parallel algorithms targeting hardware accelerators, especially GPU boards (Graphical Processing Unit). These devices now offer a high-level programming environment to access the hundred of processors available on a single chip [20] . A few bioinformatics treatments, such as ones that exhibit good computational regularity, can highly benefit from the computing power of this technology.