add search tree skeleton

Signed-off-by: Nikolaj Bjorner <nbjorner@microsoft.com>

src/params/smt_parallel_params.pyg

@@ -19,4 +19,5 @@ def_module_params('smt_parallel',
                         ('beam_search', BOOL, False, 'use beam search with PQ to rank cubes given to threads'),
                         ('explicit_hardness', BOOL, False, 'use explicit hardness metric for cube'),
                         ('cubetree', BOOL, False, 'use cube tree data structure for storing cubes'),
+                        ('searchtree', BOOL, False, 'use search tree implementation (parallel2)')
               ))

src/smt/CMakeLists.txt

@@ -39,6 +39,7 @@ z3_add_component(smt
     smt_model_finder.cpp
     smt_model_generator.cpp
     smt_parallel.cpp
+    smt_parallel2.cpp
     smt_quantifier.cpp
     smt_quick_checker.cpp
     smt_relevancy.cpp

src/smt/smt_context.cpp

@@ -42,8 +42,10 @@ Revision History:
 #include "smt/smt_model_generator.h"
 #include "smt/smt_model_checker.h"
 #include "smt/smt_model_finder.h"
+#include "smt/smt_parallel2.h"
 #include "smt/smt_parallel.h"
 #include "smt/smt_arith_value.h"
+#include "params/smt_parallel_params.hpp"
 #include <iostream>
 
 namespace smt {
@@ -3621,9 +3623,17 @@ namespace smt {
         setup_context(m_fparams.m_auto_config);
 
         if (m_fparams.m_threads > 1 && !m.has_trace_stream()) {
-            parallel p(*this);
-            expr_ref_vector asms(m);
-            return p(asms);
+            smt_parallel_params p(m_params);
+            if (p.searchtree()) {
+                parallel2 p(*this);
+                expr_ref_vector asms(m);
+                return p(asms);
+            }
+            else {
+                parallel p(*this);
+                expr_ref_vector asms(m);
+                return p(asms);
+            }
         }
 
         try {
@@ -3689,8 +3699,15 @@ namespace smt {
         search_completion sc(*this);
         if (m_fparams.m_threads > 1 && !m.has_trace_stream()) {            
             expr_ref_vector asms(m, num_assumptions, assumptions);
-            parallel p(*this);
-            return p(asms);
+            smt_parallel_params p(m_params);
+            if (p.searchtree()) {
+                parallel2 p(*this);
+                return p(asms);
+            }
+            else {
+                parallel p(*this);
+                return p(asms);
+            }
         }
         lbool r = l_undef;
         do {

src/smt/smt_context.h

@@ -85,6 +85,7 @@ namespace smt {
         friend class model_generator;
         friend class lookahead;
         friend class parallel;
+        friend class parallel2;
     public:
         statistics                  m_stats;
 

src/smt/smt_parallel2.cpp

@@ -0,0 +1,490 @@
+/*++
+Copyright (c) 2020 Microsoft Corporation
+
+Module Name:
+
+    smt_parallel.cpp
+
+Abstract:
+
+    Parallel SMT, portfolio loop specialized to SMT core.
+
+Author:
+
+    nbjorner 2020-01-31
+
+--*/
+
+
+
+#include "util/scoped_ptr_vector.h"
+#include "ast/ast_util.h"
+#include "ast/ast_pp.h"
+#include "ast/ast_ll_pp.h"
+#include "ast/ast_translation.h"
+#include "smt/smt_parallel2.h"
+#include "smt/smt_lookahead.h"
+#include "params/smt_parallel_params.hpp"
+
+#include <cmath>
+
+#ifdef SINGLE_THREAD
+
+namespace smt {
+    
+    lbool parallel2::operator()(expr_ref_vector const& asms) {
+        return l_undef;
+    }
+}
+
+#else
+
+#include <thread>
+#include <mutex>
+#include <condition_variable>
+
+
+#define LOG_WORKER(lvl, s) IF_VERBOSE(lvl, verbose_stream() << "Worker " << id << s)
+
+namespace smt {
+
+ 
+
+    void parallel2::worker::run() {
+        search_tree::node<cube_config>* node = nullptr;
+        expr_ref_vector cube(m);
+        while (m.inc()) { 
+
+            collect_shared_clauses(m_g2l);
+
+            if (!b.get_cube(m_g2l, id, cube, node)) {
+                LOG_WORKER(1, " no more cubes\n");
+                return;
+            }
+
+        check_cube_start:
+            LOG_WORKER(1, " CUBE SIZE IN MAIN LOOP: " << cube.size() << "\n");
+            lbool r = check_cube(cube);
+
+            if (!m.inc()) {
+                b.set_exception("context cancelled");
+                return;
+            }
+            
+            switch (r) {
+                case l_undef: {
+                    LOG_WORKER(1, " found undef cube\n");
+                    // return unprocessed cubes to the batch manager
+                    // add a split literal to the batch manager.
+                    // optionally process other cubes and delay sending back unprocessed cubes to batch manager.
+                    auto atom = get_split_atom();
+                    if (!atom)
+                        goto check_cube_start;
+                    b.split(m_l2g, id, node, atom);
+                    break;
+                }
+                case l_true: {
+                    LOG_WORKER(1, " found sat cube\n");
+                    model_ref mdl;
+                    ctx->get_model(mdl);
+                    b.set_sat(m_l2g, *mdl);
+                    return;
+                }
+                case l_false: {
+                    // if unsat core only contains (external) assumptions (i.e. all the unsat core are asms), then unsat and return as this does NOT depend on cubes
+                    // otherwise, extract lemmas that can be shared (units (and unsat core?)).
+                    // share with batch manager.
+                    // process next cube.
+                    expr_ref_vector const& unsat_core = ctx->unsat_core();
+                    LOG_WORKER(2, " unsat core:\n"; for (auto c : unsat_core) verbose_stream() << mk_bounded_pp(c, m, 3) << "\n");
+                    // If the unsat core only contains assumptions, 
+                    // unsatisfiability does not depend on the current cube and the entire problem is unsat.
+                    if (all_of(unsat_core, [&](expr* e) { return asms.contains(e); })) {
+                        LOG_WORKER(1, " determined formula unsat\n");
+                        b.set_unsat(m_l2g, unsat_core);
+                        return;
+                    }
+                    for (expr* e : unsat_core)
+                        if (asms.contains(e))
+                            b.report_assumption_used(m_l2g, e); // report assumptions used in unsat core, so they can be used in final core
+
+                    LOG_WORKER(1, " found unsat cube\n");                    
+                    b.backtrack(m_l2g, unsat_core, node);
+                    break;
+                }
+            }    
+#if 0
+            if (m_config.m_share_units)
+                share_units(m_l2g);
+#endif
+        }
+    }
+
+    parallel2::worker::worker(unsigned id, parallel2& p, expr_ref_vector const& _asms): 
+        id(id), p(p), b(p.m_batch_manager), m_smt_params(p.ctx.get_fparams()), asms(m),
+        m_g2l(p.ctx.m, m),
+        m_l2g(m, p.ctx.m) {
+        for (auto e : _asms) 
+            asms.push_back(m_g2l(e));
+        LOG_WORKER(1, " created with " << asms.size() << " assumptions\n");        
+        m_smt_params.m_preprocess = false;
+        ctx = alloc(context, m, m_smt_params, p.ctx.get_params());
+        context::copy(p.ctx, *ctx, true);
+        ctx->set_random_seed(id + m_smt_params.m_random_seed);
+
+        smt_parallel_params pp(p.ctx.m_params);
+        m_config.m_threads_max_conflicts = ctx->get_fparams().m_threads_max_conflicts;
+        m_config.m_max_conflicts = ctx->get_fparams().m_max_conflicts;
+        m_config.m_relevant_units_only = pp.relevant_units_only();
+        m_config.m_never_cube = pp.never_cube();
+        m_config.m_share_conflicts = pp.share_conflicts();
+        m_config.m_share_units = pp.share_units();
+        m_config.m_share_units_initial_only = pp.share_units_initial_only();
+        m_config.m_cube_initial_only = pp.cube_initial_only();
+        m_config.m_max_conflict_mul = pp.max_conflict_mul();
+        m_config.m_max_greedy_cubes = pp.max_greedy_cubes();
+        m_config.m_num_split_lits = pp.num_split_lits();
+        m_config.m_backbone_detection = pp.backbone_detection();
+        m_config.m_iterative_deepening = pp.iterative_deepening();
+        m_config.m_beam_search = pp.beam_search();
+        m_config.m_explicit_hardness = pp.explicit_hardness();
+        m_config.m_cubetree = pp.cubetree();
+
+        // don't share initial units
+        ctx->pop_to_base_lvl();
+        m_num_shared_units = ctx->assigned_literals().size();
+
+        m_num_initial_atoms = ctx->get_num_bool_vars();
+    }
+
+    void parallel2::worker::share_units(ast_translation& l2g) {
+        // Collect new units learned locally by this worker and send to batch manager
+        ctx->pop_to_base_lvl();
+        unsigned sz = ctx->assigned_literals().size();
+        for (unsigned j = m_num_shared_units; j < sz; ++j) {  // iterate only over new literals since last sync
+            literal lit = ctx->assigned_literals()[j];
+            if (!ctx->is_relevant(lit.var()) && m_config.m_relevant_units_only) 
+                continue;
+
+            if (m_config.m_share_units_initial_only && lit.var() >= m_num_initial_atoms) {
+                LOG_WORKER(2, " Skipping non-initial unit: " << lit.var() << "\n");
+                continue; // skip non-iniial atoms if configured to do so
+            }
+            
+            expr_ref e(ctx->bool_var2expr(lit.var()), ctx->m); // turn literal into a Boolean expression
+            if (m.is_and(e) || m.is_or(e))             
+                continue;
+            
+
+            if (lit.sign()) 
+                e = m.mk_not(e); // negate if literal is negative
+            b.collect_clause(l2g, id, e);
+        }
+        m_num_shared_units = sz;
+    }
+
+    void parallel2::worker::collect_statistics(::statistics& st) const {
+        ctx->collect_statistics(st);
+    }
+
+    void parallel2::worker::cancel() {
+        LOG_WORKER(1, " canceling\n");
+        m.limit().cancel();
+    }
+
+    void parallel2::batch_manager::backtrack(ast_translation& l2g, expr_ref_vector const& core, 
+                                             search_tree::node<cube_config>* node) {
+        std::scoped_lock lock(mux);
+        IF_VERBOSE(1, verbose_stream() << "Batch manager backtracking.\n");
+        if (m_state != state::is_running)
+            return;
+        vector<cube_config::literal> g_core;
+        for (auto c : core) {
+            expr_ref g_c(l2g(c), m);
+            if (is_assumption(g_c))
+                continue;
+            g_core.push_back(expr_ref(l2g(c), m));
+        }
+        m_search_tree.backtrack(node, g_core);
+        if (m_search_tree.is_closed()) {
+            m_state = state::is_unsat;
+            cv.notify_all();
+            cancel_workers();
+        }
+    }
+
+    void parallel2::batch_manager::split(ast_translation& l2g, unsigned source_worker_id, 
+                                        search_tree::node<cube_config>* node, expr* atom) {
+        std::scoped_lock lock(mux);
+        expr_ref lit(m), nlit(m);
+        lit = l2g(atom);
+        nlit = mk_not(m, lit);
+        IF_VERBOSE(1, verbose_stream() << "Batch manager splitting on literal: " << mk_bounded_pp(lit, m, 3) << "\n");
+        if (m_state != state::is_running)
+            return;
+        m_search_tree.split(node, lit, nlit);
+        cv.notify_all();
+    }
+
+    void parallel2::batch_manager::collect_clause(ast_translation& l2g, unsigned source_worker_id, expr* clause) {
+        std::scoped_lock lock(mux);
+        expr* g_clause = l2g(clause);
+        if (!shared_clause_set.contains(g_clause)) {
+            shared_clause_set.insert(g_clause);
+            shared_clause sc{source_worker_id, expr_ref(g_clause, m)};
+            shared_clause_trail.push_back(sc);
+        }
+    }
+
+    void parallel2::worker::collect_shared_clauses(ast_translation& g2l) { 
+        expr_ref_vector new_clauses = b.return_shared_clauses(g2l, m_shared_clause_limit, id); // get new clauses from the batch manager
+        // iterate over new clauses and assert them in the local context
+        for (expr* e : new_clauses) {
+            expr_ref local_clause(e, g2l.to()); // e was already translated to the local context in the batch manager!!
+            ctx->assert_expr(local_clause); // assert the clause in the local context
+            LOG_WORKER(2, " asserting shared clause: " << mk_bounded_pp(local_clause, m, 3) << "\n");
+        }
+    }
+
+    // get new clauses from the batch manager and assert them in the local context
+    expr_ref_vector parallel2::batch_manager::return_shared_clauses(ast_translation& g2l, unsigned& worker_limit, unsigned worker_id) {
+        std::scoped_lock lock(mux);
+        expr_ref_vector result(g2l.to());
+        for (unsigned i = worker_limit; i < shared_clause_trail.size(); ++i) {
+            if (shared_clause_trail[i].source_worker_id == worker_id)
+                continue; // skip clauses from the requesting worker
+            result.push_back(g2l(shared_clause_trail[i].clause.get()));
+        }
+        worker_limit = shared_clause_trail.size(); // update the worker limit to the end of the current trail
+        return result;
+    }
+
+    lbool parallel2::worker::check_cube(expr_ref_vector const& cube) {
+        for (auto& atom : cube) 
+            asms.push_back(atom);                
+        lbool r = l_undef;
+
+        ctx->get_fparams().m_max_conflicts = std::min((cube.size() + 1) *m_config.m_threads_max_conflicts, m_config.m_max_conflicts);
+        try {
+            r = ctx->check(asms.size(), asms.data());
+        }
+        catch (z3_error& err) {
+            b.set_exception(err.error_code());
+        }
+        catch (z3_exception& ex) {
+            b.set_exception(ex.what());
+        }
+        catch (...) {
+            b.set_exception("unknown exception");
+        }
+        asms.shrink(asms.size() - cube.size());
+        LOG_WORKER(1, " DONE checking cube " << r << "\n";);
+        return r;
+    }
+
+    expr_ref parallel2::worker::get_split_atom() {
+        expr_ref result(m);
+        double score = 0;
+        unsigned n = 0;
+
+        for (bool_var v = 0; v < ctx->get_num_bool_vars(); ++v) {
+            if (ctx->get_assignment(v) != l_undef)
+                continue;
+            expr* e = ctx->bool_var2expr(v);
+            if (!e)
+                continue;
+
+            double new_score = ctx->m_lit_scores[0][v] * ctx->m_lit_scores[1][v];
+
+            // decay the scores
+            ctx->m_lit_scores[0][v] /= 2;
+            ctx->m_lit_scores[1][v] /= 2;
+
+            if (new_score > score || !result || (new_score == score && m_rand(++n) == 0)) {
+                score = new_score;
+                result = e;
+            }
+        }
+        return result;
+    }
+
+    void parallel2::batch_manager::set_sat(ast_translation& l2g, model& m) {
+        std::scoped_lock lock(mux);
+        IF_VERBOSE(1, verbose_stream() << "Batch manager setting SAT.\n");
+        if (m_state != state::is_running)
+            return;
+        m_state = state::is_sat;
+        p.ctx.set_model(m.translate(l2g));
+        cancel_workers();
+    }
+
+    void parallel2::batch_manager::set_unsat(ast_translation& l2g, expr_ref_vector const& unsat_core) {
+        std::scoped_lock lock(mux);
+        IF_VERBOSE(1, verbose_stream() << "Batch manager setting UNSAT.\n");
+        if (m_state != state::is_running)
+            return;
+        m_state = state::is_unsat;    
+
+        // every time we do a check_sat call, don't want to have old info coming from a prev check_sat call
+        // the unsat core gets reset internally in the context after each check_sat, so we assert this property here
+        // takeaway: each call to check_sat needs to have a fresh unsat core
+        SASSERT(p.ctx.m_unsat_core.empty());
+        for (expr* e : unsat_core)
+            p.ctx.m_unsat_core.push_back(l2g(e));
+        cancel_workers();
+    }
+
+    void parallel2::batch_manager::set_exception(unsigned error_code) {
+        std::scoped_lock lock(mux);
+        IF_VERBOSE(1, verbose_stream() << "Batch manager setting exception code: " << error_code << ".\n");
+        if (m_state != state::is_running)
+            return;
+        m_state = state::is_exception_code;
+        m_exception_code = error_code;
+        cancel_workers();
+    }
+
+    void parallel2::batch_manager::set_exception(std::string const& msg) {
+        std::scoped_lock lock(mux);
+        IF_VERBOSE(1, verbose_stream() << "Batch manager setting exception msg: " << msg << ".\n");
+        if (m_state != state::is_running || m.limit().is_canceled())
+            return;
+        m_state = state::is_exception_msg;
+        m_exception_msg = msg;
+        cancel_workers();
+    }
+
+    void parallel2::batch_manager::report_assumption_used(ast_translation& l2g, expr* assumption) {
+        std::scoped_lock lock(mux);
+        p.m_assumptions_used.insert(l2g(assumption));
+    }
+
+    lbool parallel2::batch_manager::get_result() const {
+        if (m.limit().is_canceled())
+            return l_undef; // the main context was cancelled, so we return undef.
+        switch (m_state) {
+            case state::is_running: // batch manager is still running, but all threads have processed their cubes, which means all cubes were unsat
+                if (!m_search_tree.is_closed())
+                    throw default_exception("inconsistent end state");
+                if (!p.m_assumptions_used.empty()) {
+                    // collect unsat core from assumptions used, if any --> case when all cubes were unsat, but depend on nonempty asms, so we need to add these asms to final unsat core
+                    SASSERT(p.ctx.m_unsat_core.empty());
+                    for (auto a : p.m_assumptions_used)
+                        p.ctx.m_unsat_core.push_back(a);
+                }
+                return l_false;
+            case state::is_unsat:
+                return l_false;
+            case state::is_sat:
+                return l_true;
+            case state::is_exception_msg:
+                throw default_exception(m_exception_msg.c_str());
+            case state::is_exception_code:
+                throw z3_error(m_exception_code);
+            default:
+                UNREACHABLE();
+                return l_undef;
+        }
+    }
+
+    bool parallel2::batch_manager::get_cube(ast_translation& g2l, unsigned id, expr_ref_vector& cube, node*& n) {
+        cube.reset();
+        std::unique_lock<std::mutex> lock(mux);
+        node* t = nullptr;
+        while ((t = m_search_tree.activate_node(n)) == nullptr) {
+            // if all threads have reported they are done, then return false
+            // otherwise wait for condition variable
+            IF_VERBOSE(1, verbose_stream() << "waiting... " << "\n";);
+            if (m_search_tree.is_closed()) {
+                IF_VERBOSE(1, verbose_stream() << "all done\n";);
+                cv.notify_all();
+                return false;
+            }
+            cv.wait(lock);
+        }
+        IF_VERBOSE(1, m_search_tree.display(verbose_stream()); verbose_stream() << "\n";);
+        n = t;
+        while (t) {
+            if (cube_config::literal_is_null(t->get_literal()))
+                break;
+            expr_ref lit(g2l.to());
+            lit = g2l(t->get_literal().get());
+            cube.push_back(lit);
+            t = t->parent();
+        }
+        //        IF_VERBOSE(1, verbose_stream() << "got cube " << cube << " from " << " " << t->get_status() << "\n";);
+        return true;
+    }
+
+
+    void parallel2::batch_manager::initialize() {
+        m_state = state::is_running; 
+        m_search_tree.reset();
+        smt_parallel_params sp(p.ctx.m_params);
+        m_config.m_max_cube_depth = sp.max_cube_depth();
+        m_config.m_frugal_cube_only = sp.frugal_cube_only();
+        m_config.m_never_cube = sp.never_cube();
+        m_config.m_depth_splitting_only = sp.depth_splitting_only();
+        m_config.m_iterative_deepening = sp.iterative_deepening();
+        m_config.m_beam_search = sp.beam_search();
+        m_config.m_cubetree = sp.cubetree();
+    }
+
+    void parallel2::batch_manager::collect_statistics(::statistics& st) const {
+        //ctx->collect_statistics(st);
+        st.update("parallel-num_cubes", m_stats.m_num_cubes);
+        st.update("parallel-max-cube-size", m_stats.m_max_cube_depth);
+    }
+
+    lbool parallel2::operator()(expr_ref_vector const& asms) {
+        ast_manager& m = ctx.m;
+
+        if (m.has_trace_stream())
+            throw default_exception("trace streams have to be off in parallel mode");
+        
+        struct scoped_clear {
+            parallel2& p;
+            scoped_clear(parallel2& p) : p(p) {}
+            ~scoped_clear() { p.m_workers.reset(); p.m_assumptions_used.reset(); }
+        };
+        scoped_clear clear(*this);
+
+        {
+            m_batch_manager.initialize();
+            m_workers.reset();
+            scoped_limits sl(m.limit());
+            flet<unsigned> _nt(ctx.m_fparams.m_threads, 1);
+            SASSERT(num_threads > 1);
+            for (unsigned i = 0; i < num_threads; ++i)
+                m_workers.push_back(alloc(worker, i, *this, asms)); // i.e. "new worker(i, *this, asms)"
+                
+            // THIS WILL ALLOW YOU TO CANCEL ALL THE CHILD THREADS
+            // within the lexical scope of the code block, creates a data structure that allows you to push children
+            // objects to the limit object, so if someone cancels the parent object, the cancellation propagates to the children
+            // and that cancellation has the lifetime of the scope
+            // even if this code doesn't expliclty kill the main thread, still applies bc if you e.g. Ctrl+C the main thread, the children threads need to be cancelled
+            for (auto w : m_workers)
+                sl.push_child(&(w->limit()));
+
+            // Launch threads
+            vector<std::thread> threads(num_threads);
+            for (unsigned i = 0; i < num_threads; ++i) {
+                threads[i] = std::thread([&, i]() {
+                    m_workers[i]->run();
+                });
+            }
+
+            // Wait for all threads to finish
+            for (auto& th : threads)
+                th.join();
+
+            for (auto w : m_workers)
+                w->collect_statistics(ctx.m_aux_stats);     
+            m_batch_manager.collect_statistics(ctx.m_aux_stats);
+        }
+
+        return m_batch_manager.get_result(); // i.e. all threads have finished all of their cubes -- so if state::is_running is still true, means the entire formula is unsat (otherwise a thread would have returned l_undef)        
+    }
+
+}
+#endif

src/smt/smt_parallel2.h

@@ -0,0 +1,186 @@
+/*++
+Copyright (c) 2020 Microsoft Corporation
+
+Module Name:
+
+    smt_parallel.h
+
+Abstract:
+
+    Parallel SMT, portfolio loop specialized to SMT core.
+
+Author:
+
+    Ilana 2025
+
+Revision History:
+
+--*/
+#pragma once
+
+#include "smt/smt_context.h"
+#include "util/search_tree.h"
+#include <thread>
+#include <mutex>
+
+
+namespace smt {
+
+    struct cube_config {
+        using literal = expr_ref;
+        static bool literal_is_null(expr_ref const& l) { return l == nullptr; }
+        static std::ostream& display_literal(std::ostream& out, expr_ref const& l) { return out << mk_bounded_pp(l, l.get_manager()); }
+    };
+
+    class parallel2 {
+        context& ctx;
+        unsigned num_threads;
+
+        struct shared_clause {
+            unsigned source_worker_id;
+            expr_ref clause;
+        };
+
+        class batch_manager {        
+            enum state {
+                is_running,
+                is_sat,
+                is_unsat,
+                is_exception_msg,
+                is_exception_code
+            };
+            struct config {
+                unsigned m_max_cube_depth = 20;
+                bool m_frugal_cube_only = false;
+                bool m_never_cube = false; 
+                bool m_depth_splitting_only = false;
+                bool m_iterative_deepening = false;
+                bool m_beam_search = false;
+                bool m_cubetree = false;
+            };
+            struct stats {
+                unsigned m_max_cube_depth = 0;
+                unsigned m_num_cubes = 0;
+            };
+
+            ast_manager& m;
+            parallel2& p;
+            std::mutex mux;
+            std::condition_variable cv;
+            state m_state = state::is_running;
+            config m_config;
+            stats m_stats;
+            using node = search_tree::node<cube_config>;
+            search_tree::tree<cube_config> m_search_tree;
+            
+            unsigned m_exception_code = 0;
+            std::string m_exception_msg;
+            vector<shared_clause> shared_clause_trail; // store all shared clauses with worker IDs
+            obj_hashtable<expr> shared_clause_set; // for duplicate filtering on per-thread clause expressions
+
+            // called from batch manager to cancel other workers if we've reached a verdict
+            void cancel_workers() {
+                IF_VERBOSE(1, verbose_stream() << "Canceling workers\n");
+                for (auto& w : p.m_workers) 
+                    w->cancel();
+            }
+
+            void init_parameters_state();
+
+            bool is_assumption(expr* e) const {
+                return false; // m_assumptions_used.contains(e);
+            }
+
+        public:
+            batch_manager(ast_manager& m, parallel2& p) : m(m), p(p), m_search_tree(expr_ref(m)) { }
+
+            void initialize();
+
+            void set_unsat(ast_translation& l2g, expr_ref_vector const& unsat_core);
+            void set_sat(ast_translation& l2g, model& m);
+            void set_exception(std::string const& msg);
+            void set_exception(unsigned error_code);
+            void collect_statistics(::statistics& st) const;
+
+            bool get_cube(ast_translation& g2l, unsigned id, expr_ref_vector& cube, node*& n);
+            void backtrack(ast_translation& l2g, expr_ref_vector const& core, node* n);
+            void split(ast_translation& l2g, unsigned id, node* n, expr* atom);
+
+            void report_assumption_used(ast_translation& l2g, expr* assumption);
+            void collect_clause(ast_translation& l2g, unsigned source_worker_id, expr* clause);
+            expr_ref_vector return_shared_clauses(ast_translation& g2l, unsigned& worker_limit, unsigned worker_id);
+
+            lbool get_result() const;
+        };
+
+        class worker {
+            struct config {
+                unsigned m_threads_max_conflicts = 1000;
+                unsigned m_max_conflicts = 10000000;
+                bool m_relevant_units_only = true;
+                bool m_never_cube = false;
+                bool m_share_conflicts = true;
+                bool m_share_units = true;
+                double m_max_conflict_mul = 1.5;
+                bool m_share_units_initial_only = false;
+                bool m_cube_initial_only = false;
+                unsigned m_max_greedy_cubes = 1000;
+                unsigned m_num_split_lits = 2;
+                bool m_backbone_detection = false;
+                bool m_iterative_deepening = false;
+                bool m_beam_search = false;
+                bool m_explicit_hardness = false;
+                bool m_cubetree = false;
+            };
+
+            unsigned id; // unique identifier for the worker
+            parallel2& p;
+            batch_manager& b;
+            ast_manager m;
+            expr_ref_vector asms;
+            smt_params m_smt_params;
+            config m_config;
+            random_gen m_rand;
+            scoped_ptr<context> ctx;
+            ast_translation m_g2l, m_l2g;
+
+            unsigned m_num_shared_units = 0;
+            unsigned m_num_initial_atoms = 0;
+            unsigned m_shared_clause_limit = 0; // remembers the index into shared_clause_trail marking the boundary between "old" and "new" clauses to share
+            
+            expr_ref get_split_atom();
+
+            lbool check_cube(expr_ref_vector const& cube);
+            void share_units(ast_translation& l2g);
+
+        public:
+            worker(unsigned id, parallel2& p, expr_ref_vector const& _asms);
+            void run();
+            
+            void collect_shared_clauses(ast_translation& g2l);
+
+            void cancel();
+            void collect_statistics(::statistics& st) const;
+
+            reslimit& limit() {
+                return m.limit();
+            }
+
+        };
+
+        obj_hashtable<expr> m_assumptions_used; // assumptions used in unsat cores, to be used in final core
+        batch_manager m_batch_manager;
+        scoped_ptr_vector<worker> m_workers;
+
+    public:
+        parallel2(context& ctx) : 
+            ctx(ctx),
+            num_threads(std::min(
+                (unsigned)std::thread::hardware_concurrency(),
+                ctx.get_fparams().m_threads)),
+            m_batch_manager(ctx.m, *this) {}
+
+        lbool operator()(expr_ref_vector const& asms);
+    };
+
+}

src/util/search_tree.h

@@ -128,13 +128,17 @@ namespace search_tree {
     public:
 
         tree(literal const& null_literal) : m_null_literal(null_literal) {
-            m_root = alloc(node<Config>, m_null_literal, nullptr);
-            m_root->set_status(status::active);
+            reset();
         }
 
         void set_seed(unsigned seed) {
             m_rand.set_seed(seed);
         }
+
+        void reset() {
+            m_root = alloc(node<Config>, m_null_literal, nullptr);
+            m_root->set_status(status::active);
+        }
         
         // Split current node if it is active.
         // After the call, n is open and has two children.