Category Archives: SAT

Memory layout of clauses in MiniSat

SAT,

I have been trying to debug why some MiniSat-based solvers perform better at unit propagation than CryptoMiniSat. It took me exactly 3 full days to find out and I’d like to share this tidbit of information with everyone, as I think it might be of interest and I hardly believe many understand it.

The mystery of the faster unit propagation was that I tried my best to make my solver behave exactly as MiniSat to debug the issue, but even though it was behaving almost identically, it was still slower. It made no sense and I had to dig deeper. You have to understand that both solvers use their own memory managers. In fact, CryptoMiniSat had a memory manager before MiniSat. Memory managers are used so that the clauses are put close to one another so there is a chance that they are in the same memory page, or even better, close enough for them not to waste memory in the cache. This means that a contiguous memory space is reserved where the clauses are placed.

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Testing CryptoMiniSat using GoogleTest

Development, SAT,

Lately, I have been working quite hard on writing module tests for CryptoMinisat using GoogleTest. I’d like to share what I’ve learnt and what surprised me most about this exercise.

An example

First of all, let me show how a typical test looks like:

TEST_F(intree, fail_1)
{
    s->add_clause_outer(str_to_cl(" 1,  2"));
    s->add_clause_outer(str_to_cl("-2,  3"));
    s->add_clause_outer(str_to_cl("-2, -3"));

    inp->intree_probe();
    check_zero_assigned_lits_contains(s, "-2");
}

Here we are checking that intree probing finds that the set of three binary clauses cause a failure and it enqueues “-2” at top level. If one looks at it, it’s a fairly trivial test. It turns out that most are in fact, fairly trivial if the system is set up well. This test’s setup is the following test fixture:

struct intree : public ::testing::Test {
    intree()
    {
        must_inter = false;
        s = new Solver(NULL, &must_inter);
        s->new_vars(30);
        inp = s->intree;
    }
    ~intree()
    {
        delete s;
    }

    Solver* s;
    InTree* inp;
    bool must_inter;
};

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CryptoMiniSat: 8000 commits later

Personal, SAT, , ,

The GitHub repository for CryptoMiniSat just hit 8000 commits. To celebrate this rather weird and crazy fact, let me put together a bit of a history.

The Beginnings

CryptoMiniSat began as a way of trying to prove that a probabilistic cryptographic scheme was not possible to break using SAT solvers. This was the year 2009, and I was working in Grenoble at INRIA. It was a fun time and I was working really hard to prove what I wanted to prove, to the point that I created a probabilistic SAT solver where one could add probability weights to clauses. The propagations and conflict engine would only work if the conflict or propagation was supported by multiple clauses. Thus began a long range of my unpublished work in SAT. This SAT solver, curiously, still works and solves problems quite well. It’s a lot of fun, actually — just add some random clause into the database that bans the only correct solution and the solver will find the correct solution. A bit of a hackery, but hey, it works. Also, it’s so much faster than doing that solving with the “right” systems, it’s not even worth comparing.
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Machine Learning and SAT

Development, Research, SAT, ,

I have lately been digging myself into a deep hole with machine learning. While doing that it occurred to me that the SAT community has essentially been trying to imitate some of ML in a somewhat poor way. Let me explain.

CryptoMiniSat and clause cleaning strategy selection

When CryptoMiniSat won the SAT Race of 2010, it was in large part because I realized that glucose at the time was essentially unable to solve cryptographic problems. I devised a system where I could detect which problems were cryptographic. It checked the activity stability of variables and if they were more stable than a threshold, it was decided that the problem was cryptographic. Cryptographic problems were then solved using a geometric restart strategy with clause activities for learnt database cleaning. Without this hack, it would have been impossible to win the competition.
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Towards CryptoMiniSat 5.0

SAT, Tools, ,

I have worked a lot on CryptoMiniSat 5.0 in the past months so I thought I’d write a little bit about what I spent my time on.

Amazon AWS

I have put lots of effort into use Amazon AWS service to run CMS. This is necessary in order to compete at the SAT competition where my competitors have access to massive resources, some to clusters having over 20k CPU cores. Competing against that with a 4-core machine like I did last year will simply not cut it.

The system I built has a client-server infrastructure where the server is a very-very small machine (t1.micro) that hands out jobs to very-very beefy client machine(s) (c4.8xlarge with 18 real cores). I need this architecture because the client I use is a so-called spot instance so Amazon can shut it down any time. The server makes sure to keep in mind what has been solved and what needs to be solved next to complete the job. At the finish of the job, both the server and the client shut down. I simply need to issue, e.g. “./launch_server.py –git 82c4e5adce –s3folder newrun –cnfdir satcomp091113 -t 5000” and it will launch the full SAT competition 09+11+13 instances with a 5000s timeout using a specific GIT revision of CryptoMiniSat. When it finishes (in about 4-5 hours), it (should) send me a mail with the command line to use to download all the data from Amazon S3. It’s neat, fast, and literally just one command line to use.

As for how much I have used it, I have spent over $100 on running costs on AWS in the past 2 months. A run like the one above costs about $2. Not super-cheap, but not the end of the world, either.

Testing and continuous integration

I have TravisCI, Coverity, and Coverall integration. These provide continious integration testing, static analysis, and code coverage analysis, respectively. I find TravisCI to be immensely valuable, I would have trouble not having it for a new project. Coverity is also pretty useful, it has actually found some pretty stupid mistakes I have made. Finally, coveralls has a terrible interface but I like the idea of having test code coverage analysis and it encourages me to put more effort into that. For example, it highlights pretty well the areas that I typically break when coding without realizing it. TravisCI usually warns me if there is something bad except when there is no (or too little) coverage. I am also looking into Docker, which would allow for continuous delivery.

Checking against SWDiA5BY

I have integrated the main idea of SWDiA5BY A26 code into CryptoMiniSat. Further, I am in the process of integrating one of thepatches available on the author’s website. I find these patches to be really interesting and using SWDiA5BY A26 as a check against my own system has allowed me to get rid of a lot of bugs. So, I am greatly indebted to the authors of MiniSat, Glucose and SWDiA5BY.

Conclusions

In the past months I have put a lot of effort into cleaning up, fixing, and taking control of CryptoMiniSat in general. There have been over 240 issues filed at github against CryptoMiniSat over the years, and only 7 are currently open. This is a testament to how open and dynamic the solver development is. In case you are interested in helping to develop or have new ideas, don’t hesitate to contact me. Further, if you have any commercial interest in the solver, don’t hesitate to contact me.