Complications for Computational Experiments from Modern Processors

In this paper, we revisit the approach to empirical experiments for combinatorial solvers. We provide a brief survey on tools that can help to make empirical work easier. We illustrate origins of uncertainty in modern hardware and show how strong the influence of certain aspects of modern hardware and its experimental setup can be in an actual experimental evaluation. More specifically, there can be situations where (i) two different researchers run a reasonable-looking experiment comparing the same solvers and come to different conclusions and (ii) one researcher runs the same experiment twice on the same hardware and reaches different conclusions based upon how the hardware is configured and used. We investigate these situations from a hardware perspective. Furthermore, we provide an overview on standard measures, detailed explanations on effects, potential errors, and biased suggestions for useful tools. Alongside the tools, we discuss their feasibility as experiments often run on clusters to which the experimentalist has only limited access. Our work sheds light on a number of benchmarking-related issues which could be considered to be folklore or even myths

The Configurable SAT Solver Challenge (CSSC)

It is well known that different solution strategies work well for different types of instances of hard combinatorial problems. As a consequence, most solvers for the propositional satisfiability problem (SAT) expose parameters that allow them to be customized to a particular family of instances. In the international SAT competition series, these parameters are ignored: solvers are run using a single default parameter setting (supplied by the authors) for all benchmark instances in a given track. While this competition format rewards solvers with robust default settings, it does not reflect the situation faced by a practitioner who only cares about performance on one particular application and can invest some time into tuning solver parameters for this application. The new Configurable SAT Solver Competition (CSSC) compares solvers in this latter setting, scoring each solver by the performance it achieved after a fully automated configuration step. This article describes the CSSC in more detail, and reports the results obtained in its two instantiations so far, CSSC 2013 and 2014

A Time Leap Challenge for SAT Solving

We compare the impact of hardware advancement and algorithm advancement for SAT solving over the last two decades. In particular, we compare 20-year-old SAT-solvers on new computer hardware with modern SAT-solvers on 20-year-old hardware. Our findings show that the progress on the algorithmic side has at least as much impact as the progress on the hardware side.Comment: Authors' version of a paper which is to appear in the proceedings of CP'202

Sparkle: toward accessible meta-algorithmics for improving the state of the art in solving challenging problems

Many fields of computational science advance through improvements in the algorithms used for solving key problems. These advancements are often facilitated by benchmarks and competitions that enable performance comparisons and rankings of solvers. Simultaneously, meta-algorithmic techniques, such as automated algorithm selection and configuration, enable performance improvements by utilizing the complementary strengths of different algorithms or configurable algorithm components. In fact, meta-algorithms have become major drivers in advancing the state of the art in solving many prominent computational problems. However, meta-algorithmic techniques are complex and difficult to use correctly, while their incorrect use may reduce their efficiency, or in extreme cases, even lead to performance losses. Here, we introduce the Sparkle platform, which aims to make meta-algorithmic techniques more accessible to nonexpert users, and to make these techniques more broadly available in the context of competitions, to further enable the assessment and advancement of the true state of the art in solving challenging computational problems. To achieve this, Sparkle implements standard protocols for algorithm selection and configuration that support easy and correct use of these techniques. Following an experiment, Sparkle generates a report containing results, problem instances, algorithms, and other relevant information, for convenient use in scientific publications.Algorithms and the Foundations of Software technolog

Monte Carlo Forest Search: UNSAT Solver Synthesis via Reinforcement learning

We introduce Monte Carlo Forest Search (MCFS), an offline algorithm for automatically synthesizing strong tree-search solvers for proving \emph{unsatisfiability} on given distributions, leveraging ideas from the Monte Carlo Tree Search (MCTS) algorithm that led to breakthroughs in AlphaGo. The crucial difference between proving unsatisfiability and existing applications of MCTS, is that policies produce trees rather than paths. Rather than finding a good path (solution) within a tree, the search problem becomes searching for a small proof tree within a forest of candidate proof trees. We introduce two key ideas to adapt to this setting. First, we estimate tree size with paths, via the unbiased approximation from Knuth (1975). Second, we query a strong solver at a user-defined depth rather than learning a policy across the whole tree, in order to focus our policy search on early decisions, which offer the greatest potential for reducing tree size. We then present MCFS-SAT, an implementation of MCFS for learning branching policies for solving the Boolean satisfiability (SAT) problem that required many modifications from AlphaGo. We matched or improved performance over a strong baseline on two well-known SAT distributions (\texttt{sgen}, \texttt{random}). Notably, we improved running time by 9\% on \texttt{sgen} over the \texttt{kcnfs} solver and even further over the strongest UNSAT solver from the 2021 SAT competition

The Second Reactive Synthesis Competition (SYNTCOMP 2015)

We report on the design and results of the second reactive synthesis competition (SYNTCOMP 2015). We describe our extended benchmark library, with 6 completely new sets of benchmarks, and additional challenging instances for 4 of the benchmark sets that were already used in SYNTCOMP 2014. To enhance the analysis of experimental results, we introduce an extension of our benchmark format with meta-information, including a difficulty rating and a reference size for solutions. Tools are evaluated on a set of 250 benchmarks, selected to provide a good coverage of benchmarks from all classes and difficulties. We report on changes of the evaluation scheme and the experimental setup. Finally, we describe the entrants into SYNTCOMP 2015, as well as the results of our experimental evaluation. In our analysis, we emphasize progress over the tools that participated last year.Comment: In Proceedings SYNT 2015, arXiv:1602.0078