SYNOPSIS
dve2ltsmc [OPTION]… input<_.dve_.dve2C>
DESCRIPTION
dve2ltsmc performs multicore reachability and LTL model checking on the labelled transition system from a compiled specification provided in input.dve or input.dve2C (precompiled). LTS output is not supported by this tool, use the distributed tools instead.
DIVINE
Compile a DVE input model with divine:
divine compile l input.dve
The command results in a compiled model input.dve2C.
If an LTL property is present in the model, a buchi automaton is created. LTSmin can directly perform LTL model checking on such models:
dve2ltsmc strategy=cndfs threads=16 anderson.6.prop4.dve2C
dve2ltsseq por proviso=color strategy=scc anderson.6.prop4.dve
These two examples perform LTL model checking using: multicore NDFS (cndfs),
and a sequential SCCbased algorithm with partial order reduction (por
and
proviso
, where different provisos are available).
Again one can provide additional options to store traces, etc.
Note that this requires a modified version of the DiVinE toolset,
which adds an LTSmin compilation backend (option l
). For
availability, refer to the
LTSmin website.
OPTIONS
include::inc/threadsoption.txt
 strategy=TYPE

Select an exploration strategy. Two kinds of algorithms are available: LTL model checking and reachability. The multicore LTL algorithms (Nested DFS) are implemented in a swarmed fashion, however with a shared state storage. On top of that, MCNDFS and ENDFS offer work sharing between threads and can deliver speedups for some models. Note that the LTL algorithms require buchi automata as input. Such a model can be provided directly by the language frontend, i.e., DiVinE property models are supported, or by the LTL layer (see ltl). Note finally that strict reachability exploration orders are not guaranteed by all multicore search strategies. TYPE can be one of the following options:
 bfs

explore state space in breadthfirst order (default).
 sbfs

explore state space in strict breadthfirst order (default).
 dfs

explore state space in relaxed depthfirst order. Relaxed meaning that the inclusion check on the set of visited states is executed immediately after generating a state. This saves stack space and improves performance of the parallel reachability analysis.
 ndfs

multicore swarmed Nested DepthFirst Search (Courcoubetis et al.).
 nndfs

multicore swarmed New Nested DepthFirst Search (Schwoon, Esparza).
 ldfs

MultiCore Nested DepthFirst Search (Laarman, Langerak, van de Pol, Weber, Wijs).
 endfs

MultiCore Nested DepthFirst Search by Evangelista et al. The algorithm has been adapted with the cyan color encoding and early cycle detection, as described in "Variations on MultiCore Nested DepthFirst Search" (Laarman, van de Pol). Note that ENDFS requires a repair procedure, NNDFS is chosen by default. Alternatives can be chosen by providing a list of strategies, for example: "endfs,mcndfs", yields the NMCNDFS algorithm as described in the Variations paper (with load balancing). Finally, we also allow multiple levels of ENDFS to be combined: "endfs,endfs,nndfs".
 cndfs

New CNDFS algorithm. MultiCore Nested DepthFirst Search (Evangelista, Laarman, Petrucci, van de Pol).
 tarjan

Tarjan’s sequential SCC algorithm. DepthFirst Search and Linear Graph Algorithms (Tarjan).
 renault

Renault’s SCC algorithm. Parallel Explicit Model Checking for Generalized Büchi Automata (Renault et al.).
 ufscc

The UFSCC SCC algorithm. MultiCore OnTheFly SCC Decomposition (Bloemen, Laarman, van de Pol).
 dfsfifo

DFSFIFO detect nonprogress cycles if the frontend provides a progress state or transitions label. Additionally, it verifies weak LTL properties with superior scalability to the *NDFS algorithms. LTSmin automatically identifies weak LTL formulae when provided with a property (via the ltl option).
 perm=TYPE

Select the transition permutation, which is used to guide different threads to different parts of the state space. A good permutation can significantly speed up bug hunting. TYPE can be one of the following options, each has different properties in terms of performance and effectiveness summarized as (perf./eff.) :
 dynamic

use "fresh successor heuristics" described in "Variations on MultiCore Nested DepthFirst Search" (Laarman, van de Pol). (decent/very good for bug hunting). Default for LTL.
 sort

sort on the unique id of the successor state (decent/good)
 random

use multiple fixed random permutation schemes per worker (decent/good).
 rr

randomized using a full random sort order on the states. This is more random than the previous option, but requires more precomputation time for the random array (decent/almost perfect).
 shift

shift the order of transitions by a fixed value per worker (fast/decent).
 otf

sort according to a dynamic permutation scheme (slow/perfect).
 none

use the same exploration order for all workers. Default for reachability.
 state=TYPE

Select type of data structure for storing visited states. TYPE can be one of the following options:
 table

use a lockless hash table.
 tree

use a lockless treecompressed table. In many cases the tree can compress states to two integers regardless of their length. To efficiently accommodate more than 4*10^9 states, the table is split in a root and a leaf table whose respective size can be adjusted using the ratio option (default).
 clearytree

use a lockless treecompressed hash table with a parallel Cleary table to store roots. The compressed size of a state can approach one integer with this approach. The leafs table is stored as a standard tree table (two integers per subtree), which typically can be much smaller (up to the square root of the root table size). To control the respective size of the leaf table use the ratio option.
 ratio=NUMBER

Log_2 ratio between root and leaf table in tree compression. The ratio can theoretically be as low as the square root of the root table size (see size). This will however only work if the state vectors are perfectly combinatorial wrt their variable values. In most cases the leaf table will grow larger, but find found that a factor four (ratio=2) works well for over 75% of the BEEM models. (default: NUMBER=2).
 size=NUMBER

Log_2 hash table size in elements (default: NUMBER=24). This is also used for the internal node table of the tree.
 zobrist=NUMBER

Save time by using zobrist incremental state hashing. NUMBER defines the (log_2) size of the random number table (default: 0=OFF). Large tables mean better hash distributions, but more cache misses due to memory size. Experiments have shown that small tables (2^6) suffice for good distributions (equal or better than Jenkin’s hash). Improvements are only noticable for fast state generators, like DiVinE 2.2 (dve22ltsmc(1)).
 max=NUMBER

Maximum search depth.
 progressstates

Forcess DFSFIFO to use progress state labels, even if progress transition labels are present.
 strict

Forces DFSFIFO to use strict BFS ordering for finding shorter lassos.
 proviso=PROVISO

Change the proviso implementation for partial order reduction in presence of safety properties or liveness properties. Parallel LTL is only supported with the cndfs proviso in the cndfs search strategy. Sequentially, LTL is also supported with the stack proviso in the ndfs search strategy. PROVISO can be either:
 forcenone

Disables the ignoring proviso check (only useful for benchmarking or bug hunting).
 closedset

The closed set proviso is the default proviso which requires almost no extra work/memory. It might however result in less reduction than the stack proviso. It works with both the dfs and (p/s)bfs exploration strategies.
 stack

The stack proviso is the proviso used for example by the spin model checker. It requires some extra work/memory but may result in a better reduction than closedset. It works only for a dfs or ndfs search strategy (for resp. safety or LTL properties).
 cndfs

A special parallel cycle proviso is used to enable POR with multicore LTL model checking using the cndfs search strategy.
 n, noexit

Do not exit when an error is found. Just count errors. Error counts are printed with
v
.  d, deadlock

Find state with no outgoing transitions. Returns with exit code 1 if a deadlock is found, 0 or 255 (error) otherwise.
 i, invariant=PREDFILEPREDEXPRESSION

Find state where the invariant is violated. The file PREDFILE contains an expression in a simple predicate language (see ltsminpred(5)). Its contents can also be entered directly as a PREDEXPRESSION. Returns with exit code 1 if a violation is found, 0 or 255 (error) otherwise.
 a, action=STRING

Find state with an outgoing transition of type STRING. Returns with exit code 1 if the action is found, 0 or 255 (error) otherwise.
 trace='FILE'.gcf

When finding a deadlock state or a transition labelled with a certain action, write a trace to 'FILE'.gcf, beginning from the initial state. Traces can be prettyprinted with ltsminprinttrace(1).
PINS Options
 labels

Print state variable, type and value names, and state and action labels. Then exit. Useful for writing predicate (
invariant
), LTL (ltl
), CTL/CTL* (ctl
), and mucalculus (mu
) expressions.  matrix

Print the dependency matrix and exit.
 c, cache

Enable caching of greybox calls.
If this option is used, the state space generator makes calls to the short version of the greybox nextstate function and memoizes the results. If the nextstate function is expensive this will yield substantial speedups.
 pinsguards

Use guards in combination with the long nextstate function to speed up the nextstate function.
 allowundefinededges

Allow values for edge variables in atomic predicates to be unreachable.
 allowundefinedvalues

Allow undefined values in atomic predicates for enums.
 r, regroup=SPEC

Enable regrouping optimizations on the dependency matrix.
SPEC is a commaseparated sequence of transformations <(T,)+> which are applied in this order to the dependency matrix. The following transformations T are available:
 gs

Group Safely; macro for "gc,gr,cw,rs"; almost always a win.
 ga

Group Aggressively (row subsumption); macro for "gc,rs,ru,cw,rs"; can be a huge win, but in some cases causes slower state space generation.
 gsa

Group Simulated Annealing; macro for "gc,gr,csa,rs"; almost always a win; usually better than gs.
 gc

Group Columns; macro for "cs,cn".
 gr

Group Rows; macro for "rs,rn".
 cs

Column Sort; sort columns lexicographically.
 cn

Column Nub; (temporarily) group duplicate columns, thereby making ca more tractable. Requires cs.
 cw

Column sWap; minimize distance between columns by swapping them heuristically. This reordering improves performance of the symbolic data structures.
 ca

Column All permutations; try to find the column permutation with the best cost metric. Potentially, this is an expensive operation.
 csa

Column Simulated Annealing; minimize distance between columns by swapping them using simulated annealing.
 rs

Row Sort; sort rows lexicographically.
 rn

Row Nub; remove duplicate rows from the dependency matrix. This is always a win. Requires rs.
 ru

Row sUbsume; try to remove more rows than nubbing, thereby trading speed for memory. Requires rs.
 w2W

Overapproximate all mustwrite to maywrite. Maywrite supports the copy () dependency.
 r2+

Overapproximate read to read+write. Allows read dependencies to also subsume write dependencies.
 w2+

Overapproximate mustwrite to read+write. Allows mustwrite dependencies to also subsume read dependencies.
 W2+

Overapproximate maywrite to read+write. Allows mustwrite dependencies to also subsume read dependencies.
 2r

Overapproximate copy to read. May be useful for testing whether the dependency matrix is correct.
 rb4w

Use special heuristics to move read dependencies before write dependences. Often a win in symbolic state space generation.
 mm

Writes metrics of the selected (sr, sw, sc) matrix to stdout. The following metrics are printed:

Event span: the total distance between the minimum and maximum column of rows.

Normalized event span: the event span divided by the size of the matrix (rows x columns).

Weighted event span: the weighted event span, the event span, including a moment signifying the location of the span. See, Siminiceanu et al., we use moment 1.

Normalized weighted event span: the weighted event span divided by the size of the matrix (rows x column).

 sr

Select the read matrix for cs, csa, cw, ca, rs, bcm, bs, bk, vcm, vacm, vgps and mm.
 sw

Select the write matrix (default) for cs, csa, cw, ca, rs, bcm, bs, bk, vcm, vacm, vgps and mm. The write matrix is the default selection, because only write dependencies can create new nodes in decision diagrams. A bad variable order in the write matrix thus leads to a large number of peak nodes during reachability analysis. A bad variable order in the read matrix can also lead to a slow reachability analysis, but typically not as severe as a bad variable order in the write matrix. Slow reachability analysis due to a bad variable order in the read matrix causes many recursive calls to the relational product operation. Typically it is best that read dependencies are moved to the top DD level, thus left most in the read matrix.
 sc

Select the combined matrix for cs, csa, cw, ca, rs, bcm, bs, bk, vcm, vacm, vgps and mm. The combined matrix is the logical or of the read and write matrix.
 bg

Use a bipartite graph (default) for bcm, bk, bs, vcm, vacm and vgps.
 tg

Create a total graph of the bipartite graph for bcm, bk, bs, vcm, vacm and vgps. This adds more vertices and edges thus increasing computation time, but sometimes provides a better ordering.
Below, the sparse matrix algorithms prefixed with b are only available when LTSmin is compiled with Boost. Algorithms prefixed with v are only available when LTSmin is compiled with ViennaCL.
 bcm

Apply Boost’s CuthillMcKee ordering.
 bk

Apply Boost’s King ordering.
 bs

Apply Boost’s Sloan ordering.
 vcm

Apply ViennaCL’s CuthillMcKee ordering.
 vacm

Apply ViennaCL’s advanced CuthillMcKee ordering.
 vgps

Apply ViennaCl’s GibbsPooleStockmeyer ordering.
 f

Apply FORCE ordering.
 rowperm=PERM

Apply row permutation PERM, where PERM is a sequence of row numbers, separated by a comma. E.g. the vector 2,1,0 will swap row 2 with row 0.
 colperm=PERM

Apply column permutation PERM, where PERM is a sequence of column numbers, separated by a comma. E.g. the vector 2,1,0 will swap column 2 with column 0.
 colins=PAIRS

Insert columns before other columns in the dependency matrix.
PAIRS is a commaseparated sequence of pairs <(C.C,)+>'. E.g. colins=1.0 will insert column 1 before column 0. Each pair contains a source column C and a target column C'. During the application of the whole sequence, C will always be the column number that corresponds with the column before the application of the whole sequence. The column number C' will always be the column during the application of the whole sequence. This means that in for example colins=2.0,1.0, first column 2 is inserted at position 0, then column 1 is inserted at position 0. The result will be that the original column 2 will be at position 1. Another important detail is that when colins is used, all source columns will temporarily be "removed" during reordering from the dependency matrix, i.e. when the r,regroup option is given. After reordering is done, the columns will be inserted at the desired target position. In other words, reordering algorithms given by the option r,regroup, will only be applied on the dependency matrix with source columns removed.
 sloanw1=WEIGHT1

Use WEIGHT1 as the first weight for the Sloan algorithm, see https://www.boost.org/doc/libs/1_66_0/libs/graph/doc/sloan_ordering.htm.
 sloanw2=WEIGHT2

Use WEIGHT2 as the second weight for the Sloan algorithm, see https://www.boost.org/doc/libs/1_66_0/libs/graph/doc/sloan_ordering.htm.
 graphmetrics

Print Boost’s and ViennaCL’s graph metrics (only available when LTSmin is compiled with Boost or ViennaCL).
 regroupexit

Exit with 0 when regrouping is done.
 regrouptime

Print timing information of each transformation, given in sequence regroup (r).
 mucalc=FILEFORMULA

Compute a parity game for the mucalculus formula.
The mucalculus formula is provided in the file FILE or directly as a string FORMULA. The syntax and tool support are described in ltsminmucalc(5).
 ltl=LTLFILELTLFORMULA

Compute crossproduct of a Buchi automaton and the specification
LTLFILE is a file containing an Linear Temporal Logic formula (see ltsminltl(5)). Which content can also be provided directly as LTLFORMULA. This formula will be converted to a Buchi automaton. Then the synchronous cross product with the original specification is computed onthefly. A state label is added to encode accepting states.
 ltlsemantics=spintextbookltsmin

Change the semantics of the crossproduct generated using ltl
Three options are available, the default is automatically chosen based on the atomic predicates in the formula.
 spin

Use semantics equal to the spin model checker. From the source state all transitions are generated. Then, state predicates are evaluated on the source state. The Buchi automaton now moves according to these predicates. Deadlocks in the LTS cause the Buchi to progress independently. This option is incompatible with edgebased atomic predicates. This option is the default if no edgebased atomic predicates are found in the LTL formula.
 textbook

Use textbook semantics. A new initial state is generated with an outgoing transition to the initial state. Now, predicates are evaluated on the target state and the Buchi automaton moves according to these predicates. Deadlocks in the LTS do NOT cause the Buchi to progress independently. This option is incompatible with edgebased atomic predicates.
 ltsmin

Same as spin semantics, but now deadlocks in the LTS do NOT cause the Buchi to progress independently. This option is the default if edgebased atomic predicates are found in the LTL formula.
 por=heurdel

Activate partialorder reduction
PartialOrder Reduction (POR) can reduce the state space when searching for deadlocks (
d
) or accepting cycles (ltl
). Two POR algorithms are available: heur

Uses a costbased heuristic beamsearch to find the smallest stubborn set
 del

Uses Valmari’s deletion algorithm to find the smallest stubborn set by iteratively removing transitions while maintaining the constraints.
 weak

Use weak commutativity in partialorder reduction. Possibly yielding better reductions.
 leap

Use leaping partialorder reduction, by combining several disjoint stubborn sets sequentially.
Environment Variables
LTSmin supports the following list of environment variables.
Name  Unit  Description 

LTSMIN_MEM_SIZE 
bytes 
Sets the amount of system memory to the given value. 
LTSMIN_NUM_CPUS 
constant 
Sets the amount of CPUs to the given value. 
The variables LTSMIN_MEM_SIZE, and LTSMIN_NUM_CPUS are particularly relevant when neither sysconf(3) nor cgroups(7) is able to properly detect these limits, e.g. when LTSmin runs on Travis CI in a docker container.
Development Options
 grey

Make use of
GetTransitionsLong
calls.A language module can have three next state calls:
GetTransitionsAll
,GetTransitionsLong
andGetTransitionsShort
. The first call is used by default, the second call is used when this flag is passed and the third form is used if cache is enabled. This allows all three calls in a language module to be tested.  writestate

Write the full state vector.
General Options
 v

Increase the level of verbosity
 q

Be quiet; do not print anything to the terminal.
 debug=<file.c>

Enable debugging output for file.c (option allowed multiple times).
 version

Print version string of this tool.
 h, help

Print help text
 usage

Print short usage summary.
File Formats
The following file formats are supported:

Directory format (*.dir, *.dz and *.gcf)

Vector format (*.dir, *.gcd, *.gcf)

Binary Coded Graphs (*.bcg)

Aldebaran Format (*.aut)

FSM Format (*.fsm)

MRMC/Prism (*.tra+*.lab)

PGSolver format (*.pg)
If a tool operates in streaming mode then support for file formats is limited, as can be seen in the following table:
Format  Streaming mode  Load/Store mode 

DIR 
R/W 
R/W 
VEC 
R/W 
R/W 
BCG 
W 
R/W 
AUT 
W 
R/W 
FSM 
W 
W 
TRA 
 
R/W 
PG 
 
W 
The directory format uses multiple files to store an LTS. The various extension explain how these files are stored in the underlying file system. The *.dir format uses multiple files in a directory without compression. If the LTS has one edge label, no state labels and does not store state vectors then these files are backwards compatible. Neither the *.dz nor the *.gcf formats are backwards compatible. Both formats use compression. The first uses a directory for the files, second interleaves files into a single file.
If you try to open a *.dir with the old mCRL tools and you get the error message:
wrong file version: 0
then the directory is probably compressed. If that happens then you may convert the directory by typing the command:
ltsminconvert bad.dir good.dir
LTL MODEL CHECKING
LTL model checking requires a Buchi cross product with accepting states marked with accepting state labels, and a search strategy which takes these labels into account (see ndfs / scc strategies of the sequential and multicore tools).
For generating a cross product, the following options are supported:

The input specification is already combined with a Buchi automaton, and states are appropriately marked with accepting labels. Currently two frontends support this functionality: SpinS (see documentation on
prom
tools), and DiVinE (see documentation ondve
tools). 
LTSmin tools can build the cross product of input specification and Buchi automaton (through a PINS2PINS layer enabled with the ltl option) themselves, and will annotate crossproduct states appropriately when they are accepting. See also
labels
option in the PINS tools.
Note

Combination with the Partial Order Reduction PINS2PINS layer (por) requires the latter option. 
Note

As of LTSmin release 1.9, our default LTL semantics mimics those of SPIN/DiVinE. Before that, LTSmin implemented textbook semantics. See the documentation on the enumerative tools for more information. 
EXIT STATUS
 0

Successful termination.
 1

Counter example found.
 255

Some error occurred.
SUPPORT
Send questions, bug reports, comments and feature suggestions to the LTSmin Support Team.
SEE ALSO
ltsmin(7), ltsminprinttrace(1), ltsminmucalc(5), etf(5), DiVinE2.2, opaal, uppaal, and prom.