Data

Tools

Indexes

Applications

Experiments

Awesome

An open API service indexing awesome lists of open source software.

https://github.com/steleman/quadratic-assignment

Research on the Quadratic Assignment Problem with CUDA Acceleration
https://github.com/steleman/quadratic-assignment

cuda cuda-kernels cuda-programming cuda-programming-project quadratic-assignment quadratic-assignment-problem

Last synced: about 15 hours ago
JSON representation

Research on the Quadratic Assignment Problem with CUDA Acceleration

Awesome Lists containing this project

README 

          
My own research on the Quadratic Assignment Problem

===================================================



This is some of my own research on the Quadratic Assignment Problem (QAP).

The main program is `qap`. `genrandomdata` is a random data generator to be used with `qap`.

The CUDA-accelerated program is `cudaqap`. See more below.



This is Work-In-Progress. I haven't touched these in over a year. I've been working on other things since then. I'm just using Github as a file backup. :-)



There are two `Makefiles` here: `Makefile.gcc` and `Makefile.clang`. It should be pretty obvious what they are and what they do. :-)



The `sample-data` directory contains sample data.



Saying ` --help` will show you how to use each program.



All of this work was done on Linux (Fedora).

--------------------------------------------



Quadratic Assignment Problem

============================



This repo contains a Proof-Of-Concept implementation of the brute-force approach to the Quadratic Assignment Problem. The program name is `qap`.



```

%> ./qap --help

Usage: qap [-h | --help]

           [ -f  | --flinput ]

           [ -d  | --dstinput ]

           [-p | --print]

           (print vector contents).

```



Example:



```

%> ./qap -f ./fldata-100.dat -d ./dstata-100.dat -p

Minimum cost: 165908

Iterations: 3628800

Flow Graph Vector:

{ { 6, 7, 22, 23, 30, 52, 65, 68, 73, 77 },

  { 2, 5, 20, 39, 48, 52, 57, 76, 79, 82 },

  { 3, 4, 16, 18, 30, 41, 43, 45, 60, 75 },

  { 6, 14, 49, 55, 61, 62, 65, 67, 76, 97 },

  { 22, 26, 30, 39, 41, 47, 51, 61, 75, 82 },

  { 9, 24, 46, 53, 62, 68, 74, 76, 97, 98 },

  { 8, 12, 13, 16, 20, 44, 69, 73, 78, 83 },

  { 2, 10, 15, 25, 50, 52, 67, 73, 76, 85 },

  { 23, 60, 72, 76, 78, 82, 87, 88, 90, 95 },

  { 17, 25, 27, 29, 30, 64, 74, 85, 99, 100 } }



Distance Vector:

{ { 1, 4, 6, 20, 21, 60, 61, 63, 70, 78 },

  { 5, 13, 15, 41, 46, 50, 56, 59, 64, 75 },

  { 2, 19, 24, 49, 52, 53, 73, 78, 81, 91 },

  { 2, 4, 26, 34, 38, 45, 59, 91, 95, 100 },

  { 23, 26, 36, 41, 45, 47, 74, 87, 93, 95 },

  { 1, 17, 30, 34, 49, 53, 71, 88, 95, 96 },

  { 3, 10, 15, 27, 30, 45, 58, 59, 86, 93 },

  { 15, 21, 26, 35, 65, 72, 82, 88, 93, 99 },

  { 3, 7, 16, 19, 24, 53, 69, 82, 93, 100 },

  { 3, 11, 17, 19, 21, 37, 78, 92, 93, 95 } }



Assignment Vector:

{ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 }



Clock Resolution: 0.000000001.

CPU time: 0.000000000128 second(s).

```



The auxiliary program `genrandomdata` can be used to generate random data to be used by the `qap` program. Example usage of `genrandomdata`:



```

%> ./genrandomdata -q -f -o ./fldata-1000.dat -u 100 -m 10 -M 10 -n 1000

%> ./genrandomdata -q -f -o ./dstdata-1000.dat -u 100 -m 10 -M 10 -n 1000

```



The use case above will generate Flow Graph and Distance Graph input files for the `qap` program.



```

%> ./genrandomdata --help

Usage: genrandomdata  [--help | -h]

                      (print this message)

                      [--quiet | -q]

                      (nothing printed to stdout)

                      [--auto | -a]

                      (auto-fill the generated sets to cover the full universe)

                      [--fixed | -f]

                      (constant (fixed) set size. maxsize == minsize).

                      [--output  | -o ]

                      (output filename)

                      [--usize  | -u ]

                      (number of elements in the universe)

                      [--minsize  | -m ]

                      (minimum size of a generated subset)

                      [--maxsize  | -M ]

                      (maximum size of a generated subset)

                      [--nsets  | -n ]

```



Note: `genrandomdata` is *very* slow, because it has to fit the randomly generated set element numbers within the boundary constraints indicated on command-line.



The files suffixed with `*.dat` - included here - are sample input files suitable for use by `qap`.



Files with names beginning with `fldata` are Flow Graph input files. Files with names beginning with `dstdata` are Distance Graph input files.



CUDA Acceleration

=================



`cudaqap.cu` is the CUDA-Accelerated version of `qap.cpp`. `Makefile.cuda` is the Makefile for building with CUDA.



I tested it with CUDA 12.9. It should work with any CUDA version higher than that.



Sample results:



```

%> ./cudaqap -d ./dstdata-100.dat -f ./fldata-100.dat

Minimum cost: 319571

Iterations:   67840

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000000 second(s).



%> ./cudaqap -d ./dstdata-100.dat -f ./fldata-100.dat

Minimum cost: 319571

Iterations:   67840

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000000 second(s).



%> ./cudaqap -d ./dstdata-100.dat -f ./fldata-100.dat

Minimum cost: 319571

Iterations:   67840

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000000 second(s).



%> ./cudaqap -d ./dstdata-144.dat -f ./fldata-144.dat

Minimum cost: 1627159

Iterations:   68352

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000000 second(s).



%> ./cudaqap -d ./dstdata-144.dat -f ./fldata-144.dat

Minimum cost: 1627159

Iterations:   68352

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000000 second(s).



%> ./cudaqap -d ./dstdata-144.dat -f ./fldata-144.dat

Minimum cost: 1627159

Iterations:   68352

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000000 second(s).



%> ./cudaqap -d ./dstdata-256.dat -f ./fldata-256.dat

Minimum cost: 5517628

Iterations:   69376

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000000 second(s).



%> ./cudaqap -d ./dstdata-256.dat -f ./fldata-256.dat

Minimum cost: 5517628

Iterations:   69376

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000000 second(s).



%> ./cudaqap -d ./dstdata-256.dat -f ./fldata-256.dat

Minimum cost: 5517628

Iterations:   69376

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000000 second(s).



%> ./cudaqap -d ./dstdata-400.dat -f ./fldata-400.dat

Minimum cost: 20216534

Iterations:   70400

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000000 second(s).



%> ./cudaqap -d ./dstdata-400.dat -f ./fldata-400.dat

Minimum cost: 20216534

Iterations:   70400

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000000 second(s).



%> ./cudaqap -d ./dstdata-400.dat -f ./fldata-400.dat

Minimum cost: 20216534

Iterations:   70400

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000000 second(s).



%> ./cudaqap -d ./dstdata-625.dat -f ./fldata-625.dat

Minimum cost: 79571576

Iterations:   71680

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000000 second(s).



%> ./cudaqap -d ./dstdata-625.dat -f ./fldata-625.dat

Minimum cost: 79571576

Iterations:   71680

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000000 second(s).



%> ./cudaqap -d ./dstdata-625.dat -f ./fldata-625.dat

Minimum cost: 79571576

Iterations:   71680

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000000 second(s).



%> ./cudaqap -d ./dstdata-900.dat -f ./fldata-900.dat

Minimum cost: 234319635

Iterations:   72960

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000001 second(s).



%> ./cudaqap -d ./dstdata-900.dat -f ./fldata-900.dat

Minimum cost: 234319635

Iterations:   72960

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000001 second(s).



%> ./cudaqap -d ./dstdata-900.dat -f ./fldata-900.dat

Minimum cost: 234319635

Iterations:   72960

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000001 second(s).



%> ./cudaqap -s ./chr12a.dat

Minimum cost: 40172

Iterations:   68352

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000000 second(s).



%> ./cudaqap -s ./chr12a.dat

Minimum cost: 40172

Iterations:   68352

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000000 second(s).



%> ./cudaqap -s ./chr12a.dat

Minimum cost: 40172

Iterations:   68352

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000000 second(s).



%> ./cudaqap -s ./chr20a.dat

Minimum cost: 10478

Iterations:   70400

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000000 second(s).



%> ./cudaqap -s ./chr20a.dat

Minimum cost: 10478

Iterations:   70400

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000000 second(s).



%> ./cudaqap -s ./chr20a.dat

Minimum cost: 10478

Iterations:   70400

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000000 second(s).



%> ./cudaqap -s ./chr25a.dat

Minimum cost: 19750

Iterations:   71680

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000000 second(s).



%> ./cudaqap -s ./chr25a.dat

Minimum cost: 19750

Iterations:   71680

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000000 second(s).



%> ./cudaqap -s ./chr25a.dat

Minimum cost: 19750

Iterations:   71680

CPU Clock Resolution: 0.000000001.

GPU time: 0.000000000000 second(s).



```



Each test was run on the same sample data 3 times sequentially to prove the consistency of the results.



The test results were obtained on a Corei9 laptop with a NVIDIA 4080 GPU.



The `chr20a.dat` sample data does not finish after running continuously for 48 hours with the CPU-only version (`qap.cpp`). The improvement with CUDA is noticeable.



The CUDA Acceleration part is Work-In-Progress. It probably needs some cleanup.