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Many scientific applications involve array operations that are sparse in nature, ie array elements depend on the values of relatively few elements of the same or another array. When parallelised in the shared-memory model, there are often inter-thread dependencies which require that the individual array updates are protected in some way. Possible strategies include protecting all the updates, or having each thread compute local temporary results which are then combined globally across threads. However, for the extremely common situation of sparse array access, neither of these approaches is particularly efficient. The key point is that data access patterns usually remain constant for a long time, so it is possible to use an inspector/executor approach. When the sparse operation is first encountered, the access pattern is inspected to identify those updates which have potential inter-thread dependencies. Whenever the code is actually executed, only these selected updates are protected. We propose a new OpenMP clause, indirect, for parallel loops that have irregular data access patterns. This is trivial to implement in a conforming way by protecting every array update, but also allows for an inspector/executor compiler implementation which will be more efficient in sparse cases. We describe efficient compiler implementation strategies for the new directive. We also present timings from the kernels of a Discrete Element Modelling application and a Finite Element code where the inspector/executor approach is used. The results demonstrate that the method can be extremely efficient in practice.

In this paper we describe the extension of the CAPO parallelization support tool to support multilevel parallelism based on OpenMP directives. CAPO generates OpenMP directives with extensions supported by the NanosCompiler to allow for directive nesting and definition of thread groups. We report some results for several benchmark codes and one full application that have been parallelized using our system.