Obtaining The Code

Obtain the code from Github. This uses CMAKE to generate the build files.

How to build

If using Visual studio, there is a ready-to-use solution file avaialable called LP.sln. Otherwise ensure there is a working version of CMAKE available.

CMake

Use CMake to generate the relevant makefile, VS solution, e.t.c. Useful CMake flags to set would be adding -arch=sm_xx to "CMAKE_CUDA_FLAGS". This ensures that the project is built using a suitable architecture. Requires xx to be set to at least 30. Therefor ensure the GPU this is ran on is an NVidia graphics card with compute capability of at least 30.

e.g. for a GTX 970, which uses compute 3.5, use -arch=sm_35

Check out [this list](https://en.wikipedia.org/wiki/CUDA#GPUs_supported) for help discovering your compute version

Windows

Directory contains a visual studio solution not requiring CMake. Open this .sln file in visual studio.

What this project contains

CMake capabilities to generate a build tool such as make or .sln. The GLM library in include/glm

How to Run

Build and compile the project to generate an executable. The default location will be x64/"build_mode"/LP where "build_mode" is either "Debug" or "Release".

Executable takes 2 arguments: 1) The name of constraint data to read in. 2) The number of batches of these to solve.

An example would be ./x64/Release/LP benchmarks/64 1024 to use the pregenerated file containing 64 random constraints with feasible solution, for 1024 batches.

Program Output

The program will output timing to the "timings" folder in a timings.txt file. This file contains lines of the form %1 %2 %3\n where %1 is the number of constraints (e.g. 64), %2 is the number of batches (e.g. 1024), %3 is the time in milliseconds that the program took.

The solutions to the linear program are printed to console. Access to these values is accessible within the code by both GPU and CPU code in output[i] where i ranges over all batches.

Runing a test example

Once an executable has been generated one can check the program is working correctly. This example uses a release mode build and can be done from the command line by running the following command from the base folder directory for this code:

"./x64/Release/LP.exe" benchmarks/64 1024

Or for Linux:

./x64/Release/LP benchmarks/64 1024

which should provide the output:

Batch 0 Optimal location is x: 1.265306 y: 0.448980 value of 2.163265

Custom Data

To use custom data, create 3 files of the form _A.txt, _B.txt, _C.txt. Where * is a consistent name for all 3 files. File _A.txt starts with a line of 2 numbers, the first is the number of constraints within the file (n) and the dimension of the problem (d) (should always be 2). The remainder of the file is (n) lines of (d) numbers. These represent the left hand side of the constraints A1x + A2y <= B. All should be space-separated when on the same line

File *_B.txt contains (n) lines of 1 number. These represent the right hand side of the constraints A1x + A2y <= B.

File *_C.txt contains (d) lines of 1 number (d=2). These represent the objective function to maximise. Ensure data is written with respects to maximising objective function, and inequalities are of form Ax <= B.

For solving a minimization (e.g. min ax + by ) there is a corresponding maximization function (max -ax - by) that can always be used.