mpomarlan / oink

Oink, an implementation of modern parity game solvers

Oink

Oink is an implementation of modern parity game solvers written in C++. Oink aims to provide high-performance implementations of state-of-the-art algorithms representing different approaches to solving parity games.

Oink is licensed with the Apache 2.0 license and is aimed at both researchers and practitioners. For convenience, Oink compiles into a library that can be used by other projects.

Oink was initially developed (© 2017-2019) by Tom van Dijk and the Formal Models and Verification group at the Johannes Kepler University Linz as part of the RiSE project, and now in the Formal Methods and Tools group at the University of Twente.

The main author of Oink is Tom van Dijk who can be reached via [e-mail]. Please let us know if you use Oink in your projects. If you are a researcher, please cite the following publication.

Tom van Dijk (2018) Oink: An Implementation and Evaluation of Modern Parity Game Solvers. In: TACAS 2018.

The main repository of Oink is https://github.com/trolando/oink.

Implemented algorithms

Oink implements various modern algorithms.

Algorithm	Description
NPP	Priority promotion (bitvector implementation by Benerecetti et al)
PP	Priority promotion (basic algorithm)
PPP	Priority promotion PP+ (better reset heuristic)
RR	Priority promotion RR (even better improved reset heuristic)
DP	Priority promotion PP+ with the delayed promotion strategy
RRDP	Priority promotion RR with the delayed promotion strategy
ZLK	(parallel) Zielonka's recursive algorithm
PSI	(parallel) strategy improvement
TSPM	Traditional small progress measures
SPM	Accelerated version of small progress measures
MSPM	Maciej Gazda's modified small progress measures
SSPM	Quasi-polynomial time succinct progress measures
QPT	Quasi-polynomial time ordered progress measures
ZLKQ	Quasi-polynomial time recursive algorithm
FPI	Distraction-based Fixpoint Iteration
TL	Tangle learning (basic)
ATL	Tangle learning (alternating)
PTL	Progressive tangle learning
DTL	Distraction-free tangle learning

See the TACAS 2018 paper for more details on most of the implemented algorithms. In particular, this paper elaborates on the priority promotion algorithms, the implementation of Zielonka's recursive algorithm, on parallel strategy improvement, on small progress measures and the on QPT ordered progress measures algorithms. Furthermore:

• The accelerated SPM approach is a novel unpublished approach.
• The SSPM succinct progress measures algorithm is by Jurdzinski and Lazic, 2017.
• The QPT recursive algorithm is based on work by Pawel Parys and the follow-up work by Karoliina Lehtinen, Sven Schewe, Dominik Wojtczak.
• FPI is the distraction-based fixpoint algorithm by Tom van Dijk and Bob Rubbens.
• All tangle learning algorithms are proposed by Tom van Dijk (me).

The parallel algorithms use the work-stealing framework Lace.

The solver can further be tuned using several pre-processors:

1. Removing all self-loops (recommended)
2. Removing winner-controlled winning cycles (recommended)
3. Inflating or compressing the game before solving it
4. SCC decomposition to solve the parity game one SCC at a time.
   May either improve or deteriorate performance

Tools

Oink comes with several simple tools that are built around the library liboink.

Main tools:

Tool	Description
oink	The main tool for solving parity games
verify	Small tool that just verifies a solution (can be done by Oink too)
nudge	Swiss army knife for transforming parity games
dotty	Small tool that just generates a .dot graph of a parity game

Tools to generate games:

Tool	Description
rngame	Faster version of the random game generator of PGSolver
stgame	Faster version of the steady game generator of PGSolver
counter_rr	Counterexample to the RR solver
counter_dp	Counterexample to the DP solver
counter_m	Counterexample of Maciej Gazda, PhD Thesis, Sec. 3.5
counter_qpt	Counterexample of Fearnley et al, An ordered approach to solving parity games in quasi polynomial time and quasi linear space, SPIN 2017
counter_core	Counterexample of Benerecetti et al, Robust Exponential Worst Cases for Divide-et-Impera Algorithms for Parity Games, GandALF 2017
tc	Two Counters counterexample (GandALF 2019)
tc+	Two Counters counterexample modified for progressive tangle learning

Instructions

Oink is compiled using CMake. Optionally, use ccmake to set options. By default, Oink does not compile the extra tools, only the library liboink and the tool oink. Oink requires the Boost libraries, in particular boost_iostreams.

mkdir build && cd build
cmake .. && make && make install

Oink provides usage instructions via oink --help. Typically, Oink is provided a parity game either via stdin (default) or from a file. The file may be zipped using the gzip or bzip2 format, which is detected if the filename ends with .gz or .bz2.

What you want?	But how then?
To quickly solve a gzipped parity game:	oink -v game.pg.gz game.sol
To verify some solution:	oink -v game.pg.gz --sol game.sol

A typical call to Oink is: oink [options] [solver] <filename> [solutionfile]. This reads a parity game from filename, solves it with the chosen solver (default: --npp), then writes the solution to <solutionfile> (default: don't write). Typical options are:

• -v verifies the solution after solving the game.
• -w <workers> sets the number of worker threads for parallel solvers. By default, these solvers run their sequential version. Use -w 0 to automatically determine the maximum number of worker threads.
• --inflate and --compress inflate/compress the game before solving it.
• --scc repeatedly solves a bottom SCC of the parity game.
• --no-wcwc, --no-loops and --no-single disable preprocessors that eliminate winner-controlled winning cycles, self-loops and single-parity games.
• -z <seconds> kills the solver after the given time.
• --sol <filename> loads a partial or full solution.
• --dot <dotfile> writes a .dot file of the game as loaded.
• -p writes the vertices won by even/odd to stdout.
• -t (once or multiple times) increases verbosity level.