Tag Archives: SAT Race

CryptoMiniSat 5.8.0 Released

After many months of work, CryptoMiniSat 5.8.0 has been released. In this post I’ll go through the most important changes, and how they helped the solver to be faster and win a few awards, among them 1st place at the SAT incremental track, 3rd place SAT Main track, and 2nd&3d place in the SMT BitVector tracks together with the STP and MinkeyRink solvers.

Gauss-Jordan Elimination

First and foremost, Gauss-Jordan elimination at all levels of the search is now enabled by default. This is thanks to the work detailed in the CAV 2020 paper (video here). The gist of the paper is that we take advantage of the bit-packed matrix and some clever bit field filters to quickly check whether an XOR constraint is propagating, conflicting, or neither. This, and a variety of other improvements lead to about 3-10x speedup for the Gauss-Jordan elimination procedure.

With this speedup, the overhead is quite small, and we enable G-J elimination at all times now. However, there are still limits on the size of the matrix, the number of matrices, and we disable it if it doesn’t seem to improve performance.

As a bit of reflection: our original paper with Nohl and Castelluccia on CryptoMiniSat, featuring Gauss-Jordan elimination at all levels of the search tree was published at SAT 2009. It took about 11 years of work, and in particular the work of Han and Jiang to get to this point, but we finally arrived. The difference is day and night.

Target Phases

This one is really cool, and it’s in CaDiCaL (direct code link here) by Armin Biere, description here (on page 8). If you look at the SAT Race of 2019, you will see that CaDiCaL solved a lot more satisfiable problems than any other solver. If you dig deep enough, you’ll see it’s because of target phases.

Basically, target phases are a variation of phase saving, but instead of saving the phase all the time when backtracking, it only saves it when backtracking from a depth that’s longer than anything seen before. Furthermore, it is doing more than just this: sometimes, it picks only TRUE, and sometimes it picks only FALSE phase. To spice it up, you can keep “local deepest” and “global deepest” if you like, and even pick inverted phases.

It’s pretty self-explanatory if you read this code (basically, just switching between normal, target, inverted, fixed FALSE, fixed TRUE phases) and it helps tremendously. If you look at the graphs of the SAT 2020 competition results (side no. 19 here) you will see a bunch of solvers being way ahead of the competition. That’s target phases right there.

CCAnr Local Search Solver

CryptoMiniSat gained a new local search solver, CCAnr (paper here) and it’s now the default. This is a local search solver by Shaowei Cai who very kindly let me add his solver to CryptoMiniSat and allowed me to add him as an author to the version of CryptoMiniSat that participated in the SAT competition. It’s a local search solver, so it can only solve satisfiable instances, and does so by always working on a full solution candidate that it tries to “massage” into a full solution.

Within CryptoMiniSat, CCAnr takes the starting candidate solution from the phases inside the CDCL solver, and tries to extend it to fit all the clauses. If it finds a satisfying assignment, this is emitted as a result. If it doesn’t, the best candidate solution (the one that satisfies the most clauses) is saved into the CDCL phase and is later used in the CDCL solver. Furthermore, some statistics during the local search phase are saved and then injected into the variable branching heuristics of the CDCL solver, see code here.

Hybrid Variable Branching

Variable branching in CryptoMiniSat has always been a mix of VSIDS (Variable State Independent Decaying Sum, paper here) and Maple (multi-arm bandit based, paper here) heuristics. However, both Maple and VSIDS have a bunch of internal parameters that work best for one, or for another type of SAT problem.

To go around the issue of trying to find a single optimal value for all, CryptoMiniSat now uses a combination of different configurations that is parsed from the command line, such as: “maple1 + maple2 + vsids2 + maple1 + maple2 + vsids1” that allows different configurations for both Maple and VSIDS (v1 and v2 for both) to be configured and used, right from the command line. This configuration system allows for a wider variety of problems to be efficiently solved.

Final Remarks

CryptoMiniSat is now used in many systems. It is the default SAT solver in:

I think the above, especially given their track record of achieving high performance in their respective fields, show that CryptoMiniSat is indeed a well-performing and reliable workhorse. This is thanks to many people, including, but not limited to, Kuldeep Meel, Kian Ming A. Chai, Trevor Hansen, Arijit Shaw, Dan Liew, Andrew V. Jones, Daniel Fremont, Martin Hořeňovský, and others who have all contributed pull requests and valuable feedback. Thanks!

As always, let me know if you have any feedback regarding the solver. You can create a GitHub issue here, and pull request here. I am always interested in new use-cases and I am happy to help integrate it into new systems.

Final polish of my SAT competition entry

[wpdm_file id=2] UPDATE: Fixed crash, UPDATE2: fixed looking for Gaussian elimination library

In the last days of the competition deadline, I have improved the following on the SAT solver:

  • Adding N new variables is now possible in one go. This reduces variable addition overhead, especially when having many threads.
  • Memory allocation overhead per new clause addition is now much smaller thanks to judicious use of globally allocated temporaries. Using such temporaries is very dangerous as CryptoMiniSat uses addClause() from multiple places in the code in a recursive manner. However, I only use the temporaries for add_clause_outer(), so things should be fine.
  • If more than 0.5M variables or 1.5M binary clauses are in the problem, on-the-fly hyper-binary resolution and transitive reduction is turned off during 1st-decision-level search. This is kind of like probing, but during search. However, unlike probing, it cannot time-out and switch off these systems in case they takes too much time.
  • Diversified the threads’ parameters. Also, I reduced the number of threads to 8. I wanted to run with 12, but it’s a bit dangerous from a memory-usage perspective: there is only 24GB available for 12 cores, which means 2GB/core. Some problems take more than 2GB just to parse into the watchlists.
  • Check total memory usage at startup of threads and if too much, halve the total number of threads. This is an emergency measure in case things go wild due to very weird CNF.

Overall these are small changes but allow for a much faster startup. For example a notorious problem, AProVE07-11.cnf, now starts in 2.5s instead of 4.5s. As far as I can tell, this is very similar to the startup time of lingeling on this instance. However, lingeling only uses 1/3rd the memory (~250MB) thanks to a more tight memory manager plus I suspect it doesn’t have a couple of datastructs that I keep around.

The other difference, about OTF hyper-binary resolution, allows for large problems to actually get to the point of solving instead of getting stuck at adding and removing useless binary clauses. Let’s hope all goes well for the competition :)

My SAT Competition 2014 entry

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In case you want to download my SAT competition 2014 zipfile, you can do it now. You can port the changes that you added to CryptoMiniSatv4 — it should work out of the box. Nothing really important changed except some timeout fixes, bug fixes, threading, and fixing DRAT.

Unfortunately the SAT Competition’14 setup doesn’t have Boost’s program options, so I had to create a main_simple.cpp that compiles without Boost. It only supports options “–drat=1”, “–threads=N”, “–verbosity=N” and reading plain CNF files (not gzipped). This means you will have to edit SolverConf.cpp in case your changeset involves command line options. The command line options and their corresponding class variables are easy to look up in main.cpp, so changing SolverConf.cpp appropriately should not be difficult.

Good luck with the competition!

CryptoMiniSat 4 released

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CryptoMiniSat 4 is now available for download. This version brings a number of substantial improvements and picks up speed to be as good as the best solvers out there. It now has a much improved library interface as well as a simple but powerful python interface.

SAT Competition 2014

This release is made ahead of the SAT competition 2014 deadlines so anybody can compete and actually have a chance to win. Unfortunately, the way I see it, it’s not possible to use newer versions of lingeling or riss (see license for for details), MiniSat is rather old and glucose doesn’t have new simplification techniques. If you feel the same way, and you rather not write 30K LoC of code, you might enjoy playing with CryptoMiniSat v4 and submitting it to the competition. You can change as much as you like, it’s LGPLv2 — just don’t call it CryptoMiniSat.

Improvements and techniques

Here is a non-exhaustive list of techniques used in CryptoMiniSat v4:

  • Variable elimination and replacement, strengthening, subsumption, vivification
  • On-the-fly stamping, literal caching, hyper-binary resolution and transitive reduction during failed literal probing
  • Bounded variable addition with hack to allow 2-literal diff
  • DRUP-based unsatisfiable proof logging
  • Gate-based clause shortening and removal
  • XOR recovery and manipulation (NOTE: uses the M4RI library that is GPL, if you want LGPL, compile without it)
  • Precise time- and memory tracking. No time or memory-outs on weird CNFs
  • Precise usefulness tracking of all clauses
  • Clause usefulness-based redundant clause removal. Glues are not used by default, but glues are tracked and can be used (command line option)
  • Variable renumbering and variable number hiding. Thanks to this, XOR clauses are cut and the added variables are transparent to the user.
  • SQL-based data logging and AJAX-based powerful data display
  • And of course many-many more

All of the above are implemented as inprocessing techniques. I do not believe in preprocessing and the solver does not in fact use preprocessing at all — it immediately starts to solve instead. This, as everything else, is configurable and you can change it by passing '--presimp 1' as a command-line option. There are a total of 120 command-line options so you can tune the solver as you like.

Python interface

It’s intuitive and fun to use:

You can even have assumptions:

All the power of the SAT solver in a very accessible manner. XOR clauses are trivial, too:

Where the second argument is the right hand side (RHS) of the equation v1 XOR v2 = False.

C++ interface

Usage is pretty simple, and the header files have been significantly cleaned up:

Some suggestions where you can improve the solver to compete

Here is a non-exhaustive list of things that you can improve to win at the competition:

  • Add your own weird idea. You can add new variables if you like, use the occurrence lists already built, and take advantage of all the datastructures (such as stamps, literal cache) already present.
  • Tune the parameters. I only have exactly one i7-4770 to tune the parameters. You might have more. All parameters are accessible from command line, so tuning should be trivial.
  • Use glues to clean clauses. Or use a combination of glues and usefulness metrics. All the metrics are at your fingertips.
  • Make bounded variable addition work for learnt clauses. I could never figure this one out.
  • Improve the ordering of variable elimination. Makes a huge difference.
  • Try a different approach: I use the ‘heavy’ approach where I don’t remove all clauses that I can as I like strong propagation properties. You might try the ‘light’ approach where everything is removed if possible. Just set variable elimination to 100% and add blocked clause elimination. It might work.

For example, below is the code that calculates which clause should be cleaned or kept. You can clearly see how easily this can be changed using the data elements below:

If you were thinking about submitting your weird hack to the MiniSat hacktrack, think about doing the same to CrytoMiniSat v4. You might actually win the real competition. You can change as much as you like.

I will submit a description of CryptoMiniSat v4, your description can simply say that it’s the same except for xyz that you changed. The point of the descriptions is so that people can read what you did and why and then comprehend the results in that light. Just explain carefully what you did and why, and you should be fine.


Many-many thanks to Martin Maurer who has submitted over 100 bug reports through the GitHub issue system. Kudos to all who have helped me use, debug and improve the solver. To name just a few: Vegard Nossum, Martin Albrecht, Karsten Nohl, Luca Melette, Vijay Ganesh and Robert Aston.

CryptoMiniSat 3.2.0 released

CyptoMinSat 3.2.0 has been released. This code should be extremely stable and should contain no bugs. In case it does, CryptoMiniSat will fail quite bady at the competition. I have fuzzed the solver for about 2-3000 CPU hours, with some sophisticated fuzzers (all available here — most of them not mine) so all should be fine, but fingers are crossed.

Additions and fixes

The main addition this time is certification proofs for UNSAT through the use of DRUP. This allows for use of the solver where certainty of the UNSAT result is a necessity — e.g. where lives could potentially depend on it. Unfortunately, proof checking is relatively slow through any system, though DRUP seems to be the best and fastest method. Other than the implementation of DRUP, I have fixed some issues with variable replacement.

SAT Competition’13

The description of the solver sent in to the SAT Competition’13 is available from the subfolder “desc” of the tarball. The code of 3.2.0 is actually the same that will run during the competition, the only changes made were:

  • the DRUP output had to be put into the standard output
  • the line “o proof DRUP” had to be printed
  • certified UNSAT binary uses the “–unsat 1” option by default
  • compilation was changed to use the m4ri library that was included with the tarball
  • linking is static so m4ri and other requirements don’t cause trouble
  • boost minimum version had to be lowered

You can download my submissions to the competition, forl, from here and here.