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Genetic Optimization Algorithm (GOA)

A post-compilation optimization tool capable of optimizing myriad aspects of program runtime behavior. The three required inputs are (1) compiled program assembly code, (2) a test workload used to exercise candidate optimizations, and (3) a fitness function used to score runtime behavior. As opposed to compiler optimizations which maintain program semantics and typically target only executable speed and size, this technique is capable of addressing any measurable aspect of runtime behavior and may change program semantics.

Assembler code is modified using generic program transformations, taken from Genetic Programming, yielding candidate optimizations. The fitness of candidates are determined using by running on the workload and combining performance metrics with the fitness function. Due to the inherent mutational robustness of software [1], many of these mutations will change the runtime behavior of software without changing the specification to which the software conforms. Some candidates will have desirable non-functional properties such as faster running times, reduced energy consumption or a smaller executable size.

Modern system emulators and profilers (e.g., Linux perf [2]) allow fine-grained monitoring of aspects of program execution. Fitness functions combine these measurements to model aspects of program execution such as energy consumption and communication overhead, which may be difficult to predict a-priori.

This repository supports multiple benchmark suites. The PARSEC benchmark suite [3] focuses on emerging workloads. The "Computer Language Benchmarks Game" [4] holds a number of simpler more traditional benchmark programs implemented in multiple languages. Partial support is provided for working with the SPEC benchmark suite [5] which stresses a system's "processor, memory subsystem and compiler". Currently PARSEC has the most complete support.

Repository Layout

    README | this file
     NOTES | working notes and reproduction instructions
   COPYING | standard GPLV3 License
benchmarks | holds benchmark programs, input and output
       bin | shell scripts to run experiments and collect results
       etc | miscellaneous support files
   results | experimental results
       src | lisp source for main optimization programs

Installation and Usage

Clone this repository. To avoid a downloading a large amount of historical data, use the --single-branch option to git clone as follows.

git clone --single-branch git://

The evolution toolkit which we'll use to evolve programs is written in Common Lisp. Each optimized program also requires a shell script test driver, and a test harness (used to limit resources consumed by evolved variants) is written in C. Assuming you already have both bash and a C compiler on your system, the following additional tools will need to be installed.

  1. Steel Bank Common Lisp (SBCL) [6] or Clozure Common Lisp (CCL) [7].

  2. The Quicklisp [8] Common Lisp package manager which will be used to install all of the required lisp packages. Follow the instructions on the Quicklisp site to install it.

  3. Under the directory to which quicklisp has been installed (by default ~/quicklisp), there will be a local-projects directory. Clone the following git repositories into this directory.

     git clone git://
     git clone git://

    You will also need to symlink this repository into your local-projects directory.

     ln -s $(pwd) ~/quicklisp/local-projects/

    Finally, ensure Quicklisp has been added to your init file, and then use Quicklisp to register these newly cloned local projects.

  4. Once Quicklisp and these dependencies have all been installed, run the following to install the GOA package and all of its dependencies.

     (ql:quickload :goa)

    It may also be necessary to explicitly load some additional dependencies with the following.

     (ql:quickload :trivial-gray-streams)
     (ql:quickload :lhstats)
  5. Checkout the following tool for the protected execution of shell commands through the file system. This serves to isolate the evolutionary process from the many errors thrown during extremely long-running optimization runs, the accumulation of which can occasionally stall the lisp process. From the base of this directory run the following to clone sh-runner.

     git clone git://
  6. At this point it is possible to run program optimization from the lisp REPL as described below. To build a command line program optimization executable, install buildapp [9] and then run make.

Make Variables

The following variables may be used to control the behavior of make.

  • The QUICK_LISP variable may be set to point to a custom quicklisp installation directory. The default value is $HOME/quicklisp/.

  • The LISP_STACK variable may be used to set the maximum amount of memory available to the goa executable when compiled with SBCL. Large programs, especially when annotated (e.g., with src/configs/use-annotation.lisp) may require large amounts of memory. For example run the following to build the goa executable with 30G of memory.

       make bin/goa LISP_STACK=$((30 * 1024))
  • The LISP_LIBS variable may be used to include additional packages into compiled executables. For example to compile the iolib package into the goa executable for socket communication (e.g., with src/configs/by-flag.lisp), run the following.

       make bin/goa LISP_LIBS=iolib

Optimization at the Command Line

At this point everything needed has been installed. The following steps walk through optimizing swaptions from the command line to reduce runtime. To run this example either a time executable which supports the -p and -o options (not the shell built in), or perf is required.

  1. Run the goa executable once to view all of the optional arguments. All scripts and executables in the ./bin/ directory print help information in response to the -h flag.

     ./bin/goa -h
  2. Compile swaptions to assembly and generate the test input and oracle output files. Note, the first time this is run it will download and unpack the PARSEC benchmarks which may take some time.

     ./bin/mgmt output swaptions
  3. Run a test of the swaptions executable to ensure everything is working and to see the output available to our fitness function. If using time run the following,

     ./bin/run swaptions ./benchmarks/swaptions/swaptions -t

    If using perf run the following.

     ./bin/run swaptions ./benchmarks/swaptions/swaptions -p
  4. Optimize swaptions to reduce runtime. If using time run the following.

     ./bin/goa "./bin/run swaptions ~a -t" \
       benchmarks/swaptions/swaptions.s \
       -l g++ -L "-lm -pthread -DENABLE_THREADS" \
       -F real -f 256 -p 128 -P 64 -t 2 -r swap

    If using perf run the following.

     ./bin/goa "./bin/run swaptions ~a -p" \
       benchmarks/swaptions/swaptions.s \
       -l g++ -L "-lm -pthread -DENABLE_THREADS" \
       -F seconds -f 256 -p 128 -P 64 -t 2 -r swap

    The -l option specifies that g++ should be used as the linker (gcc is the default linker), and that the flags "-lm -pthread -DENABLE_THREADS" should be passed to g++ during linking. By passing real (or seconds) to -F we specify that we want our fitness function to minimize the time taken to run this program. The remaining flags specify 256 total fitness evaluations should be run (-f), a population of size 128 should be used (-p), periodic checkpoints should be written every 64 fitness evaluations (-P), optimization should be distributed across 2 threads (-t), and results should be saved in a directory named swap (-r).

  5. When repair complete the name of the results directory will be printed. In this directory the file holds the optimized program. This may be compiled to an optimized executable with the following (see the -h output of objread for more ways to use .store files).

     ./bin/objread swap/

Interactive Optimization at the REPL

See src/repl/example.lisp, which demonstrates how these tools may be run interactively from the common lisp REPL. The evolving population, and many important evolutionary parameters are exposed as global variables for live analysis and modification during interactive runs.












Evolutionary optimization of extant software







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