PRISM

Benchmark
Model:csma v.1 (MDP)
Parameter(s)N = 2, K = 4
Property:time_min (exp-reward)
Invocation (default)
../fix-syntax ../prism -javamaxmem 11g -cuddmaxmem 4g -heuristic speed -e 1e-6 -maxiters 1000000 csma.2-4.prism csma.props --property time_min
Execution
Walltime:1.2967524528503418s
Return code:0
Relative Error:2.3338281520928578e-07
Log
PRISM
=====

Version: 4.5.dev
Date: Sat Apr 03 01:00:35 CEST 2021
Hostname: christopher
Memory limits: cudd=4g, java(heap)=11g
Command line: prism -javamaxmem 11g -cuddmaxmem 4g -heuristic speed -e 1e-6 -maxiters 1000000 csma.2-4.prism csma.props --property time_min

Parsing model file "csma.2-4.prism"...

Type:        MDP
Modules:     bus station1 station2 
Variables:   b y1 y2 s1 x1 bc1 cd1 s2 x2 bc2 cd2 

Parsing properties file "csma.props"...

5 properties:
(1) "all_before_max": Pmax=? [ !"collision_max_backoff" U "all_delivered" ]
(2) "all_before_min": Pmin=? [ !"collision_max_backoff" U "all_delivered" ]
(3) "some_before": Pmin=? [ F min_backoff_after_success<K ]
(4) "time_max": R{"time"}max=? [ F "all_delivered" ]
(5) "time_min": R{"time"}min=? [ F "all_delivered" ]

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Model checking: "time_min": R{"time"}min=? [ F "all_delivered" ]

Warning: Switching to sparse engine and (backwards) Gauss Seidel (default for heuristic=speed).

Building model...

Computing reachable states...

Reachability (BFS): 93 iterations in 0.05 seconds (average 0.000505, setup 0.00)

Time for model construction: 0.082 seconds.

Type:        MDP
States:      7958 (1 initial)
Transitions: 10594
Choices:     7988

Transition matrix: 5586 nodes (6 terminal), 10594 minterms, vars: 36r/36c/9nd

Prob0A: 104 iterations in 0.07 seconds (average 0.000644, setup 0.00)

Prob1E: 105 iterations in 0.07 seconds (average 0.000648, setup 0.00)

Warning: PRISM hasn't checked for zero-reward loops.
Your minimum rewards may be too low...

goal = 7, inf = 0, maybe = 7951

Computing remaining rewards...
Engine: Sparse

Building sparse matrix (transitions)... [n=7958, nc=7981, nnz=10587, k=2] [186.3 KB]
Building sparse matrix (transition rewards)... [n=7958, nc=7981, nnz=7233, k=2] [147.0 KB]
Creating vector for state rewards... [62.2 KB]
Creating vector for inf... [62.2 KB]
Allocating iteration vectors... [2 x 62.2 KB]
TOTAL: [582.0 KB]

Starting iterations...

Iterative method: 207 iterations in 0.02 seconds (average 0.000068, setup 0.01)

Value in the initial state: 75.65076563517593

Time for model checking: 0.161 seconds.

Result: 75.65076563517593 (value in the initial state)


Overall running time: 0.49 seconds.

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Note: There were 2 warnings during computation.