Trends in Computation, Communication and Storage and the Consequences for Data-intensive Science

The way we are doing science is changing: Data analysis and computation modeling became tightly coupled. Divergent technological trends for computer processors, storage and memory and communication systems showed to be a real challenge in performance of current computing systems. In this paper we analyze the trends that influence computer performance, point out the technical challenges and introduce our vision in developing a guideline to an optimum distribution of computer resources addressing primarily data transmission issues.

[1]  Alexander S. Szalay,et al.  Extreme Data-Intensive Scientific Computing , 2011, Computing in Science & Engineering.

[2]  David A. Patterson,et al.  Computer Architecture - A Quantitative Approach (4. ed.) , 2007 .

[3]  Weiqin Tong,et al.  Current trends in high performance computing and its applications : proceedings of the International Conference on High Performance Computing and Applications, August 8-10, 2004, Shanghai, P.R. China , 2005 .

[4]  S. Dosanjh,et al.  Architectures and Technology for Extreme Scale Computing Report from the Workshop Node Architecture and Power Reduction Strategies , 2011 .

[5]  David J. DeWitt,et al.  Scientific data management in the coming decade , 2005, SGMD.

[6]  Anne E. Trefethen,et al.  The Data Deluge: An e-Science Perspective , 2003 .

[7]  Kiyoo Itoh.  The History of DRAM Circuit Designs – At the Forefront of DRAM Development – , 2008, IEEE Solid-State Circuits Newsletter.

[8]  Vladimir Getov,et al.  Codesign for Systems and Applications: Charting the Path to Exascale Computing , 2011, Computer.

[9]  Werner Vogels,et al.  Dynamo: amazon's highly available key-value store , 2007, SOSP.

[10]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[11]  Haralampos Pozidis,et al.  Trends in Storage Technologies , 2010, IEEE Data Eng. Bull..

[12]  Tony Hey,et al.  The Fourth Paradigm: Data-Intensive Scientific Discovery , 2009 .

[13]  Randal E. Bryant,et al.  Data-Intensive Scalable Computing for Scientific Applications , 2011, Computing in Science & Engineering.

[14]  Dean N. Williams,et al.  Data-Intensive Science in the US DOE: Case Studies and Future Challenges , 2011, Computing in Science & Engineering.

[15]  Scott Mueller,et al.  Upgrading and Repairing PCs , 1995 .

[16]  G. Bruce Berriman,et al.  How Will Astronomy Archives Survive the Data Tsunami? , 2011, ACM Queue.

[17]  J. Manyika.  Big data: The next frontier for innovation, competition, and productivity , 2011 .

[18]  Stephen Berard,et al.  Implications of Historical Trends in the Electrical Efficiency of Computing , 2011, IEEE Annals of the History of Computing.

[19]  Adam Jacobs,et al.  The pathologies of big data , 2009, Commun. ACM.

[20]  Alexander S. Szalay,et al.  Big Data [Guest editorial] , 2011, Comput. Sci. Eng..

[21]  David A. Patterson,et al.  Computer Architecture: A Quantitative Approach , 1969 .

[22]  Martin Hilbert,et al.  The World’s Technological Capacity to Store, Communicate, and Compute Information , 2011, Science.

[23]  M.H. Kryder,et al.  After Hard Drives—What Comes Next? , 2009, IEEE Transactions on Magnetics.

[24]  David D. Redell,et al.  Evolution of the Ethernet Local Computer Network , 1982, Computer.

[25]  Prashant J. Shenoy,et al.  Rules of thumb in data engineering , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[26]  John May,et al.  Parallel I/O for High Performance Computing , 2000 .

[27]  Samuel Williams,et al.  Roofline: an insightful visual performance model for multicore architectures , 2009, CACM.

[28]  GhemawatSanjay,et al.  The Google file system , 2003 .