Speeding Up Distributed MapReduce Applications Using Hardware Accelerators

—In an attempt to increase the performance/cost ratio, large compute clusters are becoming heterogeneous at multiple levels: from asymmetric processors, to different system architectures, operating systems and networks. Exploiting the intrinsic multi-level parallelism present in such a complex execution environment has become a challenging task using traditional parallel and distributed programming models. As a result, an increasing need for novel approaches to exploiting parallelism has arisen in these environments. MapReduce is a data-driven programming model originally proposed by Google back in 2004 as a flexible alternative to the existing models, specially devoted to hiding the complexity of both developing and running massively distributed applications in large compute clusters. In some recent works, the MapReduce model has been also used to exploit parallelism in other non-distributed environments, such as multi-cores, heterogeneous processors and GPUs. In this paper we intr...

• Type: Conference
• Year: 2009
• Where: ICPP
• Authors: Yolanda Becerra, Vicenç Beltran, David Carrera, Marc González, Jordi Torres, Eduard Ayguadé
• Keywords: Distributed And Parallel Computing, Execution Environment, ICPP 2009, Intrinsic Multi-level Parallelism, Large Compute Clusters