A Map-Reduce-Like System for Programming and Optimizing Data-Intensive Computations on Emerging Parallel Architectures
Wei Jiang · OhioLink ETD Center (Ohio Library and Information Network) · 2012
Parallel computing environments are ubiquitous nowadays, including traditional CPU clusters and the emergence of GPU clusters and CPU-GPU clusters because of their performance, cost and energy efficiency.With this trend, an important research issue is to effectively utilize the massive computing power in these architectures to accelerate dataintensive applications arising from commercial and scientific domains.Map-reduce and its Hadoop implementation have become popular for its high programming productivity but exhibits non-trivial performance losses for many classes of data-intensive applications.Also, there is no general map-reduce-like support up to date for programming heterogeneous systems like a CPU-GPU cluster.Besides, it is widely accepted that the existing fault tolerant techniques for high-end systems will not be feasible in the exascale era and novel solutions are clearly needed.Our overall goal is to solve these programmability and performance issues by providing a map-reduce-like API with better performance efficiency as well as efficient fault tolerance support, targeting data-intensive applications and various new emerging parallel architectures.We believe that a map-reduce-like API can ease the programming difficulty in these parallel architectures, and more importantly improve the performance efficiency of parallelizing these data-intensive applications.Also, the use of a high-level programming model can greatly simplify fault-tolerance support, resulting in low overhead checkpointing and recovery.