This lecture introduces task-based parallelism and concurrency in C++, contrasting it with data parallelism. The primary goal is to explain how to write asynchronous code that leverages multi-core and distributed computing resources effectively, focusing on managing data dependencies between tasks to improve performance and code readability.
Task-Based Parallelism vs. Data Parallelism: Task-based parallelism focuses on distributing and concurrently executing different tasks, potentially unrelated, unlike data parallelism which applies the same operation to all data elements. Task parallelism is better suited for handling tasks with varying computational needs.
Asynchrony: The lecture introduces asynchrony as a key concept in task-based parallelism, enabling concurrent execution and improved resource utilization. Asynchronous operations allow tasks to start before previous tasks are fully completed, improving overall throughput.
Futures: Futures are C++ objects used to represent the results of asynchronous operations, enabling synchronization without explicit thread management. Methods like get, wait, wait_for, and wait_until are used to access or wait for results from futures.
Data Dependencies and Futures: Futures represent data dependencies between tasks, enabling the system to schedule tasks efficiently based on these dependencies. This approach simplifies programming and improves performance by avoiding unnecessary waits and context switching.
Avoiding Sequential Code: The lecture emphasizes minimizing sequential code sections, highlighting the limitations of join parallelism due to uneven work distribution among cores and advocating for task-based parallelism to better utilize resources.
