Parallel and concurrent programming in Haskell can be tricky if you’re not familiar with the language, so this tutorial will help you learn to use skeletons and threads to structure your code. I’ll also touch on the use of nest and the lazy evaluation strategy. And of course, the book will include a chapter on the importance of parallelism. So let’s get started!
Using skeletons to structure parallel and concurrent programming in Haskell
Using skeletons to structure parallel or concurrent programming in Haskell is an excellent way to achieve high-performance application design. Skeletons can be transient or persistent and are an extension of C, an imperative language. Transient skeletons are instantiated on each invocation and persistent skeletons are invoked throughout the application. This feature allows for both implicit and explicit interaction in the programming model, depending on your use case.
Parallel skeletons provide the foundation for efficient parallel implementations. These skeletons define the communication and data access patterns among all the parallel processes. By providing these transformations, the skeletons reduce the number of errors in parallel programming. Moreover, they reduce the complexity of concurrent programs. With this feature, skeletons can be used to design programs for multi-core and cloud environments.
Using threads for parallel and concurrent programming is very easy in Haskell. Threads are simply separate Haskell processes. Each of these threads gets approximately the same amount of work, and you can make use of this common sense by using light, parallel threads. Ideally, you should use one thread per logical process, but it’s up to you. Read on for more information.
While using threads is a powerful feature of Haskell, it can also introduce some performance challenges. In parallel programming, the programmer has less control than in lower-level languages, and fixing performance issues is difficult. The main problem you’ll face is partitioning the problem. You need to think of it as dividing the problem into multiple small pieces. This makes it easier to distribute the workload between threads.
Parallelism means breaking the slow task into smaller pieces, so that it can be executed more quickly. In parallel programming, you can use multiple-core machines and multiple-threads to increase the speed of your programs. Each process represents a single instance of a program. Each process is made up of one or more threads, which share a common environment. The thread system is implemented by the Control.Concurrent module. This module is responsible for creating new threads from the main thread.
NESL is an open-source language that combines functional programming with array and parallel algorithms. Its nested data parallelism provides the benefits of data parallelism, but it is particularly useful for irregular algorithms. NESL’s language-based performance model gives a formal way to calculate how much work a program does, which can be directly related to its running time on parallel machines.
Flattening is an approach to solving the problem of mapping irregular parallelism into a regular model. Flattening is an approach that requires further optimizations. Nesl has a compiler, called Nessie, which generates CUDA code for Nvidia GPUs. Nessie makes use of a complicated shape analysis to enable fusion optimizations and smart kernel scheduling.
A modern general-purpose language that implements nested data parallelism is called Data Parallel Haskell. This book covers its implementation in GHC, the state-of-the-art compiler. It highlights how the nested to flat transformation is used to transform nested data into parallelism. The book is intended for practitioners and includes numerous exercises and examples. It is written by Simon Marlow, who built the parallel runtime system in GHC and is still its master.
Using lazy evaluation strategy
Parallel and concurrent programming in Haskell requires an efficient evaluation strategy. Unlike most programming languages, Haskell has no side effects. A non-pure function relies on more than its arguments. Thus, a lazy evaluation strategy allows for parallel computations. The key to parallel programming in Haskell is defeating lazy evaluation. Using a strategy like rpar dot rdeepseq will help you implement parallelism without excessive overhead.
Today, parallel and concurrent programming in Haskell is commonly used in industry. Facebook’s Sigma system, which uses Haskell as a DSL for detecting internet abuse, runs on thousands of multicore servers located in data centers around the world. The Haxl programming model, a subset of Parallel Haskell, is particularly suitable for applications that fetch data from network services. Concurrent Haskell is a different implementation of parallel programming in Haskell.