optimizing code in compilers using parallel genetic algorithm
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The document discusses optimizing code using a parallel genetic algorithm approach. It begins with introductions to compiler optimization, optimization levels in GNU GCC, and the challenges of phase ordering. It then describes the methodology which uses a master-slave model to evaluate populations of optimization flag combinations in parallel. Experimental results show the parallel genetic algorithm approach improves performance over random optimization or no optimization. In conclusion, this approach is well-suited to the compiler optimization problem and showed increased performance with more processor cores.
![Introduction Background Methodology Experimental results
Optimizing Code using Parallel Genetic Algorithm93/3/5
References
[1] Satish Kumar T., Sakthivel S. and Sushil Kumar S., “Optimizing Code by Selecting Compiler Flags
using Parallel Genetic Algorithm on Multicore CPUs,” International Journal of Engineering and
Technology, Vol. 32, No. 5, 2014.
[2] Prathibha B., Sarojadevi H., Harsha P., “Compiler Optimization: A Genetic Algorithm Approach,”
International Journal of Computer Applications, Vol. 112, No.10, 2015.](https://image.slidesharecdn.com/pgasam-151202105749-lva1-app6891/85/optimizing-code-in-compilers-using-parallel-genetic-algorithm-29-320.jpg)
optimizing code in compilers using parallel genetic algorithm
- 2. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 An overview • Introduction • Background • Methodology • Experimental results • Conclusion 2/22
- 3. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 Compiler optimization • Compiler optimization is the technique of minimizing or maximizing some features of an executable code by tuning the output of a compiler. • Modern compilers support many different optimization phases and these phases should analyze the code and should produce semantically equivalent performance enhanced code. • The three vital parameters defining enhancement of the performance are: Executiontime Sizeofcode Introduction 4/22
- 4. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 • The compiler optimization phase ordering not only possesses challenges to compiler developer but also for multithreaded programmer to enhance the performance of Multicore systems. • Many compilers have numerous optimization techniques which are applied in predetermined ordering. • These ordering of optimization techniques may not always give an optimal code. CODE Search Space Introduction The phase ordering 4/22
- 5. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 Optimization flags • The variation in optimization phase ordering depends on the application that is compiled, the architecture of the machine on which it runs and the compiler implementation. • Many compilers allow optimization flags to be set by the users. • Turning on optimization flags makes the compiler attempt to improve the performance and code size at the expense of compilation time. Introduction 5/22
- 6. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 GNU compiler collection • The GNU Compiler Collection (GCC) includes front ends for C, C++, Objective C, Fortran, Java, Ada, and Go, as well as libraries for these languages • In order to control compilation-time and compiler memory usage, and the trade-offs between speed and space for the resulting executable, GCC provides a range of general optimization levels, numbered from 0–3, as well as individual options for specific types of optimization. O3 O2 O1 Background 6/22
- 7. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 Optimization levels • The impact of the different optimization levels on the input code is as described below: -O0 or no-O (default) Optimization Easy bug elimination Background 7/22
- 8. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 Optimization levels 1. Less compile time. 2. smaller and faster executable code. A lot of simple optimizations eliminates redundancy -O1 or -O Background 8/22
- 9. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 Optimization levels • Only optimizations that do not require any speed-space tradeoffs are used, so the executable should not increase in size. -O2 1. maximum optimization without increasing the executable size O1+ additional optimizations instruction scheduling 1. More compile time. 2. More memory usage.the best choice for deployment of a program Background 8/22
- 10. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 Optimization levels -O3 1. faster executable code 2. Maximum Loop optimization O1+ O2+ more expensive optimizations function inlining 1. Bulky code Background 8/22
- 11. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 Optimization level Background 9/22
- 12. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 The challenge sequential quick sort Parallel quick sort Which optimization level?? overhead of inter-process communication Background 10/22
- 13. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 Genetic algorithm Initial population Selection Crossover & mutation Intermediate population (mating pool) Replacement Next population Background 11/22
- 14. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 PGA for Compiler Optimization • The work in this research uses GCC 4.8 compiler on Ubuntu 12.04 with OpenMP 3.0 library. Methodology 12/22
- 15. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 The master-slave model • In the master-slave model the master runs the evolutionary algorithm, controls the slaves and distributes the work. • The Slaves take batches of individuals from the master and evaluate them. Finally send the calculated fitness value back to master. Methodology 13/22
- 16. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 Encoding 1101011101 Methodology 14/22
- 17. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 Fitness function • In the proposed system the PGA works with a population of six Chromosomes on eight core machine and fitness function is computed at the Master core. Fitness=|(exe_with_flagi-exe_without_flagi)| i∈ {1, 2, … . , 12} Master Node Generate random population evaluates all individuals Slave nodes algorithm After 200 generations Methodology 15/22
- 18. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 Algorithm for Slave Nodes Receives all the chromosomes from the master node with the fitness values. The slave cores apply the roulette wheel, Stochastic Universal Sampling and Elitism methods respectively for selection process in parallel Create next generation applying two point crossover. Applies mutations using method, two position interchange and produce two new offspring/chromosomes. Sends both the chromosomes back to the master-node. (The master collects chromosomes from all slaves.) Step 1 Step 2 Step 3 Step 4 Step 5 Methodology 16/22
- 19. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 Selection Methodology 17/22
- 20. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 Crossover and mutation Two point crossover Swap mutation Methodology 18/22
- 21. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 Benchmarks • All the Bench mark programs are parallelized using OpenMP library to reap the benefits of PGA. Experimental results 19/22
- 22. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 Performance analysis • As one sees the Figures, the results after applying PGA (WGAO) presents a major improvement with respect to the random optimization (WRO) and compiling code without applying optimization (WOO). Experimental results 20/22
- 23. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 Performance analysis Experimental results 21/22
- 24. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 Performance analysis Experimental results 21/22
- 25. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 Performance analysis Experimental results 21/22
- 26. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 Performance analysis Experimental results 21/22
- 27. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 Conclusion • In Compiler Optimization research, the phase ordering is an important performance enhancement problem. • This study indicates that by increasing the number of cores the performance of the benchmark program increases along with the usage of PGA. • The major concern in the experiment is the master core waiting time collect values from slaves which is primarily due to the usage of Synchronized communication between the Master-Slave cores in the system. • Further it may be explicitly noted that apart from PRIMS algorithm for core-8 system all other Bench marks exhibit better average performance. 22/22
- 28. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 Thanks for your attention.
- 29. Introduction Background Methodology Experimental results Optimizing Code using Parallel Genetic Algorithm93/3/5 References [1] Satish Kumar T., Sakthivel S. and Sushil Kumar S., “Optimizing Code by Selecting Compiler Flags using Parallel Genetic Algorithm on Multicore CPUs,” International Journal of Engineering and Technology, Vol. 32, No. 5, 2014. [2] Prathibha B., Sarojadevi H., Harsha P., “Compiler Optimization: A Genetic Algorithm Approach,” International Journal of Computer Applications, Vol. 112, No.10, 2015.
Editor's Notes
- #5: search space for attempting optimization phase sequences is