Avaliação de Cluster Raspberry Pi para Execução de Aplicações de Análise de Imagens Microscópicas Médicas
Abstract
The health care area, in particular, the clinical pathology with automated analysis of microscopic images is a field with growing demand for computing power. A barrier to the effective use of computing in this scenario is the unavailability of enough computational resources due to the high related costs. In this paper, we evaluate the use of a low-cost architecture named Raspberry Pi 2 to build computer clusters with 64 cores and 16GB RAM and their use in medical applications in the analysis of microscopic images. Our evaluation’s goal the identification of the potential cost benefit of this platform compared to other processors, taking into account runtime, hardware cost and energy consumption. The experimental results showed that the use of a Raspberrys cluster is 10× and 2× faster than the execution of the application, respectively, on a Core2Duo and I7 machine. Even at full capacity, the cluster is more power efficient than other processors only in their idle mode. As such, the Raspberry Pi 2 has proved to be an excellent and promising platform for running our class of target applications.
References
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Teodoro, G., Kurc, T., Andrade, G., Kong, J., Ferreira, R., and Saltz, J. (2015). Application performance analysis and efficient execution on systems with multi-core CPUs, GPUs and MICs: a case study with microscopy image analysis. International Journal of High Performance Computing Applications.
Teodoro, G., Kurc¸, T. M., Kong, J., Cooper, L. A. D., and Saltz, J. H. (2014). Comparative Performance Analysis of Intel (R) Xeon Phi (TM), GPU, and CPU: A Case Study from Microscopy Image Analysis. In 2014 IEEE 28th International Parallel and Distributed Processing Symposium, May 19-23, 2014, pages 1063–1072.
Teodoro, G., Pan, T., Kurc, T. M., Cooper, L., Kong, J., and Saltz, J. H. (2012). A fast parallel implementation of queue-based morphological reconstruction using GPUs. Emory University, Center for Comprehensive Informatics, CCI-TR-2012-2.
Teodoro, G., Pan, T., Kurc, T. M., Kong, J., Cooper, L. A., Podhorszki, N., Klasky, S., and Saltz, J. H. (2013a). High-throughput Analysis of Large Microscopy Image Datasets on CPU-GPU Cluster Platforms. In IPDPS ’13: Proceedings of the 2013 IEEE International Symposium on Parallel and Distributed Processing.
Teodoro, G., Pan, T., Kurc, T. M., Kong, J., Cooper, L. A., and Saltz, J. H. (2013b). Efficient Irregular Wavefront Propagation Algorithms on Hybrid CPU-GPU machines. Parallel Computing, 39(4-5):189–211.
Vincent, L. (1993). Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms. IEEE Transactions on Image Processing, 2:176–201.
Beynon, M. D., Kurc, T., Catalyurek, U., Chang, C., Sussman, A., and Saltz, J. (2001). Distributed processing of very large datasets with DataCutter. Parallel Comput., 27(11):1457–1478.
Bradski, G. (2000). The OpenCV Library. Dr. Dobb’s Journal of Software Tools.
Butler, H. (2012). Intel core i7-3770k power consumption. http://www.bit-tech.net/hardware/2012/04/23/intel-core-i7-3770k-review/8.
Chang, C., Moon, B., Acharya, A., Shock, C., Sussman, A., and Saltz, J. H. (1997). Titan: A High-Performance Remote Sensing Database. In Proceedings of the Thirteenth International Conference on Data Engineering, ICDE ’97, pages 375–384, Washington, DC, USA. IEEE Computer Society.
Cooper, L. A. D., Kong, J., Gutman, D. A., Wang, F., Cholleti, S. R., Pan, T. C., Widener, P. M., Sharma, A., Mikkelsen, T., Flanders, A. E., Rubin, D. L., Meir, E. G. V., Kurc, T. M., Moreno, C. S., Brat, D. J., and Saltz, J. H. (2010). An integrative approach for in silico glioma research. IEEE Trans Biomed Eng., 57(10):2617–2621.
Corporation, S. (2016a). Cartao de memoria sandisk extreme microsdhc/microsdxc uhsi. https://www.sandisk.com.br/home/memory-cards/microsd-cards/extreme-microsd.
Corporation, S. (2016b). Cartao de memoria sandisk ultra microsdhc/microsdxc uhs-i para cameras. https://www.sandisk.com.br/home/memory-cards/microsd-cards/ultra-microsd-for-cameras.
Cox, S. J., Cox, J. T., Boardman, R. P., Johnston, S. J., Scott, M., and O’Brien, N. S. (2013). Iridis-pi: a low-cost, compact demonstration cluster. Cluster Computing, 17(2):349–358.
DELL (2016). Ecommerce dell. http://www.dell.com.br.
FOUNDATION., R. P. (2015). Raspberry pi 2 model b. https://www.raspberrypi.org/products/raspberry-pi-2-model-b/.
Klie, H., Bangerth,W., Gai, X., Wheeler, M. F., Stoffa, P. L., Sen, M. K., Parashar, M., C¸ atalyurek, U¨ . V., Saltz, J. H., and Kurc¸, T. M. (2006). Models, methods and middleware for grid-enabled multiphysics oil reservoir management. Eng. Comput. (Lond.), 22(3-4):349–370.
Kong, J., Cooper, L. A. D., Wang, F., Gao, J., Teodoro, G., Mikkelsen, T., Schniederjan, M. J., Moreno, C. S., Saltz, J. H., and Brat, D. J. (2013). Machine-based morphologic analysis of glioblastoma using whole-slide pathology images uncovers clinically relevant molecular correlates. PLoS ONE.
Kurc, T., Qi, X., Wang, D., Wang, F., Teodoro, G., Cooper, L., Nalisnik, M., Yang, L., Saltz, J., and Foran, D. J. (2015). Scalable analysis of big pathology image data cohorts using efficient methods and high-performance computing strategies. BMC Bioinformatics, 16(1):1.
Kurc¸, T. M., C¸ atalyu¨rek, U¨ . V., Zhang, X., Saltz, J. H., Martino, R., Wheeler, M. F., Peszynska, M., Sussman, A., Hansen, C., Sen, M. K., Seifoullaev, R., Stoffa, P. L., Torres-Verd´ın, C., and Parashar, M. (2005). A simulation and data analysis system for large-scale, data-driven oil reservoir simulation studies. Concurrency - Practice and Experience, 17(11):1441–1467.
Page, M. (2009). Cpu power consumption tests. http://www.pcstats.com/articleview.cfm?articleid=2395&page=2.
Parashar, M., Matossian, V., Bangerth, W., Klie, H., Rutt, B., Kurc¸, T. M., C¸ atalyurek, U¨ . V., Saltz, J. H., and Wheeler, M. F. (2005). Towards Dynamic Data-Driven Optimization of Oil Well Placement. In International Conference on Computational Science (2), pages 656–663.
Raspian (2016). Raspian. http://www.raspian.org/.
Shock, C. T., Chang, C., Moon, B., Acharya, A., Davis, L., Saltz, J., and Sussman, A. (1998). The design and evaluation of a high-performance earth science database. Parallel Computing, 24(1):65 – 89.
Teodoro, G., Kurc, T., Andrade, G., Kong, J., Ferreira, R., and Saltz, J. (2015). Application performance analysis and efficient execution on systems with multi-core CPUs, GPUs and MICs: a case study with microscopy image analysis. International Journal of High Performance Computing Applications.
Teodoro, G., Kurc¸, T. M., Kong, J., Cooper, L. A. D., and Saltz, J. H. (2014). Comparative Performance Analysis of Intel (R) Xeon Phi (TM), GPU, and CPU: A Case Study from Microscopy Image Analysis. In 2014 IEEE 28th International Parallel and Distributed Processing Symposium, May 19-23, 2014, pages 1063–1072.
Teodoro, G., Pan, T., Kurc, T. M., Cooper, L., Kong, J., and Saltz, J. H. (2012). A fast parallel implementation of queue-based morphological reconstruction using GPUs. Emory University, Center for Comprehensive Informatics, CCI-TR-2012-2.
Teodoro, G., Pan, T., Kurc, T. M., Kong, J., Cooper, L. A., Podhorszki, N., Klasky, S., and Saltz, J. H. (2013a). High-throughput Analysis of Large Microscopy Image Datasets on CPU-GPU Cluster Platforms. In IPDPS ’13: Proceedings of the 2013 IEEE International Symposium on Parallel and Distributed Processing.
Teodoro, G., Pan, T., Kurc, T. M., Kong, J., Cooper, L. A., and Saltz, J. H. (2013b). Efficient Irregular Wavefront Propagation Algorithms on Hybrid CPU-GPU machines. Parallel Computing, 39(4-5):189–211.
Vincent, L. (1993). Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms. IEEE Transactions on Image Processing, 2:176–201.
Published
2016-07-04
How to Cite
RAMOS, Rafael M.; RALHA, Célia; TEODORO, George.
Avaliação de Cluster Raspberry Pi para Execução de Aplicações de Análise de Imagens Microscópicas Médicas. In: INTEGRATED SOFTWARE AND HARDWARE SEMINAR (SEMISH), 43. , 2016, Porto Alegre.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 1795-1806.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2016.9528.
