diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index e4a000e..865113b 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -72,6 +72,8 @@ if(BUILD_CUDA AND BUILD_TBB)
   target_link_libraries(alien_reco PRIVATE CoroutineTests CUDA::cudart TBB::tbb)
   add_executable(alien_delegate alien_delegate.cpp)
   target_link_libraries(alien_delegate PRIVATE CoroutineTests CUDA::cudart TBB::tbb)
+  add_executable(alien_event_poll alien_event_poll.cpp)
+  target_link_libraries(alien_event_poll PRIVATE CoroutineTests CUDA::cudart TBB::tbb)
   if(BUILD_STDEXEC)
     add_executable(exec_reco_stdexec exec_reco.cpp)
     target_link_libraries(exec_reco_stdexec PRIVATE CoroutineTests CUDA::cudart TBB::tbb STDEXEC::stdexec)
@@ -83,5 +85,7 @@ if(BUILD_CUDA AND BUILD_TBB)
     target_link_libraries(capy_reco PRIVATE CoroutineTests CUDA::cudart TBB::tbb Boost::capy)
     add_executable(capy_delegate capy_delegate.cpp)
     target_link_libraries(capy_delegate PRIVATE CoroutineTests CUDA::cudart TBB::tbb Boost::capy)
+    add_executable(capy_event_poll capy_event_poll.cpp)
+    target_link_libraries(capy_event_poll PRIVATE CoroutineTests CUDA::cudart TBB::tbb Boost::capy)
   endif()
 endif()
diff --git a/examples/README.md b/examples/README.md
index 943bbe5..67b1f58 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -172,4 +172,10 @@ These examples show a setup and coroutine chain loosely inspired by track recons
 
 Link: [alien_delegate.cpp](alien_delegate.cpp), [exec_delegate.cpp](exec_delegate.cpp), [capy_delegate.cpp](capy_delegate.cpp)
 
-These examples are a modification of [reconstuction examples](#reconstruction) to delegate all the calls to CUDA API that are happening inside the task to execute on a designated thread by changing scheduler/executor. This represents a specific optimization: invoking CUDA APIs from multiple threads can incur performance penalties due to contention on internal CUDA locks. This contention can be reduced by delegating the calls to a single thread. The examples demonstrate how this optimization can be implemented using coroutines schedulers/executors.
+These examples are a modification of [reconstruction examples](#reconstruction) to delegate all the calls to CUDA API that are happening inside the task to execute on a designated thread by changing scheduler/executor. This represents a specific optimization: invoking CUDA APIs from multiple threads can incur performance penalties due to contention on internal CUDA locks. This contention can be reduced by delegating the calls to a single thread. The examples demonstrate how this optimization can be implemented using coroutines schedulers/executors.
+
+## Event poll
+
+Link: [alien_event_poll.cpp](alien_event_poll.cpp), [capy_event_poll.cpp](capy_event_poll.cpp)
+
+These examples are a variant of [delegate examples](#delegate) in which awaiting completion of CUDA operations is done by repeatedly querying the event state instead of using a callback as in earlier examples. All the queries are executed by a specific thread.
diff --git a/examples/alien_event_poll.cpp b/examples/alien_event_poll.cpp
new file mode 100644
index 0000000..1b49d7e
--- /dev/null
+++ b/examples/alien_event_poll.cpp
@@ -0,0 +1,303 @@
+#include <cuda_runtime_api.h>
+#include <driver_types.h>
+#include <tbb/task_arena.h>
+
+#include <cstddef>
+#include <format>
+#include <iostream>
+#include <stdexcept>
+#include <string_view>
+#include <vector>
+
+#include "CoroutineTests/alien/counting_scope.hpp"
+#include "CoroutineTests/alien/schedule_on.hpp"
+#include "CoroutineTests/alien/subtool.hpp"
+#include "CoroutineTests/alien/sync_wait.hpp"
+#include "CoroutineTests/alien/tool.hpp"
+#include "CoroutineTests/threadpool.hpp"
+#include "logging_utils.hpp"  // log, format_name
+
+#define ERROR_CHECK_CUDA(EXP)                                              \
+    do {                                                                   \
+        cudaError_t errorCode = EXP;                                       \
+        if (errorCode != cudaSuccess) {                                    \
+            throw std::runtime_error(                                      \
+                std::format("CUDA error at {}:{}: {}", __FILE__, __LINE__, \
+                            cudaGetErrorString(errorCode)));               \
+        }                                                                  \
+    } while (false)
+
+using namespace CoroutineTests::alien;
+
+template <typename T>
+struct DeviceBuffer {
+    T* ptr = nullptr;
+    std::size_t size = 0;
+};
+
+template <typename F>
+tool::Task<void> delegate(F f) {
+    f();
+    co_return;
+}
+
+struct Retry {
+    bool await_ready() const noexcept { return false; }
+    template <typename Promise>
+    void await_suspend(std::coroutine_handle<Promise> handle) const {
+        handle.promise().reschedule();
+    }
+    void await_resume() const noexcept {}
+};
+
+tool::Task<void> poll(cudaEvent_t event) {
+    auto status = cudaSuccess;
+    log() << "Polling for event completion..." << std::endl;
+    while ((status = cudaEventQuery(event)) == cudaErrorNotReady) {
+        log() << "Event not ready, retrying..." << std::endl;
+        co_await Retry{};
+    }
+    ERROR_CHECK_CUDA(status);
+}
+
+subtool::Task<DeviceBuffer<int>> clusterization(
+    DeviceBuffer<int> cells, cudaStream_t stream, cudaEvent_t event,
+    std::function<void(std::coroutine_handle<>)> delegation_scheduler,
+    std::string_view parent) {
+
+    const auto self = format_name(parent, "clusterization");
+    log(self) << "Starting clusterization" << std::endl;
+
+    const auto nCells = static_cast<int>(cells.size);
+
+    // Copy cells back to host to count non-zero entries
+    auto h_cells = std::vector<int>(nCells);
+
+    co_await schedule_on(
+        delegation_scheduler, delegate([&]() {
+            log(self) << "Delegated copy of cells from device to host"
+                      << std::endl;
+            ERROR_CHECK_CUDA(cudaMemcpyAsync(h_cells.data(), cells.ptr,
+                                             nCells * sizeof(int),
+                                             cudaMemcpyDeviceToHost, stream));
+            ERROR_CHECK_CUDA(cudaEventRecord(event, stream));
+        }));
+
+    co_await schedule_on(delegation_scheduler, poll(event));
+
+    auto nClusters = 0;
+    for (auto v : h_cells)
+        if (v != 0)
+            ++nClusters;
+
+    log(self) << "Found " << nClusters << " clusters" << std::endl;
+
+    // Allocate clusters of appropriate size on device
+    int* d_clusters = nullptr;
+
+    co_await schedule_on(
+        delegation_scheduler, delegate([&]() {
+            log(self) << "Delegated allocation of clusters on device"
+                      << std::endl;
+            ERROR_CHECK_CUDA(
+                cudaMallocAsync(reinterpret_cast<void**>(&d_clusters),
+                                nClusters * sizeof(int), stream));
+
+            // Write some dummy data to the clusters buffer to simulate work
+            ERROR_CHECK_CUDA(cudaMemsetAsync(d_clusters, 0,
+                                             nClusters * sizeof(int), stream));
+            ERROR_CHECK_CUDA(cudaMemsetAsync(
+                d_clusters, 1, nClusters / 2 * sizeof(int), stream));
+        }));
+
+    co_return DeviceBuffer<int>{d_clusters,
+                                static_cast<std::size_t>(nClusters)};
+}
+
+subtool::Task<DeviceBuffer<int>> seeding(
+    DeviceBuffer<int> clusters, cudaStream_t stream, cudaEvent_t event,
+    std::function<void(std::coroutine_handle<>)> delegation_scheduler,
+    std::string_view parent) {
+
+    const auto self = format_name(parent, "seeding");
+    log(self) << "Starting seeding" << std::endl;
+
+    const auto nClusters = static_cast<int>(clusters.size);
+
+    // Copy clusters to host to count non-zero entries
+    auto h_clusters = std::vector<int>(nClusters);
+
+    co_await schedule_on(
+        delegation_scheduler, delegate([&]() {
+            log(self) << "Delegated copy of clusters to host" << std::endl;
+            ERROR_CHECK_CUDA(cudaMemcpyAsync(h_clusters.data(), clusters.ptr,
+                                             nClusters * sizeof(int),
+                                             cudaMemcpyDeviceToHost, stream));
+            ERROR_CHECK_CUDA(cudaEventRecord(event, stream));
+        }));
+
+    co_await schedule_on(delegation_scheduler, poll(event));
+
+    int nSeeds = 0;
+    for (auto v : h_clusters)
+        if (v != 0)
+            ++nSeeds;
+
+    log(self) << "Found " << nSeeds << " seeds" << std::endl;
+
+    // Allocate seeds of appropriate size on device
+    int* d_seeds = nullptr;
+
+    co_await schedule_on(
+        delegation_scheduler, delegate([&]() {
+            log(self) << "Delegated allocation of seeds on device" << std::endl;
+            ERROR_CHECK_CUDA(cudaMallocAsync(reinterpret_cast<void**>(&d_seeds),
+                                             nSeeds * sizeof(int), stream));
+
+            // Write some dummy data to the seeds buffer to simulate work
+            ERROR_CHECK_CUDA(
+                cudaMemsetAsync(d_seeds, 0, nSeeds * sizeof(int), stream));
+            ERROR_CHECK_CUDA(
+                cudaMemsetAsync(d_seeds, 1, nSeeds / 2 * sizeof(int), stream));
+        }));
+
+    co_return DeviceBuffer<int>{d_seeds, static_cast<std::size_t>(nSeeds)};
+}
+
+tool::Task<tool::StatusCode> reconstruct(
+    cudaStream_t stream, cudaEvent_t event,
+    std::function<void(std::coroutine_handle<>)> delegation_scheduler,
+    std::string_view parent) {
+    const auto self = format_name(parent, "reconstruction");
+    log(self) << "Starting reconstruction" << std::endl;
+
+    // Allocate some dummy input data on the device
+    auto cells = DeviceBuffer<int>{nullptr, 1000};
+    co_await schedule_on(
+        delegation_scheduler, delegate([&]() {
+            log(self) << "Delegated allocation of input data on device"
+                      << std::endl;
+            ERROR_CHECK_CUDA(
+                cudaMallocAsync(reinterpret_cast<void**>(&cells.ptr),
+                                cells.size * sizeof(int), stream));
+            ERROR_CHECK_CUDA(cudaMemsetAsync(cells.ptr, 1,
+                                             cells.size * sizeof(int), stream));
+        }));
+
+    // Run the clusterization and seeding steps
+    auto clusters = co_await clusterization(cells, stream, event,
+                                            delegation_scheduler, self);
+    auto seeds =
+        co_await seeding(clusters, stream, event, delegation_scheduler, self);
+
+    // Cleanup
+    co_await schedule_on(
+        delegation_scheduler, delegate([&]() {
+            log(self) << "Delegated cleanup of device memory" << std::endl;
+            ERROR_CHECK_CUDA(cudaFreeAsync(cells.ptr, stream));
+            ERROR_CHECK_CUDA(cudaFreeAsync(clusters.ptr, stream));
+            ERROR_CHECK_CUDA(cudaFreeAsync(seeds.ptr, stream));
+            ERROR_CHECK_CUDA(cudaEventRecord(event, stream));
+        }));
+
+    co_await schedule_on(delegation_scheduler, poll(event));
+
+    log(self) << "Finishing reconstruction" << std::endl;
+    co_return tool::StatusCode::SUCCESS;
+}
+
+int main() {
+    int deviceCount = 0;
+    auto error_id = cudaGetDeviceCount(&deviceCount);
+
+    if (error_id != cudaSuccess) {
+        std::cout << "cudaGetDeviceCount returned "
+                  << static_cast<int>(error_id) << "\n"
+                  << cudaGetErrorString(error_id) << "\n";
+        return EXIT_FAILURE;
+    }
+
+    if (deviceCount == 0) {
+        std::cout << "No CUDA devices found.\n";
+        return EXIT_FAILURE;
+    }
+
+    log() << "main Starting" << std::endl;
+
+    tbb::task_arena task_arena{2, 0};
+    auto scheduler = [&task_arena](std::coroutine_handle<> handle) {
+        task_arena.enqueue([handle]() { handle.resume(); });
+    };
+
+    CoroutineTests::Threadpool delegation_threadpool(1);
+    auto delegation_scheduler =
+        [&delegation_threadpool](std::coroutine_handle<> handle) {
+            delegation_threadpool.enqueue_task(handle);
+        };
+
+    {
+        cudaStream_t stream;
+        ERROR_CHECK_CUDA(cudaStreamCreate(&stream));
+        cudaEvent_t event;
+        ERROR_CHECK_CUDA(
+            cudaEventCreateWithFlags(&event, cudaEventDisableTiming));
+        std::cout << "--- Single event, synchronous wait for completion ---\n";
+        log() << "main Launching algorithm..." << std::endl;
+        auto status =
+            sync_wait(scheduler,
+                      reconstruct(stream, event, delegation_scheduler, "main"));
+        log() << "main Final status of algorithm " << status << "" << std::endl;
+        ERROR_CHECK_CUDA(cudaEventDestroy(event));
+        ERROR_CHECK_CUDA(cudaStreamDestroy(stream));
+    }
+
+    {
+        std::cout << "--- Multiple events, wait for all to complete ---\n";
+
+        auto streams = std::vector<cudaStream_t>(2);
+        auto events = std::vector<cudaEvent_t>(streams.size());
+        auto status = std::vector<tool::StatusCode>(streams.size());
+        for (std::size_t i = 0; i < streams.size(); ++i) {
+            ERROR_CHECK_CUDA(cudaStreamCreate(&streams.at(i)));
+            ERROR_CHECK_CUDA(cudaEventCreateWithFlags(&events.at(i),
+                                                      cudaEventDisableTiming));
+        }
+
+        auto scope = counting_scope{};
+        log() << "main Launching algorithms..." << std::endl;
+
+        auto payload = [](std::vector<cudaStream_t> streams,
+                          std::vector<cudaEvent_t> events,
+                          std::vector<tool::StatusCode>& statuses,
+                          std::function<void(std::coroutine_handle<>)>
+                              delegation_scheduler,
+                          int i) -> tool::Task<void> {
+            const auto name = std::format("event{}:main", i);
+            auto& stream = streams.at(i);
+            auto& event = events.at(i);
+            auto& status = statuses.at(i);
+            status = co_await reconstruct(
+                stream, event, std::move(delegation_scheduler), name);
+            co_return;
+        };
+
+        for (std::size_t i = 0; i < streams.size(); ++i) {
+            scope.spawn(scheduler,
+                        payload(streams, events, status, delegation_scheduler,
+                                static_cast<int>(i)));
+        }
+        scope.join();
+
+        for (std::size_t i = 0; i < streams.size(); ++i) {
+            ERROR_CHECK_CUDA(cudaEventDestroy(events.at(i)));
+            ERROR_CHECK_CUDA(cudaStreamDestroy(streams.at(i)));
+        }
+
+        log() << "main All algorithms completed. Final statuses: ";
+        for (auto s : status) {
+            std::cout << s << ' ';
+        }
+        std::cout << std::endl;
+    }
+    return 0;
+}
diff --git a/examples/capy_event_poll.cpp b/examples/capy_event_poll.cpp
new file mode 100644
index 0000000..099101c
--- /dev/null
+++ b/examples/capy_event_poll.cpp
@@ -0,0 +1,281 @@
+#include <cuda_runtime_api.h>
+#include <tbb/task_arena.h>
+
+#include <boost/capy.hpp>
+#include <boost/capy/ex/thread_pool.hpp>
+#include <cstddef>
+#include <iostream>
+#include <latch>
+#include <string_view>
+#include <utility>
+
+#include "capy_task_arena_executor.hpp"  // TaskArenaExecutor
+#include "logging_utils.hpp"             // log, format_name
+#include "statuscode.hpp"                // StatusCodeImpl
+
+namespace tools {
+struct Tag {
+    static constexpr const char* name = "tools";
+};
+using StatusCode = StatusCodeImpl<Tag>;
+}  // namespace tools
+
+#define ERROR_CHECK_CUDA(EXP)                                              \
+    do {                                                                   \
+        cudaError_t errorCode = EXP;                                       \
+        if (errorCode != cudaSuccess) {                                    \
+            throw std::runtime_error(                                      \
+                std::format("CUDA error at {}:{}: {}", __FILE__, __LINE__, \
+                            cudaGetErrorString(errorCode)));               \
+        }                                                                  \
+    } while (false)
+
+template <typename T>
+struct DeviceBuffer {
+    T* ptr = nullptr;
+    std::size_t size = 0;
+};
+
+template <typename F>
+boost::capy::task<void> delegate(F&& f) {
+    std::forward<F>(f)();
+    co_return;
+}
+
+struct Retry {
+    bool await_ready() const noexcept { return false; }
+    void await_suspend(std::coroutine_handle<> handle,
+                       boost::capy::io_env const* env) const noexcept {
+        env->executor.post(handle);
+    }
+    void await_resume() const noexcept {}
+};
+
+boost::capy::task<void> poll(cudaEvent_t event) {
+    auto status = cudaSuccess;
+    log() << "Polling for event completion..." << std::endl;
+    while ((status = cudaEventQuery(event)) == cudaErrorNotReady) {
+        log() << "Event not ready, retrying..." << std::endl;
+        co_await Retry{};
+    }
+    ERROR_CHECK_CUDA(status);
+}
+
+boost::capy::task<DeviceBuffer<int>> clusterization(
+    DeviceBuffer<int> cells, cudaStream_t stream, cudaEvent_t event,
+    boost::capy::thread_pool& delegation_thread, std::string_view parent) {
+
+    const auto self = format_name(parent, "clusterization");
+    log(self) << "Starting clusterization" << std::endl;
+
+    const auto nCells = static_cast<int>(cells.size);
+
+    // Copy cells back to host to count non-zero entries
+    auto h_cells = std::vector<int>(nCells);
+
+    co_await boost::capy::run(delegation_thread.get_executor())(delegate([&]() {
+        log(self) << "Delegated copy of cells from device to host" << std::endl;
+        ERROR_CHECK_CUDA(cudaMemcpyAsync(h_cells.data(), cells.ptr,
+                                         nCells * sizeof(int),
+                                         cudaMemcpyDeviceToHost, stream));
+        ERROR_CHECK_CUDA(cudaEventRecord(event, stream));
+    }));
+
+    co_await boost::capy::run(delegation_thread.get_executor())(poll(event));
+
+    auto nClusters = 0;
+    for (auto v : h_cells)
+        if (v != 0)
+            ++nClusters;
+
+    log(self) << "Found " << nClusters << " clusters" << std::endl;
+
+    // Allocate clusters of appropriate size on device
+    int* d_clusters = nullptr;
+
+    co_await boost::capy::run(delegation_thread.get_executor())(delegate([&]() {
+        log(self) << "Delegated allocation of clusters on device" << std::endl;
+        ERROR_CHECK_CUDA(cudaMallocAsync(reinterpret_cast<void**>(&d_clusters),
+                                         nClusters * sizeof(int), stream));
+
+        // Write some dummy data to the clusters buffer to simulate work
+        ERROR_CHECK_CUDA(
+            cudaMemsetAsync(d_clusters, 0, nClusters * sizeof(int), stream));
+        ERROR_CHECK_CUDA(cudaMemsetAsync(d_clusters, 1,
+                                         nClusters / 2 * sizeof(int), stream));
+    }));
+
+    co_return DeviceBuffer<int>{d_clusters,
+                                static_cast<std::size_t>(nClusters)};
+}
+
+boost::capy::task<DeviceBuffer<int>> seeding(
+    DeviceBuffer<int> clusters, cudaStream_t stream, cudaEvent_t event,
+    boost::capy::thread_pool& delegation_thread, std::string_view parent) {
+
+    const auto self = format_name(parent, "seeding");
+    log(self) << "Starting seeding" << std::endl;
+
+    const auto nClusters = static_cast<int>(clusters.size);
+
+    // Copy clusters to host to count non-zero entries
+    auto h_clusters = std::vector<int>(nClusters);
+
+    co_await boost::capy::run(delegation_thread.get_executor())(delegate([&]() {
+        log(self) << "Delegated copy of clusters to host" << std::endl;
+        ERROR_CHECK_CUDA(cudaMemcpyAsync(h_clusters.data(), clusters.ptr,
+                                         nClusters * sizeof(int),
+                                         cudaMemcpyDeviceToHost, stream));
+        ERROR_CHECK_CUDA(cudaEventRecord(event, stream));
+    }));
+
+    co_await boost::capy::run(delegation_thread.get_executor())(poll(event));
+
+    int nSeeds = 0;
+    for (auto v : h_clusters)
+        if (v != 0)
+            ++nSeeds;
+
+    log(self) << "Found " << nSeeds << " seeds" << std::endl;
+
+    // Allocate seeds of appropriate size on device
+    int* d_seeds = nullptr;
+
+    co_await boost::capy::run(delegation_thread.get_executor())(delegate([&]() {
+        log(self) << "Delegated allocation of seeds on device" << std::endl;
+        ERROR_CHECK_CUDA(cudaMallocAsync(reinterpret_cast<void**>(&d_seeds),
+                                         nSeeds * sizeof(int), stream));
+
+        // Write some dummy data to the seeds buffer to simulate work
+        ERROR_CHECK_CUDA(
+            cudaMemsetAsync(d_seeds, 0, nSeeds * sizeof(int), stream));
+        ERROR_CHECK_CUDA(
+            cudaMemsetAsync(d_seeds, 1, nSeeds / 2 * sizeof(int), stream));
+    }));
+
+    co_return DeviceBuffer<int>{d_seeds, static_cast<std::size_t>(nSeeds)};
+}
+
+boost::capy::task<tools::StatusCode> reconstruct(
+    cudaStream_t stream, cudaEvent_t event,
+    boost::capy::thread_pool& delegation_thread, std::string parent) {
+    const auto self = format_name(parent, "reconstruction");
+    log(self) << "Starting reconstruction" << std::endl;
+
+    // Allocate some dummy input data on the device
+    auto cells = DeviceBuffer<int>{nullptr, 1000};
+    co_await boost::capy::run(delegation_thread.get_executor())(delegate([&]() {
+        log(self) << "Delegated allocation of input data on device"
+                  << std::endl;
+        ERROR_CHECK_CUDA(cudaMallocAsync(reinterpret_cast<void**>(&cells.ptr),
+                                         cells.size * sizeof(int), stream));
+        ERROR_CHECK_CUDA(
+            cudaMemsetAsync(cells.ptr, 1, cells.size * sizeof(int), stream));
+    }));
+
+    // Run the clusterization and seeding steps
+    auto clusters =
+        co_await clusterization(cells, stream, event, delegation_thread, self);
+    auto seeds =
+        co_await seeding(clusters, stream, event, delegation_thread, self);
+
+    // Cleanup
+    co_await boost::capy::run(delegation_thread.get_executor())(delegate([&]() {
+        log(self) << "Delegated cleanup of device memory" << std::endl;
+        ERROR_CHECK_CUDA(cudaFreeAsync(cells.ptr, stream));
+        ERROR_CHECK_CUDA(cudaFreeAsync(clusters.ptr, stream));
+        ERROR_CHECK_CUDA(cudaFreeAsync(seeds.ptr, stream));
+        ERROR_CHECK_CUDA(cudaEventRecord(event, stream));
+    }));
+
+    co_await boost::capy::run(delegation_thread.get_executor())(poll(event));
+
+    log(self) << "Finishing reconstruction" << std::endl;
+    co_return tools::StatusCode::SUCCESS;
+}
+
+int main() {
+    int deviceCount = 0;
+    auto error_id = cudaGetDeviceCount(&deviceCount);
+
+    if (error_id != cudaSuccess) {
+        std::cout << "cudaGetDeviceCount returned "
+                  << static_cast<int>(error_id) << "\n"
+                  << cudaGetErrorString(error_id) << "\n";
+        return EXIT_FAILURE;
+    }
+
+    if (deviceCount == 0) {
+        std::cout << "No CUDA devices found.\n";
+        return EXIT_FAILURE;
+    }
+
+    log() << "main Starting" << std::endl;
+
+    auto task_arena = tbb::task_arena{2, 0};
+    auto context = TaskArenaContext(task_arena);
+    auto executor = TaskArenaExecutor(context);
+    auto delegation_thread = boost::capy::thread_pool(1);
+
+    {
+        std::cout << "--- Single event, synchronous wait for completion ---\n";
+        cudaStream_t stream;
+        ERROR_CHECK_CUDA(cudaStreamCreate(&stream));
+        cudaEvent_t event;
+        ERROR_CHECK_CUDA(
+            cudaEventCreateWithFlags(&event, cudaEventDisableTiming));
+        auto final_result = tools::StatusCode{};
+        auto done = std::latch{1};
+        auto result_handler = [&done, &final_result](tools::StatusCode code) {
+            final_result = code;
+            done.count_down();
+        };
+
+        log() << "main Launching algorithm..." << std::endl;
+        boost::capy::run_async(executor, result_handler)(
+            reconstruct(stream, event, delegation_thread, "main"));
+        done.wait();
+        log() << "main Final status of algorithm " << final_result << ""
+              << std::endl;
+        ERROR_CHECK_CUDA(cudaEventDestroy(event));
+        ERROR_CHECK_CUDA(cudaStreamDestroy(stream));
+    }
+
+    {
+        std::cout << "--- Multiple events, wait for all to complete ---\n ";
+
+        auto streams = std::vector<cudaStream_t>(2);
+        auto events = std::vector<cudaEvent_t>(streams.size());
+        auto status = std::vector<tools::StatusCode>(streams.size());
+        for (std::size_t i = 0; i < streams.size(); ++i) {
+            ERROR_CHECK_CUDA(cudaStreamCreate(&streams.at(i)));
+            ERROR_CHECK_CUDA(cudaEventCreateWithFlags(&events.at(i),
+                                                      cudaEventDisableTiming));
+        }
+
+        auto done = std::latch{static_cast<std::ptrdiff_t>(streams.size())};
+        log() << "main Launching algorithms..." << std::endl;
+
+        for (std::size_t i = 0; i < streams.size(); ++i) {
+            auto result_handler = [&done, &status, i](tools::StatusCode code) {
+                status.at(i) = code;
+                done.count_down();
+            };
+            boost::capy::run_async(executor, result_handler)(
+                reconstruct(streams.at(i), events.at(i), delegation_thread,
+                            "event" + std::to_string(i)));
+        }
+        done.wait();
+        for (std::size_t i = 0; i < streams.size(); ++i) {
+            ERROR_CHECK_CUDA(cudaEventDestroy(events.at(i)));
+            ERROR_CHECK_CUDA(cudaStreamDestroy(streams.at(i)));
+        }
+
+        log() << "main All algorithms completed. Final statuses: ";
+        for (auto s : status) {
+            std::cout << s << ' ';
+        }
+        std::cout << std::endl;
+    }
+    return 0;
+}