Skip to main content

Automatic Communication Performance Debugging in PGAS Languages

  • Conference paper
Languages and Compilers for Parallel Computing (LCPC 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5234))

Abstract

Recent studies have shown that programming in a Partition Global Address Space (PGAS) language can be more productive than programming in a message passing model. One reason for this is the ability to access remote memory implicitly through shared memory reads and writes. But this benefit does not come without a cost. It is very difficult to spot communication by looking at the program text, since remote reads and writes look exactly the same as local reads and writes. This makes manual communication performance debugging an arduous task. In this paper, we describe a tool called ti-trend-prof that can do automatic performance debugging using only program traces from small processor configurations and small input sizes in Titanium [13], a PGAS language. ti-trend-prof presents trends to the programmer to help spot possible communication performance bugs even for processor configurations and input sizes that have not been run. We used ti-trend-prof on two of the largest Titanium applications and found bugs that would have taken days in under an hour.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alexandrov, A., Ionescu, M.F., Schauser, K.E., Scheiman, C.: LogGP: Incorporating long messages into the LogP model. Journal of Parallel and Distributed Computing 44(1), 71–79 (1997)

    Article  Google Scholar 

  2. Bonachea, D.: GASNet specifications (2003)

    Google Scholar 

  3. Brewer, E.A.: High-level optimization via automated statistical modeling. In: PPOPP 1995: Proceedings of the 5th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, pp. 80–91. ACM Press, New York (1995)

    Chapter  Google Scholar 

  4. Coarfa, C., Mellor-Crummey, J., Froyd, N., Dotsenko, Y.: Scalability Analysis of SPMD Codes Using Expectations. In: PPOPP (2007)

    Google Scholar 

  5. Culler, D.E., Karp, R.M., Patterson, D.A., Sahay, A., Schauser, K.E., Santos, E., Subramonian, R., von Eicken, T.: LogP: Towards a realistic model of parallel computation. In: Proceedings 4th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, pp. 1–12 (1993)

    Google Scholar 

  6. Givelberg, E., Yelick, K.: Distributed Immersed Boundary Simulation in Titanium (2004)

    Google Scholar 

  7. Goldsmith, S., Aiken, A., Wilkerson, D.: Measuring Empirical Computational Complexity. Foundations of Software Engineering (2007)

    Google Scholar 

  8. Graham, S.L., Kessler, P.B., Mckusick, M.K.: Gprof: A call graph execution profiler. In: SIGPLAN 1982: Proceedings of the 1982 SIGPLAN Symposium on Compiler Construction, pp. 120–126. ACM Press, New York (1982)

    Chapter  Google Scholar 

  9. Kluge, M., Knüpfer, A., Nagel, W.E.: Knowledge based automatic scalability analysis and extrapolation for MPI programs. In: Cunha, J.C., Medeiros, P.D. (eds.) Euro-Par 2005. LNCS, vol. 3648. Springer, Heidelberg (2005)

    Google Scholar 

  10. Rugina, R., Schauser, K.: Predicting the running times of parallel programs by simulation. In: Proceedings of the 12th International Parallel Processing Symposium and 9th Symposium on Parallel and Distributed Processing (1998)

    Google Scholar 

  11. Vetter, J., Worley, P.: Asserting performance expectations. In: SC (2002)

    Google Scholar 

  12. Wen, T., Colella, P.: Adaptive Mesh Refinement in Titanium. In: IPDPS (2005)

    Google Scholar 

  13. Yelick, K., Semenzato, L., Pike, G., Miyamoto, C., Liblit, B., Krishnamurthy, A., Hilfinger, P., Graham, S., Gay, D., Colella, P., Aiken, A.: Titanium: A high-performance Java dialect. In: Workshop on Java for High-Performance Network Computing (1998)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Vikram Adve María Jesús Garzarán Paul Petersen

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Su, J., Yelick, K. (2008). Automatic Communication Performance Debugging in PGAS Languages. In: Adve, V., Garzarán, M.J., Petersen, P. (eds) Languages and Compilers for Parallel Computing. LCPC 2007. Lecture Notes in Computer Science, vol 5234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85261-2_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-85261-2_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85260-5

  • Online ISBN: 978-3-540-85261-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics