Discuss concurrency and parallelism in Haskell, including the techniques and libraries available for concurrent programming.

Concurrency and parallelism are important concepts in Haskell that enable efficient utilization of resources and improve program performance. Haskell provides powerful techniques and libraries to support concurrent programming. Let's explore concurrency and parallelism in Haskell, along with the techniques and libraries available.

Concurrency in Haskell:
Concurrency refers to the ability of a program to execute multiple tasks independently, allowing for better responsiveness and improved resource utilization. Haskell provides lightweight threads, known as "green threads," which are managed by a runtime system. These threads are purely concurrent, meaning they are not bound to physical threads. Haskell's concurrency model is based on the concept of "Concurrency with Explicit Communication" (CEC).

The `Control.Concurrent` module in the Haskell standard library provides functions and abstractions for working with concurrency. Some key features and techniques for concurrency in Haskell include:

1. `forkIO`: The `forkIO` function creates a new thread and allows concurrent execution of computations.
2. `MVar`: The `MVar` (mutable variable) is a synchronization primitive in Haskell that allows safe communication and coordination between threads. It provides synchronized access to shared resources.
3. Software Transactional Memory (STM): STM is a powerful mechanism in Haskell for managing shared state in a concurrent setting. It ensures atomicity and isolation of transactions, preventing data races and ensuring consistency.

Parallelism in Haskell:
Parallelism involves executing computations simultaneously to achieve faster execution and take advantage of multicore processors. Haskell provides several techniques and libraries for achieving parallelism:

1. Strategies: Strategies allow you to express parallelism at a higher level by specifying how a computation can be evaluated in parallel. Strategies encapsulate the evaluation and scheduling of computations, making it easier to parallelize code.
2. Parallel List Comprehensions: Haskell provides parallel versions of list comprehensions using the `parList` function. It allows for parallel evaluation of list expressions, improving performance for certain types of computations.
3. `Control.Parallel` and `Control.Parallel.Strategies`: These modules provide functions and combinators for expressing explicit parallelism in Haskell programs. They allow you to evaluate expressions in parallel, define parallel computations, and control granularity.
4. Data Parallelism: Haskell also supports data parallelism through libraries like `repa` (REgular PArallel arrays) and `accelerate`. These libraries provide high-level abstractions for expressing parallel computations over arrays.
5. Parallelism with `par` and `pseq`: Haskell's `par` and `pseq` functions allow for fine-grained control over evaluation order and expressing dependencies between computations. They help in specifying parallelism explicitly at the expression level.

Libraries for Concurrent and Parallel Programming:
Haskell offers several libraries to simplify concurrent and parallel programming:

1. `async`: The `async` library provides a convenient way to work with asynchronous computations, enabling composition and coordination of concurrent tasks.
2. `stm`: The `stm` library provides a rich set of abstractions for working with Software Transactional Memory. It allows safe manipulation of shared state in a concurrent setting.
3. `monad-par`: The `monad-par` library provides a monadic interface for expressing parallel computations. It abstracts over the underlying parallel runtime, making it easier to write parallel code.
4. `parallel`: The `parallel` library offers various combinators and functions for explicit parallelism, enabling fine-grained control over parallel computations.

By leveraging these techniques and libraries, Haskell developers can effectively utilize concurrency and parallelism, leading to faster and more efficient programs.