Proceedings of the 29th annual ACM/IEEE international symposium on Microarchitecture
Exploiting hardware performance counters with flow and context sensitive profiling
Proceedings of the ACM SIGPLAN 1997 conference on Programming language design and implementation
Continuous profiling: where have all the cycles gone?
Proceedings of the sixteenth ACM symposium on Operating systems principles
ProfileMe: hardware support for instruction-level profiling on out-of-order processors
MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
Predicting data cache misses in non-numeric applications through correlation profiling
MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
Improving data-flow analysis with path profiles
PLDI '98 Proceedings of the ACM SIGPLAN 1998 conference on Programming language design and implementation
Proceedings of the ACM SIGPLAN 1999 conference on Programming language design and implementation
A framework for reducing the cost of instrumented code
Proceedings of the ACM SIGPLAN 2001 conference on Programming language design and implementation
Workload Design: Selecting Representative Program-Input Pairs
Proceedings of the 2002 International Conference on Parallel Architectures and Compilation Techniques
Interprocedural Path Profiling
CC '99 Proceedings of the 8th International Conference on Compiler Construction, Held as Part of the European Joint Conferences on the Theory and Practice of Software, ETAPS'99
Preferential path profiling: compactly numbering interesting paths
Proceedings of the 34th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
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Current profiling techniques are good at identifying where time is being spent during program execution. These techniques are not as good at pinpointing exactly where in the execution there are de.nite opportunities a programmer can exploit with optimization. In this paper we present a new type of profiling analysis called Variational Path Profiling (VPP). VPP pinpoints exactly where in the program there are potentially significant optimization opportunities for speedup. VPP finds the acyclic control flow paths that vary the most in execution time (the time it takes to execute each occurrence of the path). This is calculated by sampling the time it takes to execute frequent paths using hardware performance counters. The motivation for concentrating on a path with a high net variation in its execution time is that it can potentially be optimized so that most or all executions of that path have the minimal execution time seen during profiling. We present a profiling and analysis approach to find these variational paths, so that they can be communicated back to a programmer to guide optimization. Our results show that this variation accounts for a signi.cant fraction of overall program execution time and a small number of paths account for a large fraction of this variation. By applying straight forward prefetching optimizations to these variational paths we see 8.5% speedups on average.