Abstract
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 multicore 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. Copyright © 2014 ACM.
| Original language | English |
|---|---|
| Title of host publication | HPDC 2014 - Proceedings of the 23rd International Symposium on High-Performance Parallel and Distributed Computing |
| Publisher | Association for Computer Machinery |
| Pages | 295-298 |
| Number of pages | 4 |
| ISBN (Print) | 9781450327480 |
| DOIs | |
| Publication status | Published - 2014 |
| Event | 23rd ACM Symposium on High-Performance Parallel and Distributed Computing, HPDC 2014 - Vancouver, BC, Canada Duration: 23 Jun 2014 → 27 Jun 2014 |
Conference
| Conference | 23rd ACM Symposium on High-Performance Parallel and Distributed Computing, HPDC 2014 |
|---|---|
| Country/Territory | Canada |
| City | Vancouver, BC |
| Period | 23/06/14 → 27/06/14 |
Keywords
- Heterogeneous
- MapReduce
- Opencl
- Scalability
