A simple and fast C++ thread pool implementation capable of running task graphs (2407.15805v2)
Abstract: In this paper, the author presents a simple and fast C++ thread pool implementation capable of running task graphs. The implementation is publicly available on GitHub, see https://github.com/dpuyda/scheduling.
Summary
- The paper introduces a minimalistic thread pool design with fewer than 1000 lines of code that supports complex task graphs.
- It employs a work-stealing algorithm using the Chase-Lev deque, achieving performance comparable to established solutions like Taskflow.
- The implementation uses only C++20 features, enhancing portability and eliminating third-party dependencies.
A Simple and Fast C++ Thread Pool Implementation Capable of Running Task Graphs
The paper under consideration presents an efficient and minimalistic implementation of a C++ thread pool capable of executing task graphs. Authored by Dmytro Puyda, the implementation aims to offset common issues in multithreaded environments, such as context switching overhead and the inefficiency of creating and destroying threads frequently.
Key Contributions
The primary contributions of this paper are:
- Minimalistic Implementation: The proposed thread pool implementation is succinct, comprising less than one thousand lines of C++ code, making it easy to understand and extend.
- Performance-Oriented Design: Benchmarks indicate that the performance of this thread pool is comparable to existing solutions like Taskflow.
- Independence from Third-Party Dependencies: The implementation relies solely on C++20 standards, avoiding external libraries to reduce complexity and dependency issues.
Implementation Details
The thread pool leverages a work-stealing algorithm, implemented through the Chase-Lev deque, a well-established data structure. Each worker thread is provided with its own task queue to minimize contention. When its own queue is empty, a worker thread attempts to steal tasks from other queues.
Work-Stealing Deque
The Chase-Lev deque used in this implementation is a lock-free structure where the owner thread performs operations at one end (push/pop), while other threads perform steals from the opposite end. However, there is an intricate challenge in ensuring the correctness of concurrent operations. Past implementations have had issues with memory model correctness, specifically the use of atomic thread fences. The paper references modifications to mitigate these issues, including examples from Google's Filament, which avoid false positives in thread sanitizers.
Task Graph Execution
Task graphs are supported via simple wrappers over std::function<void()>. Each task node maintains references to its successors and the count of uncompleted predecessors. This allows dynamic scheduling of tasks where completion of predecessor tasks triggers the execution of successors.
Benchmarks
The author provides benchmark comparisons against Taskflow, particularly evaluating CPU and wall time performance on tasks such as the Fibonacci sequence computation. The results suggest that the proposed thread pool offers competitive performance, validating the author's claim of efficiency.
Practical Implications
The simplicity and performance characteristics of this thread pool make it suitable for integration into commercial projects requiring efficient task scheduling. Given the minimal dependency on external libraries and adherence to standard C++20, the implementation is highly portable and can be easily adapted or extended.
Future Developments
The discussion in the paper opens avenues for further research in refining work-stealing mechanisms, particularly for newer memory models. Moreover, the capability to integrate with emerging C++ standards and possibly support modules could enhance the portability and functionality of the thread pool.
Usage
The paper includes extensive code snippets and usage instructions, indicating the ease with which this thread pool can be integrated into existing C++ projects. The examples demonstrate basic asynchronous task execution as well as the construction and execution of task graphs.
For more detailed instructions and additional benchmarks, the implementation is made publicly available on GitHub (https://github.com/dpuyda/scheduling).
Conclusion
This paper presents a robust and minimalistic thread pool implementation designed for efficiency and ease of use. It addresses key challenges in multithreaded programming by providing a performant work-stealing mechanism and supporting complex task graphs without third-party dependencies. This combination makes it a potent tool for developers looking to efficiently harness the power of multithreading in C++ applications. Future work may focus on optimizing this implementation further and exploring additional features to broaden its applicability.