Generating Code and Memory Buffers to Reorganize Data on Many-core Architectures

The dataflow programming model has shown to be a relevant approach to efficiently run massively parallel applications over many-core architectures. In this model, some particular builtin agents are in charge of data reorganizations between user agents. Such agents can Split, Join and Duplicate data onto their communication ports. They are widely used in signal processing for example. These system agents, and their associated implementations, are of major importance when it comes to performance, because they can stand on the critical path (think about Amdhal's law). Furthermore, a particular data reorganization can be expressed by the developer in several ways that may lead to inefficient solutions (mostly unneeded data copies and transfers). In this paper, we propose several strategies to manage data reorganization at compile time, with a focus on indexed accesses to shared buffers to avoid data copies. These strategies are complementary: they ensure correctness for each system agent configuration, as well as performance when possible. They have been implemented within the Sigma-C industry-grade compilation toolchain and evaluated over the Kalray MPPA 256-core processor.