Programming with Sociable Resources

We present a model for shared-memory parallel programming that makes shared objects (“resources”) the drivers of heap-manipulating parallel computations. The model aims to syntactically capture patterns of spatial locality in heap updates and to express the maximum amount of logical parallelism in computations. To achieve this, we take a “resources’-eye” view of parallel operations on the heap. Resources are now viewed as active entities arranged in a network that is the heap. While they actively change their data content and links to other resources, each change is local and restricted to a spatial “neighborhood”. Global computations are phrased as massively parallel compositions of these local operations. Our programming abstractions include operations that merge neighboring resources into larger resources and split complex resources into simpler ones. These abstractions are composable and directly encode heap-allocated data structures and spatial separation among resources. The model is data-race free even though it does not explicitly use locks. We demonstrate that the model allows easy, and easily parallelizable, programming of several important applications exhibiting irregular data-parallelism. In particular, it faithfully expresses the parallelism inherent in many natural processes and thus seems ideal for scientific and multimedia applications modeling them.