How can you design a programming language that is efficient and easy to parallelize?

1 Trade-offs and goals

The first step in designing a parallel programming language is to identify the trade-offs and goals that you want to achieve. For example, do you want to optimize for performance, scalability, portability, simplicity, or expressiveness? Depending on your priorities, you may have to sacrifice some features or aspects of your language. For instance, if you want to achieve high performance, you may have to use low-level constructs and explicit memory management, which can make your language more complex and error-prone.

2 Parallelism models

The next step is to choose a parallelism model that suits your language and your target domain. A parallelism model is a way of organizing and coordinating the parallel execution of tasks or data. There are many parallelism models, such as shared memory, message passing, data parallelism, task parallelism, or functional parallelism. Each model has its own advantages and disadvantages, and may require different language features or constructs to support it. For example, shared memory requires synchronization mechanisms, such as locks or semaphores, to prevent data races and inconsistencies. Message passing requires communication primitives, such as channels or sockets, to exchange data between processes or threads. Data parallelism requires operations that can be applied to collections or arrays of data in parallel, such as map or reduce. Task parallelism requires mechanisms to create and manage independent tasks or subroutines, such as futures or promises. Functional parallelism requires functions that are pure and free of side effects, such as lambda expressions or closures.

3 Language features

The third step is to design language features that enable and facilitate parallelism. These features can be either built-in or library-based, depending on the desired control and flexibility for the programmer. Examples of language features that support parallelism include concurrency primitives like threads, processes, coroutines, or actors; parallel data structures like concurrent queues, hash tables, or vectors; parallel algorithms like sorting, searching, or matrix multiplication; parallel annotations that indicate or direct the parallel execution of certain parts of the code; and parallel abstractions such as streams, pipelines, or monads. These abstractions can help hide the low-level details of parallelism and allow the programmer to focus on the logic or behavior of the program.