计算机发展前沿讲座

本系列讲座内容主要是围绕Scala编程语言技术，大数据分析处理和并行分布式计算领域的发展前沿技术。内容主要是来自于近期相关领域的文献的浏览和总结。

课程安排

随课程进展，以下安排可能会有变化。

学期周 日期 内容 幻灯片 注释
11 11/14 Scala 编程技术最新发展 PDF  
12 11/21 级式编程；嵌入的特定领域语言    
13 11/28 深度学习(deep learning)简介 PDF  
14 12/5 深度学习(deep learning)简介继续；2013年10大突破技术    
15 12/12 Spark简介； PDF  
16 12/17 Spark简介继续；并行分布算法 PDF  

编程讲解参考

参考文献

[1] G. Mone, “Beyond Hadoop,” Commun. ACM, vol. 56, pp. 22-24, 2013.

[2] H. V. Jagadish, J. Gehrke, A. Labrinidis, Y. Papakonstantinou, J. M. Patel, R. Ramakrishnan, et al., “Big data and its technical challenges,” Commun. ACM, vol. 57, pp. 86-94, 2014.

[3] J. Horey, E. Begoli, R. Gunasekaran, S.-H. Lim, and J. Nutaro, “Big data platforms as a service: challenges and approach,” presented at the Proceedings of the 4th USENIX conference on Hot Topics in Cloud Ccomputing, Boston, MA, 2012.

[4] U. Kang and C. Faloutsos, “Big graph mining,” ACM SIGKDD Explorations Newsletter, vol. 14, p. 29, 2013.

[5] A. K. Sujeeth, T. Rompf, K. J. Brown, H. Lee, H. Chafi, V. Popic, et al., “Composition and Reuse with Compiled Domain-Specific Languages,” in ECOOP 2013 – Object-Oriented Programming. vol. 7920, D. Hutchison, T. Kanade, J. Kittler, J. M. Kleinberg, F. Mattern, J. C. Mitchell, et al., Eds., ed Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 52–78.

[6] G. Anthes, “Deep learning comes of age,” Commun. ACM, vol. 56, pp. 13-15, 2013.

[7] V. Gramoli and R. Guerraoui, “Democratizing transactional programming,” Commun. ACM, vol. 57, pp. 86-93, 2014.

[8] B. Haeupler, G. Pandurangan, D. Peleg, R. Rajaraman, and Z. Sun, “Discovery through gossip,” in Proceedinbgs of the 24th ACM symposium, G. Blelloch and M. Herlihy, Eds., ed, p. 140.

[9] H. Chafi, A. K. Sujeeth, K. J. Brown, H. Lee, A. R. Atreya, and K. Olukotun, “A domain-specific approach to heterogeneous parallelism,” ACM SIGPLAN Notices, vol. 46, p. 35, 2011.

[10] S. Sakr, A. Liu, and A. G. Fayoumi, “The family of mapreduce and large-scale data processing systems,” ACM Comput. Surv., vol. 46, pp. 1-44, 2013.

[11] G. Mishne, J. Dalton, Z. Li, A. Sharma, and J. Lin, “Fast data in the era of big data: Twitter’s real-time related query suggestion architecture,” presented at the Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, New York, New York, USA, 2013.

[12] T. Rompf and M. Odersky, “Lightweight modular staging,” Communications of the ACM, vol. 55, p. 121, 2012.

[13] B. Hindman, A. Konwinski, M. Zaharia, A. Ghodsi, A. D. Joseph, R. Katz, et al., “Mesos: a platform for fine-grained resource sharing in the data center,” presented at the Proceedings of the 8th USENIX conference on Networked systems design and implementation, Boston, MA, 2011.

[14] H. Karloff, S. Suri, and S. Vassilvitskii, “A model of computation for MapReduce,” presented at the Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms, Austin, Texas, 2010.

[15] T. Rompf, A. K. Sujeeth, N. Amin, K. J. Brown, V. Jovanovic, H. Lee, et al., “Optimizing data structures in high-level programs: new directions for extensible compilers based on staging,” SIGPLAN Not., vol. 48, pp. 497-510, 2013.

[16] A. K. Sujeeth, H. Lee, K. J. Brown, T. Rompf, H. Chafi, M. Wu, et al., “OptiML: An implicitly parallel domain-specific language for machine learning,” in Proceedings of the International Conference on Machine Learning. Haifa, Israel, ed, 2011.

[17] G. E. Blelloch and B. M. Maggs, “Parallel algorithms,” in Algorithms and theory of computation handbook, J. A. Mikhail and B. Marina, Eds., ed: Chapman & Hall/CRC, 2010, pp. 25-25.

[18] J. Langford, “Parallel machine learning on big data,” XRDS, vol. 19, pp. 60-62, 2012.

[19] S. Venkataraman, E. Bodzsar, I. Roy, A. AuYoung, and R. S. Schreiber, “Presto: distributed machine learning and graph processing with sparse matrices,” presented at the Proceedings of the 8th ACM European Conference on Computer Systems, Prague, Czech Republic, 2013.

[20] M. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma, M. McCauley, et al., “Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing,” presented at the Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation, San Jose, CA, 2012.

[21] J. Lin and D. Ryaboy, “Scaling big data mining infrastructure: the twitter experience,” SIGKDD Explor. Newsl., vol. 14, pp. 6-19, 2013.

[22] M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker, and I. Stoica, “Spark: cluster computing with working sets,” presented at the Proceedings of the 2nd USENIX conference on Hot topics in cloud computing, Boston, MA, 2010.

[23] G. Salvaneschi, J. Drechsler, and M. Mezini, “Towards Distributed Reactive Programming,” in Coordination Models and Languages. vol. 7890, D. Hutchison, T. Kanade, J. Kittler, J. M. Kleinberg, F. Mattern, J. C. Mitchell, et al., Eds., ed Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 226–235.

[24] M. Odersky and T. Rompf, “Unifying functional and object-oriented programming with Scala,” Communications of the ACM, vol. 57, pp. 76–86, 2014.

[25] M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. Katz, A. Konwinski, et al., “A view of cloud computing,” Commun. ACM, vol. 53, pp. 50-58, 2010.

其他参考