Predicting conditional branch directions from previous runs of a program
ASPLOS V Proceedings of the fifth international conference on Architectural support for programming languages and operating systems
Profile-driven instruction level parallel scheduling with application to super blocks
Proceedings of the 29th annual ACM/IEEE international symposium on Microarchitecture
Partial dead code elimination using slicing transformations
Proceedings of the ACM SIGPLAN 1997 conference on Programming language design and implementation
A new algorithm for partial redundancy elimination based on SSA form
Proceedings of the ACM SIGPLAN 1997 conference on Programming language design and implementation
Improving data-flow analysis with path profiles
PLDI '98 Proceedings of the ACM SIGPLAN 1998 conference on Programming language design and implementation
Better global scheduling using path profiles
MICRO 31 Proceedings of the 31st annual ACM/IEEE international symposium on Microarchitecture
Overcoming the challenges to feedback-directed optimization (Keynote Talk)
DYNAMO '00 Proceedings of the ACM SIGPLAN workshop on Dynamic and adaptive compilation and optimization
Path Profile Guided Partial Dead Code Elimination Using Predication
PACT '97 Proceedings of the 1997 International Conference on Parallel Architectures and Compilation Techniques
Path Profile Guided Partial Redundancy Elimination Using Speculation
ICCL '98 Proceedings of the 1998 International Conference on Computer Languages
Updating formulae and a pairwise algorithm for computing sample variances
Updating formulae and a pairwise algorithm for computing sample variances
LLVM: A Compilation Framework for Lifelong Program Analysis & Transformation
Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization
All of Statistics: A Concise Course in Statistical Inference
All of Statistics: A Concise Course in Statistical Inference
Automatic speculative parallelization of loops using polyhedral dependence analysis
Proceedings of the First International Workshop on Code OptimiSation for MultI and many Cores
| Hi-index | 0.00 |
Feedback-directed optimization (FDO) depends on profiling information that is representative of a typical execution of a given application. For most applications of interest, multiple data inputs need to be used to characterize the typical behavior of the program. Thus, profiling information from multiple runs of the program needs to be combined. We are working on a new methodology to produce statistically sound combined profiles from multiple runs of a program. This paper presents the motivation for combined profiling (CP), the requirements for a practical and useful methodology to combine profiles, and introduces the principal ideas under development for the creation of this methodology. We are currently working on implementations of CP in both the LLVM compiler and the IBM XL suite of compilers.