Glasswing
Ismail El-Helw, Rutger F. H. Hofman, Henri E. Bal · 2014
The impact and significance of parallel computing techniques is continuously increasing given the current trend of incorporating more cores in new processor designs. However, many Big Data systems fail to exploit the abundant computational power of multi-core CPUs and GPUs to their full potential. We present Glasswing, a scalable MapReduce framework that employs a configurable mixture of coarse- and fine-grained parallelism to achieve high performance on multi-core CPUs and GPUs. We experimentally evaluated the performance of five MapReduce applications and show that Glasswing outperforms Hadoop on a 64-node multi-core CPU cluster by a factor between 1.8 and 4, and by a factor from 20 to 30 on a 16-node GPU cluster.