Artifact: assignments.f90

Description:

A Python program to build and solve the CCM-MILP problems, with a set of examples.

Requirements:

• Python 3.x

Execution

From the folder src

Generate and solve a problem (and apply permutation on Synthetic Blocks example):

Either in interactive mode:

python ccm_milp_full.py

or in direct mode with a configuration file in YAML format:

python ccm_milp_full.py -c "<configuration file name>"

or with a specific solver installed on the machine:

python ccm_milp_full.py -s "<solver name: COIN_CMD, GLPK_CMD, PULP_CBC_CMD>"

By default the solver used is PULP_CBC_CMD

Generate problem files (.pl and .mps):

Either in interactive mode:

python ccm_milp_problem.py

or in direct mode with a configuration file in YAML format:

python ccm_milp_problem.py -c "<configuration file name>"

Solve a problem file (.mps):

Solve the generated problem.mps file generated before:

python ccm_milp_solver.py

Solve a specific .mps problem file:

python ccm_milp_solver.py -p "<problem file .mps>"

Solve with a specific solver installed on the machine:

python ccm_milp_solver.py -s "<solver name: COIN_CMD, GLPK_CMD, PULP_CBC_CMD>"

Permutation for JSON data files using a permutation file:

The seprator for --input-json-files files is a space

Permute json data and create output files:

python ccm_milp_permute_json.py --permutation-file="/abs/path/permutation.json" --input-json-files="/abs/path/data.0.json /abs/path/data.1.json /abs/path/data.2.json ..."

Permute json data and create output files using a prefix for output files:

python ccm_milp_permute_json.py --output-file-prefix="permuted_" --permutation-file="/abs/path/permutation.json" --input-json-files="/abs/path/data.0.json /abs/path/data.1.json /abs/path/data.2.json ..."

Parse JSON

The seprator for files is a space

Parse json data and output result into the terminal:

python ccm_milp_parse_json.py --input-json-files="/abs/path/data.0.json /abs/path/data.1.json /abs/path/data.2.json ..."

What to Expect:

A text output in the terminal and a .sol file when it solve with the results of the optimization process, along with a .lp and .mps file containing the generated linear program.

For SyntheticBlock we also can retrieve in the output folder, after running ccm_milp_full.py, the JSON data files permuted and the permutation file generated and used for it.

Expected Results:

By default the configuration is:

alpha: 1
beta:  0
gamma: 0
delta: 0
bounded_memory: false
preserve_clusters: false

The example tested is SyntheticBlock with these differents configurations:

• Load only

  • Configuration: is_fwmp: false
  • Optimal objective value 2.00000000

• Load only and-cluster

  • Configuration: is_fwmp: false, preserve_clusters: true
  • Optimal objective value 2.50000000

• Load only and-memory-bound

  • Configuration: is_fwmp: false, bounded_memory: true
  • Optimal objective value 2.00000000

• FWMP with alpha

  • Configuration: is_fwmp: true
  • Optimal objective value 2.00000000

• FWMP with alpha-beta

  • Configuration: is_fwmp: true, beta: 1
  • Optimal objective value 4.00000000

• Null case

  • Configuration: is_fwmp: true, alpha: 0, preserve_clusters: true
  • Optimal objective value 0.00000000

• Off node communication-only

  • Configuration: is_fwmp: true, alpha: 0, beta: 1
  • Optimal objective value 0.00000000

• Load no memory homing (delta: 0.1)

  • Configuration: is_fwmp: true, delta: 0.1, preserve_clusters: true
  • Optimal objective value 2.50000000

• Load no memory homing (delta: 0.3)

  • Configuration: is_fwmp: true, delta: 0.3, preserve_clusters: true
  • Optimal objective value 4.00000000