Iridis-pi: A low-cost, compact demonstration cluster

Cluster Computing

Volumn 17, Issue 2, 2014, Pages 349-358

  Cox, Simon J ^a   Cox, James T ^a   Boardman, Richard P ^a   Johnston, Steven J ^a   Scott, Mark ^a   O'Brien, Neil S ^a  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

ARM; Education; Hadoop; HDFS; HPL; Low cost; Low power cluster; MPI

Indexed keywords

ARM PROCESSORS; EDUCATION; RANDOM ACCESS STORAGE;

ARM; HADOOP; HDFS; HPL; LOW COSTS; LOW POWER; MPI;

COST ENGINEERING;

EID: 84901619687     PISSN: 13867857     EISSN: 15737543     Source Type: Journal    
DOI: 10.1007/s10586-013-0282-7     Document Type: Article

References (18)

1. Open source ARM userland. http://www. raspberrypi. org/archives/2221.
2. Petitet, A., Whaley, R. C., Dongarra, J., Cleary, A.: HPL-a portable implementation of the high-performance linpack benchmark for distributed-memory computers. http://www. netlib. org/benchmark/hpl/. Accessed Jan 2013.
3. Andersen, D. G., Franklin, J., Kaminsky, M., Phanishayee, A., Tan, L., Vasudevan, V.: Fawn: a fast array of wimpy nodes. Commun. ACM 54(7), 101-109 (2011). doi: 10. 1145/1965724. 1965747.
4. Argonne National Laboratory: MPICH2. http://www-unix. mcs. anl. gov/mpi/mpich2/.
5. Asanovic, K., Bodik, R., Catanzaro, B. C., Gebis, J. J., Husbands, P., Keutzer, K., Patterson, D. A., Plishker, W. L., Shalf, J., Williams, S. W., Yelick, K. A.: The landscape of parallel computing research: a view from Berkeley. Tech. Rep. UCB/EECS-2006-183, EECS Department, University of California, Berkeley. http://www. eecs. berkeley. edu/Pubs/TechRpts/2006/EECS-2006-183. html (2006).
6. Dongarra, J. J., Luszczek, P., Petitet, A.: The linpack benchmark: past, present and future. Concurr. Comput., Pract. Exp. 15(9), 803-820 (2003). doi: 10. 1002/cpe. 728.
7. Gates, M., Tirumala, A., Ferguson, J., Dugan, J., Qin, F., Gibbs, K., Estabrook, J.: Iperf-the TCP/UDP bandwidth measurement tool. http://sourceforge. net/projects/iperf/ [Online].
8. Hamilton, J.: Cooperative expendable micro-slice servers (cems): low cost, low power servers for internet-scale services. www. mvdirona. com/jrh/talksandpapers/jameshamilton_cems. pdf (2009).
9. Levenberg, K.: A method for the solution of certain non-linear problems in least squares. Q. Appl. Math. 2, 164-168 (1944).
10. Lim, K., Ranganathan, P., Chang, J., Patel, C., Mudge, T., Reinhardt, S.: Understanding and designing new server architectures for emerging warehouse-computing environments. Comput. Archit. News 36(3), 315-326 (2008). doi: 10. 1145/1394608. 1382148.
11. Marquardt, D.: An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Math. 11(2), 431-441 (1963). doi: 10. 1137/0111030.
12. Raspberry Pi Foundation: Raspberry Pi FAQs. http://www. raspberrypi. org/faqs. Accessed 2012.
13. Snell, Q. O., Mikler, A. R., Gustafson, J. L.: Netpipe: a network protocol independent performance evaluator. In: IASTED International Conference on Intelligent Information Management and Systems (1996).
14. Szalay, A. S., Bell, G. C., Huang, H. H., Terzis, A., White, A.: Low-power amdahl-balanced blades for data intensive computing. Oper. Syst. Rev. 44(1), 71-75 (2010). doi: 10. 1145/1740390. 1740407.
15. Whaley, R. C., Petitet, A.: Minimizing development and maintenance costs in supporting persistently optimized BLAS. Softw. Pract. Exp. 35(2), 101-121 (2005). http://www. cs. utsa. edu/~whaley/papers/spercw04. ps.
16. Ghemawat, S., Gobioff, H., Leung, S.-T.: The Google file system. In: ACM Symposium on Operating Systems Principles, pp. 29-43 (2003).
17. Shvachko, K., Kuang, H., Radia, S., Chansler, R.: The hadoop distributed file system. In: Symposium on Mass Storage Systems (2010).
18. Dean, J., Ghemawat, S.: MapReduce: simplied data processing on large clusters. In: Operating Systems Design and Implementation, pp. 137-150 (2004).