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High-Performance Storage Support for Scientific Big Data Applications on the Cloud

  • Dongfang ZhaoEmail author
  • Akash Mahakode
  • Sandip Lakshminarasaiah
  • Ioan Raicu
Chapter
Part of the Computer Communications and Networks book series (CCN)

Abstract

This work studies the storage subsystem for scientific big data applications to be running on the cloud. Although cloud computing has become one of the most popular paradigms for executing data-intensive applications, the storage subsystem has not been optimized for scientific applications. In particular, many scientific applications were originally developed assuming a tightly coupled cluster of compute nodes with network-attached storage allowing massively parallel I/O accesses—the high-performance computing (HPC) systems. These applications, in turn, struggle in leveraging cloud platforms whose design goal is fundamentally different than that of HPC systems. We believe that when executing scientific applications in the cloud, a node-local distributed storage architecture is a key approach to overcome the challenges from the storage subsystem. We analyze and evaluate four representative file systems (S3FS, HDFS, Ceph, and FusionFS) on multiple platforms (Kodiak cluster, Amazon EC2) with a variety of benchmarks to explore how well these storage systems can handle metadata-intensive, write-intensive, and read-intensive workloads. Moreover, we elaborate the design and implementation of FusionFS that employs a scalable approach to managing both metadata and data in addition to its unique features on cooperative caching, dynamic compression, GPU-accelerated data redundancy, lightweight provenance, and parallel serialization.

Keywords

Cloud Computing File System Message Passing Interface Cloud Storage Cloud Platform 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Shvachko, K., Kuang, H., Radia, S., Chansler, R.: The hadoop distributed file system. In: Proceedings of IEEE Symposium on Mass Storage Systems and Technologies (2010)Google Scholar
  2. 2.
    Carns, P., Lang, S., Ross, R., Vilayannur, M., Kunkel, J., Ludwig, T.: Small-file access in parallel file systems. In: Proceedings of IEEE International Symposium on Parallel and Distributed Processing (2009)Google Scholar
  3. 3.
    Ghemawat, S., Gobioff, H., Leung, S.T.: The Google file system. In: ACM Symposium on Operating Systems Principles (2003)Google Scholar
  4. 4.
    S3FS: https://code.google.com/p/s3fs/. Accessed 6 March 2015
  5. 5.
    FUSE: http://fuse.sourceforge.net. Accessed 5 Sept 2014
  6. 6.
    Weil, S.A., Brandt, S.A., Miller, E.L., Long, D.D.E., Maltzahn, C.: Ceph: a scalable, high-performance distributed file system. In: Proceedings of the 7th Symposium on Operating Systems Design and Implementation (2006)Google Scholar
  7. 7.
    Zhao, D., Zhang, Z., Zhou, X., Li, T., Wang, K., Kimpe, D., Carns, P., Ross, R., Raicu, I.: FusionFS: Toward supporting data-intensive scientific applications on extreme-scale distributed systems. In: Proceedings of IEEE International Conference on Big Data, pp. 61–70 (2014)Google Scholar
  8. 8.
    Zhao, D., Liu, N., Kimpe, D., Ross, R., Sun, X.H., Raicu, I.: Towards exploring data-intensive scientific applications at extreme scales through systems and simulations. IEEE Trans. Parallel Distrib. Syst. 1–14 (2015). doi: 10.1109/TPDS.2015.2456896
  9. 9.
    Weil, S.A., Brandt, S.A., Miller, E.L., Maltzahn, C.: Crush: controlled, scalable, decentralized placement of replicated data. In: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing (2006)Google Scholar
  10. 10.
    Zhao, D., Raicu, I.: Distributed file systems for exascale computing. In: International Conference for High Performance Computing, Networking, Storage and Analysis (SC ’12), doctoral showcase (2012)Google Scholar
  11. 11.
    Zhao, D., Burlingame, K., Debains, C., Alvarez-Tabio, P., Raicu, I.: Towards high-performance and cost-effective distributed storage systems with information dispersal algorithms. In: IEEE International Conference on Cluster Computing (2013)Google Scholar
  12. 12.
    Zhao, D., Shou, C., Malik, T., Raicu, I.: Distributed data provenance for large-scale data-intensive computing. In: IEEE International Conference on Cluster Computing (2013)Google Scholar
  13. 13.
    Zhao, D., Qiao, K., Raicu, I.: Hycache+: towards scalable high-performance caching middleware for parallel file systems. In: Proceedings of the 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp. 267–276 (2014)Google Scholar
  14. 14.
    Zhao, D., Raicu, I.: HyCache: a user-level caching middleware for distributed file systems. In: Proceedings of IEEE 27th International Symposium on Parallel and Distributed Processing Workshops and PhD Forum (2013)Google Scholar
  15. 15.
    Zhao, D., Yin, J., Qiao, K., Raicu, I.: Virtual chunks: on supporting random accesses to scientific data in compressible storage systems. In: Proceedings of IEEE International Conference on Big Data, pp. 231–240 (2014)Google Scholar
  16. 16.
    Zhao, D., Yin, J., Raicu, I.: Improving the i/o throughput for data-intensive scientific applications with efficient compression mechanisms. In: International Conference for High Performance Computing, Networking, Storage and Analysis (SC ’13), poster session (2013)Google Scholar
  17. 17.
    Zhao, D., Qiao, K., Zhou, Z., Li, T., Zhou, X., Wang, K., Raicu, I.: Exploiting multi-cores for efficient interchange of large messages in distributed systems. Concurrency Comput.: Pract. Experience 2015 (accepted)Google Scholar
  18. 18.
  19. 19.
    Amazon EC2: http://aws.amazon.com/ec2. Accessed 6 March 2015
  20. 20.
    Welch, B., Noer, G.: Optimizing a hybrid SSD/HDD HPC storage system based on file size distributions. In: IEEE 29th Symposium on Mass Storage Systems and Technologies (2013)Google Scholar
  21. 21.
    Nagle, D., Serenyi, D., Matthews, A.: The Panasas activescale storage cluster: delivering scalable high bandwidth storage. In: Proceedings of ACM/IEEE Conference on Supercomputing (2004)Google Scholar
  22. 22.
    Zhao, D., Zhang, D., Wang, K., Raicu, I.: Exploring reliability of exascale systems through simulations. In: Proceedings of the 21st ACM/SCS High Performance Computing Symposium (HPC) (2013)Google Scholar
  23. 23.
    Schmuck, F., Haskin, R.: GPFS: a shared-disk file system for large computing clusters. In: Proceedings of the 1st USENIX Conference on File and Storage Technologies (2002)Google Scholar
  24. 24.
    Schwan, P.: Lustre: building a file system for 1,000-node clusters. In: Proceedings of the Linux Symposium (2003)Google Scholar
  25. 25.
    Wu, H., Ren, S., Garzoglio, G., Timm, S., Bernabeu, G., Chadwick, K., Noh, S.-Y.: A reference model for virtual machine launching overhead. IEEE Trans. Cloud Comput. (pp. 99), 1–1 (2014)Google Scholar
  26. 26.
    Wu, H., Ren, S., Garzoglio, G., Timm, S., Bernabeu, G., Noh, S.-Y.: Modeling the virtual machine launching overhead under fermicloud. In: 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), May 2014Google Scholar
  27. 27.
    Li, T., Zhou, X., Brandstatter, K., Zhao, D., Wang, K., Rajendran, A., Zhang, Z., Raicu, I.: ZHT: A light-weight reliable persistent dynamic scalable zero-hop distributed hash table. In: Proceedings of IEEE International Symposium on Parallel and Distributed Processing (2013)Google Scholar
  28. 28.
    Li, T., Ma, C., Li, J., Zhou, X., Wang, K., Zhao, D., Raicu, I.: Graph/z: a key-value store based scalable graph processing system. In: IEEE International Conference on Cluster Computing (2015)Google Scholar
  29. 29.
    Zhao, Y., Hategan, M., Clifford, B., Foster, I., von Laszewski, G., Nefedova, V., Raicu, I., Stef-Praun, T., Wilde, M.: Swift: Fast, reliable, loosely coupled parallel computation. In: IEEE Congress on Services (2007)Google Scholar
  30. 30.
    Raicu, I., Foster, I.T., Zhao, Y., Little, P., Moretti, C.M., Chaudhary, A., Thain, D.: The quest for scalable support of data-intensive workloads in distributed systems. In: Proceedings of ACM International Symposium on High Performance Distributed Computing (2009)Google Scholar
  31. 31.
    Shou, C., Zhao, D., Malik, T., Raicu, I.: Towards a provenance-aware distributed filesystem. In: 5th Workshop on the Theory and Practice of Provenance (TaPP) (2013)Google Scholar
  32. 32.
    Protocol Buffers: http://code.google.com/p/protobuf/. Accessed 5 Sept 2014
  33. 33.
    Carns, P.H., Ligon, W.B., Ross, R.B., Thakur, R.: PVFS: a parallel file system for linux clusters. In: Proceedings of the 4th Annual Linux Showcase and Conference (2000)Google Scholar
  34. 34.
    Li, T., Zhou, X., Wang, K., Zhao, D., Sadooghi, I., Zhang, Z., Raicu, I.: A convergence of key-value storage systems from clouds to supercomputer. Concurrency Comput.: Pract. Experience (2016)Google Scholar
  35. 35.
    Zhao, D., Yang, X., Sadooghi, I., Garzoglio, G., Timm, S., Raicu, I.: High-performance storage support for scientific applications on the cloud. In: Proceedings of the 6th Workshop on Scientific Cloud Computing (ScienceCloud) (2015)Google Scholar
  36. 36.
    Li, T., Keahey, K., Wang, K., Zhao, D., Raicu, I.: A dynamically scalable cloud data infrastructure for sensor networks. In: Proceedings of the 6th Workshop on Scientific Cloud Computing (ScienceCloud) (2015)Google Scholar
  37. 37.
    Raicu, I., Zhao, Y., Foster, I.T., Szalay, A.: Accelerating large-scale data exploration through data diffusion. In: Proceedings of the 2008 International Workshop on Data-aware Distributed Computing (2008)Google Scholar
  38. 38.
    Li, S., Huang, H.H.: Black-box performance modeling for solid-state drives. In: 2010 IEEE International Symposium on Modeling, Analysis Simulation of Computer and Telecommunication Systems (MASCOTS) (2010)Google Scholar
  39. 39.
    Rizvi, S., Chung, T.-S.: Flash SSD vs HDD: High performance oriented modern embedded and multimedia storage systems. In: 2nd International Conference on Computer Engineering and Technology (ICCET) (2010)Google Scholar
  40. 40.
    Chen, F., Koufaty, D.A., Zhang, X.: Hystor: making the best use of solid state drives in high performance storage systems. In: Proceedings of the International Conference on Supercomputing (2011)Google Scholar
  41. 41.
    Guerra, J., Pucha, H., Glider, J., Belluomini, W., Rangaswami, R.: Cost effective storage using extent based dynamic tiering. In: Proceedings of the 9th USENIX Conference on File and Stroage Technologies (2011)Google Scholar
  42. 42.
    Zhang, X., Davis, K., Jiang, S.: iTransformer: using SSD to improve disk scheduling for high-performance I/O. In: Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium (2012)Google Scholar
  43. 43.
    Zhang, X., Ke, L., Davis, K., Jiang, S.: iBridge: improving unaligned parallel file access with solid-state drives. In: Proceedings of the 2013 IEEE 27th International Parallel and Distributed Processing Symposium (2013)Google Scholar
  44. 44.
    Mao, B., Jiang, H., Feng, D., Wu, S., Chen, J., Zeng, L., Tian, L.: HPDA: a hybrid parity-based disk array for enhanced performance and reliability. In: 2010 IEEE International Symposium on Parallel Distributed Processing (IPDPS) (2010)Google Scholar
  45. 45.
    Badam, A., Pai, V.S.: SSDAlloc: hybrid SSD/RAM memory management made easy. In: Proceedings of the 8th USENIX Conference on Networked systems design and implementation (2011)Google Scholar
  46. 46.
    Wang, C., Vazhkudai, S.S., Ma, X., Meng, F., Kim, Y., Engelmann, C.: Nvmalloc: exposing an aggregate ssd store as a memory partition in extreme-scale machines. In: Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium (2012)Google Scholar
  47. 47.
    Wu, X., Narasimha Reddy, A.L.: SCMFS: a file system for storage class memory. In: Proceedings of International Conference for High Performance Computing, Networking, Storage and Analysis (2011)Google Scholar
  48. 48.
    Joo, Y., Ryu, J., Park, S., Shin, K.G.: FAST: quick application launch on solid-state drives. In: Proceedings of the 9th USENIX Conference on File and Stroage Technologies (2011)Google Scholar
  49. 49.
    Yang, Q., Ren, J.: I-CASH: intelligently coupled array of SSD and HDD. In: Proceedings of the 2011 IEEE 17th International Symposium on High Performance Computer Architecture (2011)Google Scholar
  50. 50.
    Fares, R., Romoser, B., Zong, Z., Nijim, M., Qin, X.: Performance evaluation of traditional caching policies on a large system with petabytes of data. In: 2012 IEEE 7th International Conference on Networking, Architecture and Storage (NAS) (2012)Google Scholar
  51. 51.
    Podlipnig, S., Böszörmenyi, L.: A survey of web cache replacement strategies. ACM Comput. Surv. 35(4) (2003)Google Scholar
  52. 52.
    Shi, L., Liu, Z., Xu, L.: Bwcc: a fs-cache based cooperative caching system for network storage system. In: Proceedings of the 2012 IEEE International Conference on Cluster Computing (2012)Google Scholar
  53. 53.
    Wu, C., Xubin, H., Qiang, C., Changsheng, X., Shenggang, W.: Hint-k: an efficient multi-level cache using k-step hints. IEEE Trans. Parallel Distrib. Syst. 99 (2013)Google Scholar
  54. 54.
    Meister, D., Kaiser, J., Brinkmann, A.: Block locality caching for data deduplication. In: Proceedings of the 6th International Systems and Storage Conference (2013)Google Scholar
  55. 55.
    Xia, P., Feng, D., Jiang, H., Tian, L., Wang, F.: Farmer: a novel approach to file access correlation mining and evaluation reference model for optimizing peta-scale file system performance. In: Proceedings of the 17th International Symposium on High Performance Distributed Computing (2008)Google Scholar
  56. 56.
    Lin, J., Lu, Q., Ding, X., Zhang, Z., Zhang, X., Sadayappan, P.: Enabling software management for multicore caches with a lightweight hardware support. In: Proceedings of the 2009 ACM/IEEE Conference on Supercomputing (2009)Google Scholar
  57. 57.
    Zhan, D., Jiang, H., Seth, S.C.: Locality & utility co-optimization for practical capacity management of shared last level caches. In: Proceedings of the 26th ACM International Conference on Supercomputing (2012)Google Scholar
  58. 58.
    Gonzalez-Ferez, P., Piernas, J., Cortes, T.: The ram enhanced disk cache project (redcap). In: Proceedings of the 24th IEEE Conference on Mass Storage Systems and Technologies (2007)Google Scholar
  59. 59.
    Huang, S., Wei, Q., Chen, J., Chen, C., Feng, D.: Improving flash-based disk cache with lazy adaptive replacement. In: 2013 IEEE 29th Symposium on Mass Storage Systems and Technologies (MSST) (2013)Google Scholar
  60. 60.
    Zhu, Z., Zhang, X.: Access-mode predictions for low-power cache design. IEEE Micro 22(2) (2002)Google Scholar
  61. 61.
    Yue, J., Zhu, Y., Cai, Z., Lin, L.: Energy and thermal aware buffer cache replacement algorithm. In: Proceedings of the 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST) (2010)Google Scholar
  62. 62.
    Manzanares, A., Ruan, X., Yin, S., Xie, J., Ding, Z., Tian, Y., Majors, J., Qin, X.: Energy efficient prefetching with buffer disks for cluster file systems. In: Proceedings of the 2010 39th International Conference on Parallel Processing (2010)Google Scholar
  63. 63.
    Li, Z., Wilson, C., Jiang, Z., Liu, Y., Zhao, B., Jin, C., Zhang, Z.L., Dai, Y.: Efficient batched synchronization in dropbox-like cloud storage services. In: Proceedings of the 14th International Middleware Conference (2013)Google Scholar
  64. 64.
    Xu, Y., Xing, C., Zhou, L.: A cache replacement algorithm in hierarchical storage of continuous media object. In: Advances in Web-Age Information Management: 5th International Conference (2004)Google Scholar
  65. 65.
    Li, R., Guo, R., Xu, Z., Feng, W.: A prefetching model based on access popularity for geospatial data in a cluster-based caching system. Int. J. Geogr. Inf. Sci. 26(10) (2012)Google Scholar
  66. 66.
    Qiao, K., Tao, F., Zhang, L., Li, Z.: A ga maintained by binary heap and transitive reduction for addressing psp. In: 2010 International Conference on Intelligent Computing and Integrated Systems (ICISS) (2010)Google Scholar
  67. 67.
    Tao, F., Qiao, K., Zhang, L., Li, Z., Nee, A.: GA-BHTR: an improved genetic algorithm for partner selection in virtual manufacturing. Int. J. Prod. Res. 50(8) (2012)Google Scholar
  68. 68.
    Calinescu, G., Qiao, K.: Asymmetric topology control: exact solutions and fast approximations. In: IEEE International Conference on Computer Communications (INFOCOM ’12) (2012)Google Scholar
  69. 69.
    Calinescu, G., Kapoor, S., Qiao, K., Shin, J.: Stochastic strategic routing reduces attack effects. In: Global Telecommunications Conference (GLOBECOM 2011), 2011. IEEE (2011)Google Scholar
  70. 70.
    Zhao, D,, Yang, L.: Incremental isometric embedding of high-dimensional data using connected neighborhood graphs. IEEE Trans. Pattern Anal. Mach. Intell. 31(1), 86–98 (2009)Google Scholar
  71. 71.
    Lohfert, R., Lu, J., Zhao, D.; Solving sql constraints by incremental translation to sat. In: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (2008)Google Scholar
  72. 72.
    Zhao, D., Yang, L.: Incremental construction of neighborhood graphs for nonlinear dimensionality reduction. In: Proceedings of 18th International Conference on Pattern Recognition, vol. 3, pp. 177–180 (2006)Google Scholar
  73. 73.
    Ferreira, K.B., Riesen, R., Arnold, D., Ibtesham, D., Brightwell, R.: The viability of using compression to decrease message log sizes. In: Proceedings of International Conference on Parallel Processing Workshops (2013)Google Scholar
  74. 74.
    Zerin Islam, T., Mohror, K., Bagchi, S., Moody, A., de Supinski, B.R., Eigenmann, R.: McrEngine: a scalable checkpointing system using data-aware aggregation and compression. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis (SC) (2012)Google Scholar
  75. 75.
    Slim Bouguerra, M., Gainaru, A., Gomez, L.B., Cappello, F., Matsuoka, S., Maruyam, N.: Improving the computing efficiency of hpc systems using a combination of proactive and preventive checkpointing. In: IEEE International Symposium on Parallel Distributed Processing (2013)Google Scholar
  76. 76.
    Noeth, M., Marathe, J., Mueller, F., Schulz, M., de Supinski, B.: Scalable compression and replay of communication traces in massively parallel environments. In: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing (SC) (2006)Google Scholar
  77. 77.
    Laney, D., Langer, S., Weber, C., Lindstrom, P., Wegener, A.: Assessing the effects of data compression in simulations using physically motivated metrics. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis (2013)Google Scholar
  78. 78.
    Lakshminarasimhan, S., Jenkins, J., Arkatkar, I., Gong, Z., Kolla, H., Ku, S.-H., Ethier, S., Chen, J., Chang, C.S., Klasky, S., Latham, R., Ross, R., Samatova, N.F.: ISABELA-QA: query-driven analytics with ISABELA-compressed extreme-scale scientific data. In: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC’11) (2011)Google Scholar
  79. 79.
    MPEG-1: http://en.wikipedia.org/wiki/MPEG-1. Accessed 5 Sept 2014
  80. 80.
    Bicer, T., Yin, J., Chiu, D., Agrawal, G., Schuchardt, K.: Integrating online compression to accelerate large-scale data analytics applications. In: Proceedings of the 2013 IEEE 27th International Symposium on Parallel and Distributed Processing (IPDPS) (2013)Google Scholar
  81. 81.
    Schendel, E.R., Pendse, S.V., Jenkins, J., Boyuka, D.A., II, Gong, Z., Lakshminarasimhan, S., Liu, Q., Kolla, H., Chen, J., Klasky, S.,Ross, R., Samatova, N.F.: Isobar hybrid compression-i/o interleaving for large-scale parallel i/o optimization, In: Proceedings of International Symposium on High-Performance Parallel and Distributed Computing (2012)Google Scholar
  82. 82.
    Jenkins, J., Schendel, E.R., Lakshminarasimhan, S., Boyuka, D.S., II, Rogers, T., Ethier, S., Ross, R., Klasky, S., Samatova, N.F.: Byte-precision level of detail processing for variable precision analytics. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis (SC) (2012)Google Scholar
  83. 83.
    Burrows, M., Jerian, C., Lampson, B., Mann, T.: On-line data compression in a log-structured file system. In: Proceedings of the Fifth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) (1992)Google Scholar
  84. 84.
    Joshua, P.: MacDonald. File system support for delta compression. Technical report, University of California, Berkley (2000)Google Scholar
  85. 85.
    Olson, M.A., Bostic, K., Seltzer M.: db. In: Proceedings of the Annual Conference on USENIX Annual Technical Conference (1999)Google Scholar
  86. 86.
    Edel, N.K., Tuteja, D., Miller, E.L., Brandt S.A.: Mramfs: a compressing file system for non-volatile ram. In: Proceedings of the the IEEE Computer Society’s 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems (MASCOTS) (2004)Google Scholar
  87. 87.
    Muthitacharoen, A., Chen, B., Mazières, D.: A low-bandwidth network file system. In: Proceedings of the Eighteenth ACM Symposium on Operating Systems Principles (SOSP) (2001)Google Scholar
  88. 88.
    Park, K.S., Ihm, S., Bowman, M., Pai, V.S.: Supporting practical content-addressable caching with czip compression. In: 2007 USENIX Annual Technical Conference (2007)Google Scholar
  89. 89.
    Meister, D., Brinkmann, A., Süß, T.: File recipe compression in data deduplication systems. In: Proceedings of the 11th USENIX Conference on File and Storage Technologies (FAST) (2013)Google Scholar
  90. 90.
    Lakshminarasimhan, S., Boyuka, D.A., Pendse, S.V., Zou, X., Jenkins, J., Vishwanath, V., Papka, M.E., Samatova, N.F.: Scalable in situ scientific data encoding for analytical query processing. In: Proceedings of the 22nd International Symposium on High-performance Parallel and Distributed Computing (HPDC) (2013)Google Scholar
  91. 91.
    Gong, Z., Lakshminarasimhan, S., Jenkins, J., Kolla, H., Ethier, S., Chen, J., Ross, R., Klasky, S., Samatova, N.F.: Multi-level layout optimization for efficient spatio-temporal queries on isabela-compressed data. In: Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium (IPDPS) (2012)Google Scholar
  92. 92.
    Shnaiderman, L., Shmueli, O.: A parallel twig join algorithm for XML processing using a GPGPU. In: International Workshop on Accelerating Data Management Systems Using Modern Processor and Storage Architectures (2012)Google Scholar
  93. 93.
    Wang, H., Potluri, S., Bureddy, D., Rosales, C., Panda, D.K.: Gpu-aware mpi on rdma-enabled clusters: design, implementation and evaluation. IEEE Trans. Parallel Distrib. Syst. 25(10) (2014)Google Scholar
  94. 94.
    Bordawekar, R., Bondhugula, U., Rao. R.: Believe it or not!: mult-core cpus can match gpu performance for a flop-intensive application! In: Proceedings of the 19th International Conference on Parallel Architectures and Compilation Techniques, PACT ’10, (2010)Google Scholar
  95. 95.
    Farooqui, N., Schwan, K., Yalamanchili, S.: Efficient instrumentation of gpgpu applications using information flow analysis and symbolic execution. In: Proceedings of Workshop on General Purpose Processing Using GPUs, GPGPU-7 (2014)Google Scholar
  96. 96.
    Muniswamy-Reddy, K.-K.: Foundations for provenance-aware systems (2010)Google Scholar
  97. 97.
    Foster, I.T., Vckler, J.S., Wilde, M., Zhao, Y.: The virtual data grid: a new model and architecture for data-intensive collaboration. In: CIDR’03 (2003)Google Scholar
  98. 98.
    Provenance aware service oriented architecture. http://twiki.pasoa.ecs.soton.ac.uk/bin/view/PASOA/WebHome. Accessed 6 July 2015
  99. 99.
    Parker-Wood, A., Long, D.D.E., Miller, E.L., Seltzer, M., Tunkelang, D.: Making sense of file systems through provenance and rich metadata. Technical Report UCSC-SSRC-12-01, University of California, Santa Cruz, March 2012Google Scholar
  100. 100.
    Muniswamy-Reddy, K.-K., Holland, D.A., Braun, U., Seltzer, M.: Provenance-aware storage systems. In: Proceedings of the annual conference on USENIX ’06 Annual Technical Conference (2006)Google Scholar
  101. 101.
    Muniswamy-Reddy, K.-K., Macko, P., Seltzer, M.: Making a cloud provenance-aware. In: 1st Workshop on the Theory and Practice of Provenance (2009)Google Scholar
  102. 102.
    Muniswamy-Reddy, K.-K., Braun, U., Holland, D.A., Macko, P., Maclean, D., Margo, D., Seltzer, M., Smogor, R.: Layering in provenance systems. In: Proceedings of the 2009 USENIX Annual Technical Conference (2009)Google Scholar
  103. 103.
    Gehani, A., Tariq, D.: SPADE: support for provenance auditing in distributed environments. In: Proceedings of the 13th International Middleware Conference (2012)Google Scholar
  104. 104.
    Zhou, W., Sherr, M., Tao, T., Li, X., Thau Loo, B., Mao, Y.: Efficient querying and maintenance of network provenance at internet-scale. In: Proceedings of the 2010 International Conference on Management of Data, pp. 615–626 (2010)Google Scholar
  105. 105.
    Abraham, J., Brazier, P., Chebotko, A., Navarro, J., Piazza, A.: Distributed storage and querying techniques for a semantic web of scientific workflow provenance. In: 2010 IEEE International Conference on Services Computing (SCC), pp. 178–185. IEEE (2010)Google Scholar
  106. 106.
    Malik, T., Gehani, A., Tariq, D., Zaffar, F.: Sketching distributed data provenance. In: Data Provenance and Data Management in eScience, pp. 85–107 (2013)Google Scholar
  107. 107.
    Heinis, T., Alonso, G.: Efficient lineage tracking for scientific workflows. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1007–1018 (2008)Google Scholar
  108. 108.
    Extensible Markup Language (XML): http://www.w3.org/xml/. Accessed 13 Dec 2014
  109. 109.
    JSON: http://www.json.org/. Accessed 8 Dec 2014
  110. 110.
    Binary JSON: http://bsonspec.org/. Accessed 13 Dec 2014
  111. 111.
    Apache Thrift: https://thrift.apache.org/. Accessed 8 Dec 2014
  112. 112.
    Apache Avro: http://avro.apache.org/. Accessed 13 Dec 2014
  113. 113.
    Apache Etch: https://etch.apache.org/. Accessed 13 Dec 2014
  114. 114.
    BERT: http://bert-rpc.org/. Accessed 13 Dec 2014
  115. 115.
    Message Pack: http://msgpack.org/. Accessed 13 Dec 2014
  116. 116.
    Hessian: http://hessian.caucho.com/. Accessed 13 Dec 2014
  117. 117.
    ICE: http://doc.zeroc.com/display/ice34/home. Accessed 13 Dec 2014
  118. 118.
    CBOR: http://cbor.io/. Accessed 13 Dec 2014
  119. 119.
    Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. In: Proceedings of USENIX Symposium on Opearting Systems Design & Implementation (2004)Google Scholar
  120. 120.
    Apache Hadoop: http://hadoop.apache.org/. Accessed 5 Sept 2014
  121. 121.
    Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. In: Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing (2010)Google Scholar
  122. 122.
    MPICH: http://www.mpich.org/. Accessed 10 Dec 2014
  123. 123.
    Open MPI: http://www.open-mpi.org/. Accessed 10 Dec 2014
  124. 124.
    OpenMP: http://openmp.org/wp/. Accessed 9 Dec 2014
  125. 125.
  126. 126.
    Jeon, M., He, Y., Elnikety, S., Cox, A.L., Rixner, S.: Adaptive parallelism for web search. In: Proceedings of the 8th ACM European Conference on Computer Systems, EuroSys ’13 (2013)Google Scholar
  127. 127.
    Jeon, M., Kim, S., Hwang, S., He, Y., Elnikety, S., Cox, A.L., Rixner, S.: Predictive parallelization: taming tail latencies in web search. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR ’14 (2014)Google Scholar
  128. 128.
    Lee, J., Winslett, M., Ma, X., Yu, S.: Enhancing data migration performance via parallel data compression. In: Proceedings of the 16th International Parallel and Distributed Processing Symposium, IPDPS ’02 (2002)Google Scholar
  129. 129.
    Klasky, S., Ethier, S., Lin, Z., Martins, K., McCune, D., Samtaney, R.: Grid-based parallel data streaming implemented for the gyrokinetic toroidal code. In: Proceedings of the 2003 ACM/IEEE Conference on Supercomputing, SC ’03 (2003)Google Scholar
  130. 130.
    Warneke, D., Kao, O.: Nephele: efficient parallel data processing in the cloud. In: Proceedings of the 2nd Workshop on Many-Task Computing on Grids and Supercomputers, MTAGS ’09 (2009)Google Scholar
  131. 131.
    Yu, Y., Isard, M., Fetterly, D., Budiu, M., Erlingsson, U., Gunda, P.K., Currey, J.: Dryadlinq: a system for general-purpose distributed data-parallel computing using a high-level language. In: Proceedings of the 8th USENIX Conference on Operating Systems Design and Implementation, OSDI’08 (2008)Google Scholar
  132. 132.
    Ronnie, C., Bob, J., Per-Ake, L., Bill, R., Darren, S., Simon, W., Jingren, Z.: Scope: easy and efficient parallel processing of massive data sets. Proc. VLDB Endow. 1(2), 1265–1276 (2008)Google Scholar
  133. 133.
    Ahrens, J., Brislawn, K., Martin, K., Geveci, B., Charles Law, C., Papka, M.: Large-scale data visualization using parallel data streaming. In: Computer Graphics and Applications. IEEE, 21(4), July 2001Google Scholar
  134. 134.
    Allen, M.D., Sridharan, S., Sohi, G.S.: Serialization sets: a dynamic dependence-based parallel execution model. In: Proceedings of the 14th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP ’09 (2009)Google Scholar
  135. 135.
    Voss, M., Eigenmann, R.: Reducing parallel overheads through dynamic serialization. In: Proceedings of the 13th International Symposium on Parallel Processing and the 10th Symposium on Parallel and Distributed Processing, IPPS ’99/SPDP ’99 (1999)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Dongfang Zhao
    • 1
    Email author
  • Akash Mahakode
    • 2
  • Sandip Lakshminarasaiah
    • 2
  • Ioan Raicu
    • 2
  1. 1.University of WashingtonSeattleUSA
  2. 2.Illinois Institute of TechnologyChicagoUSA

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