Section: New Results

Combining Locking and Data Management Interfaces

Participants : Jens Gustedt, Daniel Salas.

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 [2] shows how to produce deadlock-free iterative programs that implement strong overlapping between communication, IO and computation.

An implementation (ORWL) of our ideas of combining control and data management in C has been undertaken, see Section 6.3. In previous work it has demonstrated its efficiency for a large variety of platforms.

In the framework of the ASNAP project we have used ordered read-write locks (ORWL) as a model to dynamically schedule a pipeline of parallel tasks that realize a parallel control flow of two nested loops; an outer iteration loop and an inner data traversal loop. Other than dataflow programming, for each individual data object we conserve the same modification order as the sequential algorithm. As a consequence the visible side effects on any object can be guaranteed to be identical to a sequential execution. Thus the set of optimizations that are performed are compatible with C's abstract state machine and compilers could perform them, in principle, automatically and unobserved. See [16] for first results.

In the context of the Prim'Eau project (see 9.1.2) we use ORWL to integrate parallelism into an already existing Fortran application that computes floods in the region that is subject to the study. A first step of such a parallelization has been started by using ORWL on a process level. Our final goal will be to extend it to the thread level and to use the application structure for automatic placement on compute nodes. A first step to this goal has been a specific decomposition of geological data, see [21].

Within the framework of the thesis of Daniel Salas we have successfully applied ORWL to process large histopathology images. We are now able to treat such images distributed on several machines or shared in an accelerator (Xeon Phi) transparently for the user. This year, Daniel has successfully defended his thesis, see [7].