Fast and lightweight support for nested parallelism on cluster-based embedded many-cores

Several recent many-core accelerators have been architected as fabrics of tightly-coupled shared memory clusters. A hierarchical interconnection system is used – with a crossbarlike medium inside each cluster and a network-on-chip (NoC) at the global level – which make memory operations nonuniform (NUMA). Nested parallelism represents a powerful programming abstraction for these architectures, where a first level of parallelism can be used to distribute coarse-grained tasks to clusters, and additional levels of fine-grained parallelism can be distributed to processors within a cluster. This paper presents a lightweight and highly optimized support for nested parallelism on cluster-based embedded many-cores. We assess the costs to enable multi-level parallelization and demonstrate that our techniques allow to extract high degrees of parallelism.