diff --git a/examples/distributed-solver/distributed-solver.cpp b/examples/distributed-solver/distributed-solver.cpp
index d9a1050f32d..809f47a7159 100644
--- a/examples/distributed-solver/distributed-solver.cpp
+++ b/examples/distributed-solver/distributed-solver.cpp
@@ -91,7 +91,8 @@ int main(int argc, char* argv[])
     if (argc == 2 && (std::string(argv[1]) == "--help")) {
         if (rank == 0) {
             std::cerr << "Usage: " << argv[0]
-                      << " [executor] [num_grid_points] " << std::endl;
+                      << " [executor] [num_grid_points] [num_iterations] "
+                      << std::endl;
         }
         std::exit(-1);
     }
@@ -104,6 +105,8 @@ int main(int argc, char* argv[])
     const auto executor_string = argc >= 2 ? argv[1] : "reference";
     const auto grid_dim =
         static_cast<gko::size_type>(argc >= 3 ? std::atoi(argv[2]) : 100);
+    const auto num_iters =
+        static_cast<gko::size_type>(argc >= 4 ? std::atoi(argv[3]) : 1000);
 
     // Pick the requested executor.
     std::map<std::string, std::function<std::shared_ptr<gko::Executor>()>>
@@ -216,7 +219,8 @@ int main(int argc, char* argv[])
     auto Ainv =
         solver::build()
             .with_criteria(
-                gko::stop::Iteration::build().with_max_iters(100u).on(exec),
+                gko::stop::Iteration::build().with_max_iters(num_iters).on(
+                    exec),
                 gko::stop::ResidualNorm<ValueType>::build()
                     .with_baseline(gko::stop::mode::absolute)
                     .with_reduction_factor(1e-4)
diff --git a/examples/distributed-solver/doc/intro.dox b/examples/distributed-solver/doc/intro.dox
index 4f5e6532b6f..da8f7cb13aa 100644
--- a/examples/distributed-solver/doc/intro.dox
+++ b/examples/distributed-solver/doc/intro.dox
@@ -2,7 +2,7 @@
 <h1>Introduction</h1>
 This distributed solver example should help you understand the basics of using Ginkgo in a distributed setting.
 The example will solve a simple 1D Laplace equation where the system can be distributed row-wise to multiple processes.
-To run the solver with multiple processes, use `mpirun -n NUM_PROCS ./distributed-solver [executor] [num_grid_points]`.
+To run the solver with multiple processes, use `mpirun -n NUM_PROCS ./distributed-solver [executor] [num_grid_points] [num_iterations]`.
 
 If you are using GPU devices, please make sure that you run this example with at most as many processes as you have GPU
 devices available.
diff --git a/include/ginkgo/core/base/executor.hpp b/include/ginkgo/core/base/executor.hpp
index 7623411d657..7f9689bda97 100644
--- a/include/ginkgo/core/base/executor.hpp
+++ b/include/ginkgo/core/base/executor.hpp
@@ -1526,10 +1526,7 @@ class CudaExecutor : public detail::ExecutorBase<CudaExecutor>,
         this->get_exec_info().num_pu_per_cu = 0;
         this->CudaExecutor::populate_exec_info(
             machine_topology::get_instance());
-        if (this->get_exec_info().closest_pu_ids.size()) {
-            machine_topology::get_instance()->bind_to_pus(
-                this->get_closest_pus());
-        }
+
         // it only gets attribute from device, so it should not be affected by
         // DeviceReset.
         this->set_gpu_property();
@@ -1732,10 +1729,7 @@ class HipExecutor : public detail::ExecutorBase<HipExecutor>,
         this->get_exec_info().num_computing_units = 0;
         this->get_exec_info().num_pu_per_cu = 0;
         this->HipExecutor::populate_exec_info(machine_topology::get_instance());
-        if (this->get_exec_info().closest_pu_ids.size()) {
-            machine_topology::get_instance()->bind_to_pus(
-                this->get_closest_pus());
-        }
+
         // it only gets attribute from device, so it should not be affected by
         // DeviceReset.
         this->set_gpu_property();