Current embedded computing architectures are moving to many-core concepts in order to sustain ever growing computing requirements within complexity and power budgets. Programming many-core architectures not only needs parallel programming skills, but also efficient exploitation of the parallelism at both the architecture and runtime levels. This paper presents a reactive tasks management (RTM) technique that is suitable for fine grain parallelism. Exploiting fine-grain parallelism eases the work of the developer since, most of the time, it is a form of parallelism which is naturally present in applications and doesn't require heavy algorithm rewriting. The RTM API leverages both hardware and software support to efficiently exploit fine-grain parallelism at the lowest possible cost. Simulation on the VC-1 decoding application showed that only 3.5% overhead is induced by this API which makes it truly suitable for fine grain tasks scheduling.
Synchronous Reactive Fine Grain Tasks Management for Homogeneous Many-Core Architectures / Ojail M.; David R.; Chehida K.B.; Lhuillier Y. (CEA, France); Benini L.. - STAMPA. - (2011), pp. 144-150. (Intervento presentato al convegno ARCS 2011, 24th International Conference on Architecture of Computing Systems 2011 tenutosi a Como, Italy nel February 22-23, 2011).
Synchronous Reactive Fine Grain Tasks Management for Homogeneous Many-Core Architectures
BENINI, LUCA
2011
Abstract
Current embedded computing architectures are moving to many-core concepts in order to sustain ever growing computing requirements within complexity and power budgets. Programming many-core architectures not only needs parallel programming skills, but also efficient exploitation of the parallelism at both the architecture and runtime levels. This paper presents a reactive tasks management (RTM) technique that is suitable for fine grain parallelism. Exploiting fine-grain parallelism eases the work of the developer since, most of the time, it is a form of parallelism which is naturally present in applications and doesn't require heavy algorithm rewriting. The RTM API leverages both hardware and software support to efficiently exploit fine-grain parallelism at the lowest possible cost. Simulation on the VC-1 decoding application showed that only 3.5% overhead is induced by this API which makes it truly suitable for fine grain tasks scheduling.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
