Alchemist: A Transparent Dependence Distance Profiling Infrastructure
Proceedings of the 7th annual IEEE/ACM International Symposium on Code Generation and Optimization
The Paralax infrastructure: automatic parallelization with a helping hand
Proceedings of the 19th international conference on Parallel architectures and compilation techniques
Estimating and exploiting potential parallelism by source-level dependence profiling
EuroPar'10 Proceedings of the 16th international Euro-Par conference on Parallel processing: Part I
QUAD: a memory access pattern analyser
ARC'10 Proceedings of the 6th international conference on Reconfigurable Computing: architectures, Tools and Applications
Efficient and accurate data dependence profiling using software signatures
Proceedings of the Tenth International Symposium on Code Generation and Optimization
Multi-slicing: a compiler-supported parallel approach to data dependence profiling
Proceedings of the 2012 International Symposium on Software Testing and Analysis
Sambamba: runtime adaptive parallel execution
Proceedings of the 3rd International Workshop on Adaptive Self-Tuning Computing Systems
Online dynamic dependence analysis for speculative polyhedral parallelization
Euro-Par'13 Proceedings of the 19th international conference on Parallel Processing
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With the proliferation of multicore processors, there is an urgent need for tools and methodologies supporting parallelization of existing applications. In this paper, we present a novel tool for aiding programmers in parallelizing programs. The tool, Embla, is based on the Valgrind framework, and allows the user to discover the data dependences in a sequential program, thereby exposing opportunities for parallelization. Embla performs an off-line dynamic analysis, and records dependences as they arise during program execution. It reports an optimistic view of parallelizable sequences, and ignores dependences that do not arise during execution. Moreover, since the tool instruments the machine code of the program, it is largely language independent. Since Embla finds the dependencies that occur for particular executions, the confidence one would assign to its results depend on whether different executions yield different (bad) or largely the same (good) dependencies. We present a preliminary investigation into this issue using 84 different inputs to the SPEC CPU 2006 benchmark 403.gcc. The results indicate that there is a strong correlation between coverage and finding dependencies; executing the entire program is likely to reveal all dependencies.