By Pan, Wu
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Additional resources for A Novel Data Hiding Method for Two-Color Images
Evidence-Based Static Branch Prediction Using Machine Learning. In: ACM Transactions on Programming Languages and Systems. Volume 19. : Mappping Reference Code to Irregular DSPs with the Retargetable, Optimizing Compiler COGEN(T). In: International Symposium on Microarchitecture. Volume 34. edu Abstract. Program profiling can help performance prediction and compiler optimization. This paper describes the initial work behind TFP, a new profiling strategy that can gather and verify a range of flow-specific information at runtime.
First, our learning representation allows for conditional execution of passes, while theirs does not. In addition, we differ in the end goal; because they were targeting embedded microprocessors, they based fitness on code size. While this is a legitimate metric, code size is not a big issue for parallel architectures, nor does it necessarily correlate with wall clock performance. We also simultaneously train on multiple benchmarks to create general-purpose solutions. They use the application-specific sequences to hand-craft a general-purpose solution.
Thus we make up for not being able to store the bit stream corresponding 1 We did not consider some trivial two block loops having just a single path. Also for eon and some FP benchmarks we considered less than 10 back edges as there was a significant drop in the frequencies of the remaining ones. TFP: Time-Sensitive, Flow-Specific Profiling at Runtime 41 to a path in a single variable by maintaining parts of the bit stream in separate variables. However, is rarely more than 1 (of the 110 instrumented regions only one had more than 64 blocks in it).
A Novel Data Hiding Method for Two-Color Images by Pan, Wu