A genetic algorithm to improve Linux kernel performance on resource-constrained devices

Interested in learning more about this topic? Visit this web site to read the paper: https://www.gregorykapfhammer.com/research/papers/Kukunas2010a/

1. A Genetic Algorithm to Improve Linux Kernel Performance on Resource-Constrained Devices James T. Kukunas, Robert D. Cupper, and Gregory M. Kapfhammer Department of Computer Science Allegheny College, Pennsylvania, USA Late Breaking Abstracts The Genetic and Evolutionary Computation Conference (GECCO), July 2010 James Kukunas <jkukunas@acm.org> Allegheny College A Genetic Algorithm to Improve Linux Kernel Performance on Resource-Constrained Devices
2. What is a Resource-Constrained Device? A Resource-Constrained Device Is . . . Any Device In Which Resources Are Intentionally Constrained James Kukunas <jkukunas@acm.org> Allegheny College A Genetic Algorithm to Improve Linux Kernel Performance on Resource-Constrained Devices
3. What is a Resource-Constrained Device? A Resource-Constrained Device Is . . . Any Device In Which Resources Are Intentionally Constrained James Kukunas <jkukunas@acm.org> Allegheny College A Genetic Algorithm to Improve Linux Kernel Performance on Resource-Constrained Devices
4. Why Do We Intentionally Constrain Resources? VS The Goal of This Research is to . . . Achieve BMW Performance With A Honda Motor While Keeping Honda Benefits James Kukunas <jkukunas@acm.org> Allegheny College A Genetic Algorithm to Improve Linux Kernel Performance on Resource-Constrained Devices
5. Intel Atom Specifics: In-Order Execution Memory Instruction Buffer Pipeline Out-of-Order Instruction Execution Hardware Dynamically Reorders Instructions to Reduce Dependency Stalls in the Pipeline James Kukunas <jkukunas@acm.org> Allegheny College A Genetic Algorithm to Improve Linux Kernel Performance on Resource-Constrained Devices
6. Intel Atom Specifics: In-Order Execution Memory Pipeline In-Order Instruction Execution Pipeline Sensitive to Depedency Stalls James Kukunas <jkukunas@acm.org> Allegheny College A Genetic Algorithm to Improve Linux Kernel Performance on Resource-Constrained Devices
7. Intel Atom Specifics: In-Order Execution Program Compiler Memory Pipeline Pipeline Modeling . . . At Compile-Time Reduces Dependency Stalls James Kukunas <jkukunas@acm.org> Allegheny College A Genetic Algorithm to Improve Linux Kernel Performance on Resource-Constrained Devices
8. Intel Atom Specifics: Power-Aware Cache Cache CPU At Higher Processor Power States . . . All Caches Blocks are Enabled James Kukunas <jkukunas@acm.org> Allegheny College A Genetic Algorithm to Improve Linux Kernel Performance on Resource-Constrained Devices
9. Intel Atom Specifics: Power-Aware Cache Cache CPU At Lower Processor Power States . . . Cache Blocks are Disabled to Conserve Power James Kukunas <jkukunas@acm.org> Allegheny College A Genetic Algorithm to Improve Linux Kernel Performance on Resource-Constrained Devices
10. What is the Linux Kernel? CPU Mem Disk Peripherals Hardware James Kukunas <jkukunas@acm.org> Allegheny College A Genetic Algorithm to Improve Linux Kernel Performance on Resource-Constrained Devices
11. What is the Linux Kernel? System Calls Interrupts CPU Mem Disk Peripherals I/O Mem. Mgmt Proc. Mgmt Applications System Layout User Space Kernel Space Hardware James Kukunas <jkukunas@acm.org> Allegheny College A Genetic Algorithm to Improve Linux Kernel Performance on Resource-Constrained Devices
12. Precise Fitness Metric System Calls with Counts > 26 System Call Counts (#) System Calls read getxattr fstat64 close mmap2 stat64 open 50 100 150 200 Fitness Metric System Calls Model User/Kernel Space Interaction James Kukunas <jkukunas@acm.org> Allegheny College A Genetic Algorithm to Improve Linux Kernel Performance on Resource-Constrained Devices
13. Precise Fitness Metric System Calls with Counts > 26 System Call Counts (#) System Calls read getxattr fstat64 close mmap2 stat64 open 50 100 150 200 Fitness Metric Only 10% of System Calls had Non-Zero Counts James Kukunas <jkukunas@acm.org> Allegheny College A Genetic Algorithm to Improve Linux Kernel Performance on Resource-Constrained Devices
14. Genetic Algorithm Initialization Initialize Population Parse Compiler Flags Of Enabled Compiler Flags Bit String Representation Initialization: Individual Represents Enabled Compiler Options James Kukunas <jkukunas@acm.org> Allegheny College A Genetic Algorithm to Improve Linux Kernel Performance on Resource-Constrained Devices
15. Genetic Algorithm Initialization Fitness Operator Build Kernel With Specified Compiler Flags Send Kernel To Netbook Micro−Benchmarking Fitness Operator: System Call Micro-Benchmarking James Kukunas <jkukunas@acm.org> Allegheny College A Genetic Algorithm to Improve Linux Kernel Performance on Resource-Constrained Devices
16. Genetic Algorithm Initialization Fitness Operator Top 75% Reproduce Enough to Replace Bottom 25% Bottom 25% Discarded Crossover Operators Selection and Selection Operator: Enforces Strong Elitism James Kukunas <jkukunas@acm.org> Allegheny College A Genetic Algorithm to Improve Linux Kernel Performance on Resource-Constrained Devices
17. Genetic Algorithm Initialization Fitness Operator Crossover Operators Selection and Mutation Operator 1% Chance of a Bit Flip Mutation Operator: Too Much Mutation Masks Evolution James Kukunas <jkukunas@acm.org> Allegheny College A Genetic Algorithm to Improve Linux Kernel Performance on Resource-Constrained Devices
18. Genetic Algorithm Initialization Fitness Operator Crossover Operators Selection and Mutation Operator Result Analysis N Iterations Termination Condition: Predefined Generation Count James Kukunas <jkukunas@acm.org> Allegheny College A Genetic Algorithm to Improve Linux Kernel Performance on Resource-Constrained Devices
19. Results Analysis Results Analysis Technique Phoronix Test Suite Suite to emulate netbook workload SQLLite GnuPG Ogg CRay SciMark 7Zip GTKPerf James Kukunas <jkukunas@acm.org> Allegheny College A Genetic Algorithm to Improve Linux Kernel Performance on Resource-Constrained Devices
20. GTK Results The Fastest Evolved Kernel . . . is About 20 Seconds Faster James Kukunas <jkukunas@acm.org> Allegheny College A Genetic Algorithm to Improve Linux Kernel Performance on Resource-Constrained Devices
21. SQLLite The Fasted Evolved Kernel . . . is About 140 Seconds Faster James Kukunas <jkukunas@acm.org> Allegheny College A Genetic Algorithm to Improve Linux Kernel Performance on Resource-Constrained Devices
22. Conclusions and Future Work Conclusions Evolved Kernels Outperformed Stock Fedora Kernel Genetic Algorithm Excels at Finding Correlations Between Optimizations Future Work More Platforms More Compilers More GA Options James Kukunas <jkukunas@acm.org> Allegheny College A Genetic Algorithm to Improve Linux Kernel Performance on Resource-Constrained Devices
23. Conclusions and Future Work More Information at . . . Jim Kukunas <jkukunas@acm.org> http://member.acm.org/~treak007 James Kukunas <jkukunas@acm.org> Allegheny College A Genetic Algorithm to Improve Linux Kernel Performance on Resource-Constrained Devices