The Chapel Programming Language

Productive parallel computing at every scale.
writeln("Hello, world!");

// create a parallel task per processor core
coforall tid in 0..<here.maxTaskPar do
  writeln("Hello from task ", tid);

// print these 1,000 messages in parallel using all cores
forall i in 1..1000 do
  writeln("Hello from iteration ", i);
// print a message per compute node
coforall loc in Locales do
  on loc do
    writeln("Hello from locale ", loc.id);

// print a message per core per compute node
coforall loc in Locales do
  on loc do
    coforall tid in 0..<here.maxTaskPar do
      writeln("Hello from task ", tid, " on locale ", loc.id);

// print 1,000 messages in parallel using all nodes and cores
use BlockDist;
const Inds = blockDist.createDomain(1..1000);
forall i in Inds do
  writeln("Hello from iteration ", i, " running on locale ", here.id);
use IO;

// read in a file containing 'city name;temperature' lines (1BRC-style)
const stats = [line in stdin.lines()] new cityTemperature(line);
writeln(stats);

record cityTemperature {
  const city: string;  // city name
  const temp: real;    // temperature

  proc init(str: string) {
    const words = str.split(";");
    this.city = words[0];
    this.temp = words[1]: real;
  }
}
// set different values at runtime with command line arguments
// e.g. --n=2048 --numSteps=256 --alpha=0.8
config const n = 1000,
             numSteps = 100,
             alpha = 1.0;

const fullDomain = {1..n},
      interior   = {2..n-1};

var u: [fullDomain] real = 1.0; 
u[n/4..3*n/4] = 2.0;  // make the middle a bit hotter

var un = u;

for 1..numSteps {
  forall i in interior do  // shared-memory parallelism
    u[i] = un[i] + alpha * (un[i-1] - 2*un[i] + un[i+1]);  
  un <=> u;  // swap the two arrays
}

writeln(un);
use Random, Math;

const nGpus = here.gpus.size,
      n     = Locales.size*nGpus;

var A: [1..n, 1..n] real;

fillRandom(A);

// use all nodes
coforall (loc, localRowStart) in zip(Locales, 1.. by nGpus) do on loc { 
  // and all GPUs within each
  coforall (gpu, row) in zip(here.gpus, localRowStart..) do on gpu {    
    var B: [1..n] real = A[row, ..];    // copy a row from device to host
    B = asin(B);                        // compute (kernel launch)
    A[row, ..] = B;                     // copy the row back
  }
}

writeln(A);

Users Love It

The use of Chapel worked as intended: the code maintenance is very much reduced, and its readability is astonishing. This enables undergraduate students to contribute to its development, something almost impossible to think of when using very complex software.

- Éric Laurendeau, Professor, Polytechnique Montréal

A lot of the nitty gritty is hidden from you until you need to know it. ... It feels like the complexity grows as you get more comfortable -- rather than being hit with everything at once.

- Tess Hayes, Developer, Bytoa

Chapel in Production

What’s New?

Announcing Chapel 2.5!

By Brad Chamberlain, Michael Ferguson, Lydia Duncan, Jade Abraham, Ben Harshbarger, Daniel Fedorin on June 12, 2025

Highlights from the June 2025 release of Chapel 2.5

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Paper and Presentation Refresh

on June 10, 2025

We've just completed a long-overdue refresh of Chapel-related papers and presentations from the past year or so

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10 Myths About Scalable Parallel Programming Languages (Redux), Part 2: Past Failures and Future Attempts

By Brad Chamberlain on May 28, 2025

Another archival post from the IEEE TCSC blog in 2012 with a current reflection on it

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Public Weekly Deep-Dive / Demo Meeting Launched

on May 20, 2025

In addition to our weekly project meeting, we've now added a weekly meeting slot for deep-dive discussions and demos

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Quarterly Newsletter - Spring 2025

on May 20, 2025

Our spring quarter newsletter is now available, covering recent and upcomign talks, new public project meetings, and more.

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Chapel/Fortran Interop in an Ocean Model: Introduction

By Brandon Neth, Michelle Strout on April 24, 2025

An introduction to interoperating between Chapel and Fortran

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