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Handle concurrent mode oom and avoid unnecessary deep copy of problem_t #541

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Merged
rapids-bot merged 2 commits into from
Oct 27, 2025
Merged

Handle concurrent mode oom and avoid unnecessary deep copy of problem_t #541

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6376963
Handle oom and avoid unecessary copy of problem_t in concurrent mode
hlinsen Oct 24, 2025
1c982b2
Fix style
hlinsen Oct 24, 2025
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3 changes: 3 additions & 0 deletions cpp/src/dual_simplex/barrier.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -3575,6 +3575,9 @@ lp_status_t barrier_solver_t<i_t, f_t>::solve(f_t start_time,
} catch (const raft::cuda_error& e) {
settings.log.debug("Error in barrier_solver_t: %s\n", e.what());
return lp_status_t::NUMERICAL_ISSUES;
} catch (const rmm::out_of_memory& e) {
settings.log.debug("Out of memory in barrier_solver_t: %s\n", e.what());
return lp_status_t::NUMERICAL_ISSUES;
}
}

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10 changes: 4 additions & 6 deletions cpp/src/linear_programming/solve.cu
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Expand Up @@ -444,7 +444,7 @@ optimization_problem_solution_t<i_t, f_t> run_barrier(
{
// Convert data structures to dual simplex format and back
dual_simplex::user_problem_t<i_t, f_t> dual_simplex_problem =
cuopt_problem_to_simplex_problem<i_t, f_t>(problem);
cuopt_problem_to_simplex_problem<i_t, f_t>(problem.handle_ptr, problem);
auto sol_dual_simplex = run_barrier(dual_simplex_problem, settings, timer);
return convert_dual_simplex_sol(problem,
std::get<0>(sol_dual_simplex),
Expand Down Expand Up @@ -515,7 +515,7 @@ optimization_problem_solution_t<i_t, f_t> run_dual_simplex(
{
// Convert data structures to dual simplex format and back
dual_simplex::user_problem_t<i_t, f_t> dual_simplex_problem =
cuopt_problem_to_simplex_problem<i_t, f_t>(problem);
cuopt_problem_to_simplex_problem<i_t, f_t>(problem.handle_ptr, problem);
auto sol_dual_simplex = run_dual_simplex(dual_simplex_problem, settings, timer);
return convert_dual_simplex_sol(problem,
std::get<0>(sol_dual_simplex),
Expand Down Expand Up @@ -671,16 +671,14 @@ optimization_problem_solution_t<i_t, f_t> run_concurrent(
// Initialize the dual simplex structures before we run PDLP.
// Otherwise, CUDA API calls to the problem stream may occur in both threads and throw graph
// capture off
auto barrier_handle = raft::handle_t(*op_problem.get_handle_ptr());
detail::problem_t<i_t, f_t> d_barrier_problem(problem);
auto barrier_handle = raft::handle_t(*op_problem.get_handle_ptr());
rmm::cuda_stream_view barrier_stream = rmm::cuda_stream_per_thread;
d_barrier_problem.handle_ptr = &barrier_handle;
raft::resource::set_cuda_stream(barrier_handle, barrier_stream);
// Make sure allocations are done on the original stream
problem.handle_ptr->sync_stream();

dual_simplex::user_problem_t<i_t, f_t> dual_simplex_problem =
cuopt_problem_to_simplex_problem<i_t, f_t>(d_barrier_problem);
cuopt_problem_to_simplex_problem<i_t, f_t>(&barrier_handle, problem);
// Create a thread for dual simplex
std::unique_ptr<
std::tuple<dual_simplex::lp_solution_t<i_t, f_t>, dual_simplex::lp_status_t, f_t, f_t, f_t>>
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22 changes: 12 additions & 10 deletions cpp/src/linear_programming/translate.hpp
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Expand Up @@ -28,21 +28,21 @@ namespace cuopt::linear_programming {

template <typename i_t, typename f_t>
static dual_simplex::user_problem_t<i_t, f_t> cuopt_problem_to_simplex_problem(
detail::problem_t<i_t, f_t>& model)
raft::handle_t const* handle_ptr, detail::problem_t<i_t, f_t>& model)
{
dual_simplex::user_problem_t<i_t, f_t> user_problem(model.handle_ptr);
dual_simplex::user_problem_t<i_t, f_t> user_problem(handle_ptr);

int m = model.n_constraints;
int n = model.n_variables;
int nz = model.nnz;
user_problem.num_rows = m;
user_problem.num_cols = n;
user_problem.objective = cuopt::host_copy(model.objective_coefficients);
user_problem.objective = cuopt::host_copy(model.objective_coefficients, handle_ptr->get_stream());

dual_simplex::csr_matrix_t<i_t, f_t> csr_A(m, n, nz);
csr_A.x = cuopt::host_copy(model.coefficients);
csr_A.j = cuopt::host_copy(model.variables);
csr_A.row_start = cuopt::host_copy(model.offsets);
csr_A.x = cuopt::host_copy(model.coefficients, handle_ptr->get_stream());
csr_A.j = cuopt::host_copy(model.variables, handle_ptr->get_stream());
csr_A.row_start = cuopt::host_copy(model.offsets, handle_ptr->get_stream());

csr_A.to_compressed_col(user_problem.A);

Expand All @@ -51,8 +51,10 @@ static dual_simplex::user_problem_t<i_t, f_t> cuopt_problem_to_simplex_problem(
user_problem.range_rows.clear();
user_problem.range_value.clear();

auto model_constraint_lower_bounds = cuopt::host_copy(model.constraint_lower_bounds);
auto model_constraint_upper_bounds = cuopt::host_copy(model.constraint_upper_bounds);
auto model_constraint_lower_bounds =
cuopt::host_copy(model.constraint_lower_bounds, handle_ptr->get_stream());
auto model_constraint_upper_bounds =
cuopt::host_copy(model.constraint_upper_bounds, handle_ptr->get_stream());

// All constraints have lower and upper bounds
// lr <= a_i^T x <= ur
Expand All @@ -79,7 +81,7 @@ static dual_simplex::user_problem_t<i_t, f_t> cuopt_problem_to_simplex_problem(
}
user_problem.num_range_rows = user_problem.range_rows.size();
std::tie(user_problem.lower, user_problem.upper) =
extract_host_bounds<f_t>(model.variable_bounds, model.handle_ptr);
extract_host_bounds<f_t>(model.variable_bounds, handle_ptr);
user_problem.problem_name = model.original_problem_ptr->get_problem_name();
if (model.row_names.size() > 0) {
user_problem.row_names.resize(m);
Expand All @@ -97,7 +99,7 @@ static dual_simplex::user_problem_t<i_t, f_t> cuopt_problem_to_simplex_problem(
user_problem.obj_scale = model.presolve_data.objective_scaling_factor;
user_problem.var_types.resize(n);

auto model_variable_types = cuopt::host_copy(model.variable_types);
auto model_variable_types = cuopt::host_copy(model.variable_types, handle_ptr->get_stream());
for (int j = 0; j < n; ++j) {
user_problem.var_types[j] =
model_variable_types[j] == var_t::CONTINUOUS
Expand Down