Automatic Communication Performance Debugging in PGAS Languages

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.

[1]  Ramesh Subramonian,et al.  LogP: towards a realistic model of parallel computation , 1993, PPOPP '93.

[2]  Chris J. Scheiman,et al.  LogGP: incorporating long messages into the LogP model—one step closer towards a realistic model for parallel computation , 1995, SPAA '95.

[3]  Nathan Froyd,et al.  Scalability analysis of SPMD codes using expectations , 2007, ICS '07.

[4]  Katherine A. Yelick,et al.  Titanium: A High-performance Java Dialect , 1998, Concurr. Pract. Exp..

[5]  Katherine A. Yelick,et al.  Distributed Immersed Boundary Simulation in Titanium , 2006, SIAM J. Sci. Comput..

[6]  Simon Goldsmith,et al.  Measuring empirical computational complexity , 2007, ESEC-FSE '07.

[7]  Wolfgang E. Nagel,et al.  Knowledge Based Automatic Scalability Analysis and Extrapolation for MPI Programs , 2005, Euro-Par.

[8]  Eric A. Brewer,et al.  High-level optimization via automated statistical modeling , 1995, PPOPP '95.

[9]  Paul D. Gader,et al.  Image algebra techniques for parallel image processing , 1987 .

[10]  Phillip Colella,et al.  Adaptive mesh refinement in Titanium , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

[11]  Klaus E. Schauser,et al.  Predicting the running times of parallel programs by simulation , 1998, Proceedings of the First Merged International Parallel Processing Symposium and Symposium on Parallel and Distributed Processing.

[12]  Susan L. Graham,et al.  Gprof: A call graph execution profiler , 1982, SIGPLAN '82.

[13]  Jeffrey S. Vetter,et al.  Asserting Performance Expectations , 2002, ACM/IEEE SC 2002 Conference (SC'02).

[14]  Stephen Gilmore,et al.  Flexible Skeletal Programming with eSkel , 2005, Euro-Par.

[15]  E. Givelberg Immersed boundary simulation of the cochlea: recent results , 2003 .