International audienceThe ever-growing number of cores in embedded chips emphasizes more than ever the complexity inherent to parallel programming. To solve these programmability issues, there is a renewed interest in the dataflow paradigm. In this context, we present a compilation toolchain for the Sigma-C language, which allows the hierarchical construction of stream applications and automatic mapping of this application to an embedded manycore target. As a demonstration of this toolchain, we present an implementation of a H.264 encoder and evaluate its performance on Kalray's embedded manycore MPPA chip

International audienceThe dataflow programming model has shown to be a relevant approach to efficiently run mas-sively 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 impor-tance 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 devel-oper 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

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In this work we present Armadillo a compilation chain used for compiling applications written in a high-level language (C++) to work on encrypted data. The back-end of the compilation chain is based on homomorphic encryption. The tool-chain further automatically handle a huge amount of parallelism so as to mitigate the performance overhead of using homomorphic encryption.