This library provides a minimal cross-platform interface for task parallelism. Given a computation that is partitioned into a set of interdependent tasks, the library efficiently distributes this work to a thread pool using lock-free queues, while respecting dependencies between tasks.
Each task is associated with a callback function that is potentially invoked multiple times if the task consists of multiple work units. This whole process is arbitrarily recursive: task callbacks can submit further jobs, wait for their completion, etc. Parallel loops, reductions, and more complex graph-based computations are easily realized using these abstractions.
This project is internally implemented in C++11, but exposes the main functionality using a pure C99 API, along with a header-only C++11 convenience wrapper. It has no dependencies other than CMake and a C++11-capable compiler. The entire project requires less than 1000 lines of header and implementation code (according to cloc).
This library is part of the larger Dr.Jit project and parallelizes workloads generated by the Dr.Jit-Core library. However, this project has no dependencies on these parent projects and can be used in any other context.
Many of my previous projects have built on Intel's Thread Building Blocks for exactly this type of functionality. Unfortunately, large portions of TBB's task interface were recently deprecated as part of the oneAPI / oneTBB transition. Rather than struggling with this complex dependency, I decided to build something minimal and stable that satisfies my requirements.
The follow examples showcase the C++11 interface, which is a thin header-only layer over the C99 API.
template <typename T, typename Func>
void parallel_for(const blocked_range<T> &range, Func &&func, Pool *pool = nullptr);This function submits a single task consisting of a arbitrarily many work units
that are processed in blocks of a specified size, and waits for their
completion. If no thread pool Pool * is specified, the default pool will be
used (and created on the fly, if needed).
Example:
#include <nanothread/nanothread.h>
namespace dr = drjit;
int main(int, char **) {
int result[100];
// Call the provided lambda function 99 times with blocks of size 1
dr::parallel_for(
dr::blocked_range<uint32_t>(/* begin = */ 0, /* end = */ 100, /* block_size = */ 1),
// The callback is allowed to be a stateful lambda function
[&](dr::blocked_range<uint32_t> range) {
for (uint32_t i = range.begin(); i != range.end(); ++i) {
printf("Worker thread %u is starting to process work unit %u\n",
pool_thread_id(), i);
// Write to variables defined in the caller's frame
result[i] = i;
}
}
);
}Small amounts of work that only consist of a single block will immediately be
executed on the calling thread instead of involving the thread pool. Exceptions
occurring during parallel execution will be captured and re-thrown by
dr::parallel_for.
Parallel for loops can also run asynchronously—in that case, the function
immediately returns a Task * handle that can be used to wait for
completion, or to schedule child tasks, whose execution will be delayed until
all parents have completed.
template <typename T, typename Func>
Task *parallel_for_async(const blocked_range<T> &range, Func &&func,
std::initializer_list<Task *> parents = { },
Pool *pool = nullptr);The returned task handle must eventually be released using the functions
task_release(Task *) (which is instantaneous) or
task_wait_and_release(Task *) (which blocks until the task has terminated).
A failure to do so will leak memory.
Example:
#include <nanothread/nanothread.h>
namespace dr = drjit;
int main(int, char **) {
// Schedule task 1
Task *task_1 = dr::parallel_for_async(
dr::blocked_range<uint32_t>(/* ... */),
[&](dr::blocked_range<uint32_t> range) { /* ... */ }
);
// Schedule task 2
Task *task_2 = dr::parallel_for_async(
dr::blocked_range<uint32_t>(/* ... */),
[&](dr::blocked_range<uint32_t> range) { /* ... */ }
);
// Schedule task 3 ...
Task *task_3 = dr::parallel_for_async(
dr::blocked_range<uint32_t>(/* ... */),
[&](dr::blocked_range<uint32_t> range) { /* ... */ },
{ task_1, task_2 } // ... <- but don't execute until these tasks are done
);
task_release(task_1);
task_release(task_2);
task_wait_and_release(task_3);
}If a task only consists of single-threaded work that cannot easily be converted
into a parallel for loop, the function do_async provides an more
convenient interface that is analogous to parallel_for_async with a
blocked_range of size 1.
template <typename Func>
Task *do_async(Func &&func, std::initializer_list<Task *> parents = {},
Pool *pool = nullptr);The following code fragment submits a single task consisting of 100 work units and waits for its completion.
#include <nanothread/nanothread.h>
#include <stdio.h>
#include <unistd.h>
// Task callback function. Will be called with index = 0..99
void my_task(uint32_t index, void *payload) {
printf("Worker thread %u is starting to process work unit %u\n",
pool_thread_id(), index);
// Use payload to communicate some data to the caller
((uint32_t *) payload)[index] = index;
}
int main(int argc, char** argv) {
uint32_t temp[100];
// Create a worker per CPU thread
Pool *pool = pool_create(NANOTHREAD_AUTO);
// Synchronous interface: submit a task and wait for it to complete
task_submit_and_wait(
pool,
100, // How many work units does this task contain?
my_task, // Function to be executed
temp // Optional payload, will be passed to function
);
// .. contents of 'temp' are now ready ..
// Clean up used resources
pool_destroy(pool);
}Tasks can also be executed asynchronously, in which case extra steps must be added to wait for tasks, and to release task handles.
/// Heap-allocate scratch space for inter-task communication
uint32_t *payload = malloc(100 * sizeof(uint32_t));
/// Submit a task and return immediately
Task *task_1 = task_submit(
pool,
100, // How many work units does this task contain?
my_task_1, // Function to be executed
payload, // Optional payload, will be passed to function
0, // Size of the payload (only relevant if it should be copied)
nullptr, // Payload deletion callback
0 // Enforce asynchronous execution even if task is small?
);
/// Submit a task that is dependent on other tasks (specifically task_1)
Task *task_2 = task_submit_dep(
pool,
&task_1, // Pointer to a list of parent tasks
1, // Number of parent tasks
100, // How many work units does this task contain?
my_task_2, // Function to be executed
payload, // Optional payload, will be passed to function
0, // Size of the payload (only relevant if it should be copied)
free, // Call free(payload) once this task completes
0 // Enforce asynchronous execution even if task is small?
);
/* Now that the parent-child relationship is specified,
the handle of task 1 can be released */
task_release(task_1);
// Wait for the completion of task 2 and also release its handle
task_wait_and_release(task_2);The complete API is documented in the file nanothread/nanothread.h.
This library follows a lock-free design: tasks that are ready for execution are stored in a Michael-Scott queue that is continuously polled by workers, and task submission/removal relies on atomic compare-and-swap (CAS) operations. Workers that idle for more than roughly 50 milliseconds are put to sleep until more work becomes available.
The lock-free design is important: the central data structures of a task
submission system are heavily contended, and traditional abstractions (e.g.
std::mutex) will immediately put contending threads to sleep to defer lock
resolution to the OS kernel. The associated context switches produce an
extremely large overhead that can make a parallel program orders of magnitude
slower than a single-threaded version.
The implementation catches exception that occur while executing parallel work and re-throws them the caller's thread (this part is of no relevance for software written in C99).
The functions task_wait() and task_wait_and_release() do not just
wait---they spend the wait time fetching and executing work from the task
queue, which has two implications: first, it is not wasteful to wait for the
completion of another task while executing a task. Second, the thread pool can
be set to a size of zero via pool_create(0) or pool_set_size(pool, 0),
in which case the program will still run correctly without launching any
additional threads.