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International Conference on High Performance Computing

ISC High Performance 2017: High Performance Computing pp 106–114Cite as

Eliminating Dark Bandwidth: A Data-Centric View of Scalable, Efficient Performance, Post-Moore

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10524))

Abstract

Most of computing research has focused on the computing technologies themselves versus how full systems make use of them (e.g., memory fabric, interconnect, software, and compute elements combined). Technologists have largely failed to look at the compute system as a whole, instead optimizing subsystems mostly in isolation. The result, for example, is that systems are built where applications can only ask for a fixed multiple of data (e.g., 64-bytes from DRAM), even if what is required is far less. This is efficient from a hardware interface perspective, however, it results in consuming valuable bandwidth that is never utilized by the core; this hidden bandwidth is effectively dark to the system. The causes of dark bandwidth are systemic, built into the very core of our virtual memory abstractions and memory interfaces. Continued focus on newer, revolutionary memory technologies to improve surface performance characteristics without a systems focus on reducing data movement will simply push this problem off onto future systems. This paper examines the problem of dark bandwidth and offers a holistic approach to reduce overall data movement within future compute systems.

J. Randall—Partially supported by U.S. DoE FastForward-2 Contract - Subcontract No. B609229.

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References

  1. Data Movement Dominates. https://goo.gl/rro35D. Accessed Mar 2017

  2. Dongarra, J., Heroux, M.A.: Toward a new metric for ranking high performance computing systems. Sandia Report, SAND2013-4744 312 (2013)

    Google Scholar 

  3. Dongarra, J.J., Moler, C.B., Bunch, J.R., Stewart, G.W.: LINPACK Users’ Guide. Society for Industrial and Applied Mathematics, Philadelphia (1979)

    Book  MATH  Google Scholar 

  4. Henning, J.L.: SPEC CPU2006 benchmark descriptions. ACM SIGARCH Comput. Architect. News 34(4), 1–17 (2006)

    Article  Google Scholar 

  5. Jacob, B., Ng, S., Wang, D.: Memory Systems: Cache, DRAM, Disk. Morgan Kaufmann (2010)

    Google Scholar 

  6. Karakostas, V., Gandhi, J., Cristal, A., Hill, M.D., McKinley, K.S., Nemirovsky, M., Swift, M.M., Unsal, O.S.: Energy-efficient address translation. In: 2016 IEEE International Symposium on High Performance Computer Architecture (HPCA), pp. 631–643. IEEE (2016)

    Google Scholar 

  7. Kestor, G., Gioiosa, R., Kerbyson, D.J., Hoisie, A.: Quantifying the energy cost of data movement in scientific applications. In: 2013 IEEE International Symposium on Workload Characterization (IISWC) (2013)

    Google Scholar 

  8. Lloyd, S., Gokhale, M.: In-memory data rearrangement for irregular, data-intensive computing. Computer 48(8), 18–25 (2015). doi:10.1109/MC.2015.230

    Article  Google Scholar 

  9. Markov, I.L.: Limits on fundamental limits to computation. Nature 512(7513), 147–154 (2014)

    Article  Google Scholar 

  10. Srinivasan, J.R.: Improving cache utilisation. Technical report, University of Cambridge, Computer Laboratory (2011)

    Google Scholar 

  11. Vesely, J., Basu, A., Oskin, M., Loh, G.H., Bhattacharjee, A.: Observations and opportunities in architecting shared virtual memory for heterogeneous systems. In: 2016 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), pp. 161–171. IEEE (2016)

    Google Scholar 

  12. Wang, L., Zhan, J., Luo, C., Zhu, Y., Yang, Q., He, Y., Gao, W., Jia, Z., Shi, Y., Zhang, S., et al.: Bigdatabench: a big data benchmark suite from internet services. In: 2014 IEEE 20th International Symposium on High Performance Computer Architecture (HPCA), pp. 488–499. IEEE (2014)

    Google Scholar 

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Author information

Authors and Affiliations

  1. ARM Research, Austin, TX, USA

    Jonathan C. Beard & Joshua Randall

Authors
  1. Jonathan C. Beard
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  2. Joshua Randall
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Corresponding author

Correspondence to Jonathan C. Beard .

Editor information

Editors and Affiliations

  1. Deutsches Klimarechenzentrum (DKRZ), Hamburg, Hamburg, Germany

    Julian M. Kunkel

  2. TITECH, Tokyo, Japan

    Rio Yokota

  3. Department of Computer Science, University of Delaware, Newark, Delaware, USA

    Michela Taufer

  4. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    John Shalf

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© 2017 Springer International Publishing AG

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Beard, J.C., Randall, J. (2017). Eliminating Dark Bandwidth: A Data-Centric View of Scalable, Efficient Performance, Post-Moore. In: Kunkel, J., Yokota, R., Taufer, M., Shalf, J. (eds) High Performance Computing. ISC High Performance 2017. Lecture Notes in Computer Science(), vol 10524. Springer, Cham. https://doi.org/10.1007/978-3-319-67630-2_9

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  • DOI: https://doi.org/10.1007/978-3-319-67630-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67629-6

  • Online ISBN: 978-3-319-67630-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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