HadoopCL2: Motivating the Design of a Distributed, Heterogeneous Programming System with Machine-Learning Applications

IEEE Transactions on Parallel and Distributed Systems

Volumn 27, Issue 3, 2016, Pages 762-775

  Grossman, Max ^a   Breternitz, Mauricio ^b   Sarkar, Vivek ^a  

^a Rice University   (United States)

^b ADVANCED MICRO DEVICES   (United States)

Author keywords

auto scheduling; distributed; GPU; heterogeneous; MapReduce; programming model

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLUSTERING ALGORITHMS; DISTRIBUTED COMPUTER SYSTEMS; LEARNING SYSTEMS; OPTIMIZATION; SOFTWARE DESIGN;

APPLICATION DEVELOPMENT; DISTRIBUTED; DOMAIN-SPECIFIC KNOWLEDGE; HETEROGENEOUS; HETEROGENEOUS PROGRAMMING; MACHINE LEARNING APPLICATIONS; MAP-REDUCE; PROGRAMMING MODELS;

COMPUTER SYSTEMS PROGRAMMING;

EID: 84962532980     PISSN: 10459219     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPDS.2015.2414943     Document Type: Article

References (16)

1. Apache hadoop. [Online]. Available: http://hadoop. apache. org/, 2007.
2. E. A. Chu and Cheng-Tao, "Map-reduce for machine learning on multicore, " in Proc. NIPS, 2006, vol 6.
3. S. Ghemawat and J. Dean, "Mapreduce: Simplified data processing on large clusters, " Commun. ACM, vol. 51, no. 1, pp. 107-113, 2008.
4. M. Grossman, M. Breternitz, and V. Sarkar, "HadoopCL: MapReduce on distributed heterogeneous platforms through seamless integration of Hadoop and OpenCL, " HPDIC, 2013
5. J. D. Owens, M. Houston, D. Luebke, S. Green, J. E. Stone, J. C. Phillips, "GPU computing, " in Proc. IEEE, 2008, vol. 96, no. 5, pp. 879-899.
6. S. Che, M. Boyer, J. Meng, D. Tarjan, J. W. Sheaffer, S.-H. Lee, and K. Skadron, "Rodinia: A benchmark suite for heterogeneous computing, " in Proc. IEEE Int. Symp. Workload Characterization, 2009, pp. 44-54.
7. M. Curtis-Maury, C. D. Antonopoulos, F. Blagojevic, and D. S. Nikolopoulos, "Predication-based power-performance adaptation of multithreaded scientific codes, " IEEE Trans. Parallel Distrib. Syst., vol. 19, no. 10, pp. 1396-1410, Oct. 2008.
8. M. Curtis-Maury, A. Shah, F. Blagojevic, D. S. Nikolopoulos, B. R. de Supinski, and M. Schulz, "Prediction models for multi-dimensional power-performance optimization on many cores, " in Proc. 17th Int. Conf. Parallel Archit. Compilation Tech., 2008, pp. 250-259.
9. G. Frost. Aparapi in amd developer website. [Online]. Available: http://developer. amd. com/tools/heterogeneous-computing/ aparapi/, 2011.
10. Apache. Spark: Lightning-fast cluster computing. [Online]. Available: https://spark. apache. org/, 2012.
11. M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker, and I. Stoica, "Spark: Cluster computing with working sets, " in Proc. 2nd USENIX Conf. Hot Topics Cloud Comput., 2010, pp. 10-10.
12. M. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma, M. McCauley, M. J. Franklin, S. Shenker, and I. Stoica, "Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing, " in Proc. 9th USENIX Conf. Netw. Syst. Des. Implementation, 2012, p. 2.
13. J. Stuart and J. Owens, "Multi-GPU mapreduce on GPU clusters, " in Proc. Parallel Distrib. Process. Symp., 2011, pp. 1068-1079.
14. S. Koichi, H. Sato, and S. Matsuoka, "Hybrid map task scheduling for GPU-based heterogeneous clusters, " in Proc. IEEE 2nd Int. Conf. Cloud Comput. Technol. Sci., 2010, pp. 733-740.
15. A. Amin, F. Khunjush, and R. Azimi, "A preliminary study of incorporating GPUs in the Hadoop framework, " in Proc. 16th CSI Int. Symp. Comput. Archit. Digital Syst., 2012, pp. 178-185.
16. Y. Jia. (2013). Caffe: An open source convolutional architecture for fast feature embedding. [Online]. Available: http://caffe. berkeleyvision. org/