Learn CHAPEL with Real Code Examples

Chapel allows developers to write parallel programs without dealing with low-level threading details.

It provides constructs for task parallelism, data parallelism, and heterogeneous computing.

Ideal for scientific computing, simulations, and large-scale HPC applications.

Parallel loops (forall) and tasking

Domain and array types for data parallelism

User-defined types and generics

Modules for code organization

Interoperability with C and other languages

Tasks and parallel loops

Domains and arrays for data distribution

Variables and types, including user-defined

Modules and namespaces

Interoperability with C and external libraries

src/ - Chapel source code

lib/ - reusable modules

tests/ - validation and benchmark scripts

docs/ - documentation

configs/ - HPC platform configurations

Write Chapel source code (.chpl files)

Compile using chpl compiler

Run on local machine or HPC cluster

Debug parallel execution and performance

Optimize data distribution and tasking

Beginner: serial Chapel programs and simple parallel loops

Intermediate: parallel arrays and domains

Advanced: tasking and distributed execution

Expert: scalable HPC algorithms

Research: simulations and large-scale computation

Higher-level than MPI/OpenMP

More focused on HPC than general-purpose languages

Offers global-view abstraction

Supports both task and data parallelism

Smaller ecosystem than Python or C++ in HPC

2009 – Chapel initial release by Cray

2010s – Added parallel loops, arrays, and modules

2015 – Improved distributed memory support

2018 – Async tasks and HPC enhancements

2020s – Ongoing development and HPC community adoption

Domain: index set for arrays

Array: collection of elements over a domain

Task: unit of parallel execution

Domain map: data distribution strategy

Module: reusable code unit