`std::system_error‘ usingthe ＜future＞ in C++

Effective Parallel Programming in C++ with std::future

Parallel programming in C++ has been greatly simplified with the introduction of the std::thread, std::async, std::future, and std::promise classes in C++11. These tools allow developers to write concurrent code that is both efficient and easier to understand. However, as with any powerful tool, there are pitfalls to avoid. This tutorial focuses on std::future and how to use it effectively while avoiding common mistakes, illustrated by a real-world example of a bug encountered during the parallelization of an ElGamal encryption scheme.

Understanding std::future

std::future represents a future result of a computation that may or may not have completed yet. It is typically used in conjunction with std::async, which launches a potentially asynchronous task that returns a std::future.

Common Mistakes and Misconceptions

1. Over-subscription: Creating more threads than the hardware can efficiently handle, leading to excessive context switching and poor performance.
2. Resource Exhaustion: Launching too many tasks without considering system limits, potentially resulting in std::system_error.
3. Improper Synchronization: Accessing shared resources without proper locking or using futures can lead to race conditions.

Real-World Example: Parallelizing ElGamal Encryption

Consider the task of parallelizing the encryption, decryption, and ciphertext randomization methods of an ElGamal encryption scheme. A naive approach might involve using std::async for each chunk of data, leading to the creation of thousands of threads and possibly hitting system limits.

The Bug

Attempting to process a large message (e.g., 32,768 bytes) by launching a separate async task for each byte or small chunk can exhaust system resources, resulting in a std::system_error with the message “Resource temporarily unavailable”.

Solution Strategy

To solve this problem and avoid common pitfalls, follow these guidelines:

Limit the Number of Concurrent Threads

Instead of launching a thread or async task for each piece of work, divide the work into larger chunks and process each chunk in a separate thread, up to a certain maximum number of threads.

Example Code Snippet

const size_t numThreads = std::thread::hardware_concurrency();
const size_t batchSize = (data.size() + numThreads - 1) / numThreads;

std::vector<std::future<void>> futures;

for (size_t t = 0; t < numThreads; ++t) {
    futures.push_back(std::async(std::launch::async, [/* capture necessary variables */](){
        // Process a batch of data
    }));
}

for (auto& future : futures) {
    future.get(); // Ensure all futures have completed
}

Key Takeaways

• Batch Processing: Divide your data into batches and process each batch in parallel to reduce the overhead and resource usage associated with managing a large number of threads.
• Hardware Concurrency: Use std::thread::hardware_concurrency() to determine an appropriate number of threads that matches your hardware capabilities.
• Error Handling: Implement error handling within your asynchronous tasks to manage exceptions and ensure robustness.

Best Practices for Using std::future in Parallel Programming

1. Balance Workload and Resources: Adapt the number of threads to your hardware and workload to find an optimal balance between performance and resource utilization.
2. Error Handling: Use try-catch blocks within tasks to handle exceptions and ensure that your program remains robust under all conditions.
3. Proper Synchronization: When accessing shared resources, ensure proper synchronization to avoid race conditions and data corruption.

Conclusion

Parallel programming with std::future and std::async offers a powerful model for writing concurrent C++ applications. By understanding and avoiding common pitfalls, such as over-subscription and resource exhaustion, you can ensure that your applications are both efficient and robust. Remember to balance concurrency with the workload and system capabilities, and always implement proper error handling and synchronization mechanisms.