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A taste of parallel programming with Chapel (in one hour)

Abstract: Chapel is a relatively new high-level programming language for shared- and distributed-memory machines. It combines the ease-of-use of Python and the speed of C++ and is the perfect language to learn the basics of parallel programming, whether you are trying to accelerate your computation on a multi-core laptop or on an HPC cluster. In this one-hour hands-on introduction I will go over several of Chapel's high-level abstractions.

Requirements: All attendees will need to bring their laptops with a remote ssh client installed (on Windows I recommend the free edition of https://mobaxterm.mobatek.net/download.html; on Mac and Linux no need to install anything). No need to install Chapel -- we'll play with it on a cluster, and I will provide guest accounts.

You can find a much longer version of these notes at:

Task parallelism on a single locale

We'll use a Slurm cluster inside a VM:

ssh user01@206.12.100.115   # user01..user10
$ . /home/centos/startSingleLocale.sh
$ which chpl
$ mkdir tmp && cd tmp

A Chapel program always starts as a single main thread. You can then start concurrent tasks with the begin statement. A task spawned by the begin statement will run in a different thread while the main thread continues its normal execution. Let's start a new code begin.chpl with the following lines:

var x = 100;
writeln('This is the main thread starting first task');
begin {
  var count = 0;
  while count < 10 {
    count += 1;
    writeln('thread 1: ', x + count);
  }
}
writeln('This is the main thread starting second task');
begin {
  var count = 0;
  while count < 10 {
    count += 1;
    writeln('thread 2: ', x + count);
  }
}
writeln('This is the main thread, I am done ...');

$ chpl begin.chpl -o begin
$ ./begin

If we have many cores, this would normally run on three cores. In our case, I don't know how many cores our code ran on -- most likely on two on our login node. Let us submit a job to the cluster asking for two cores on a single node (one MPI task, two cores inside), by writing job.sh:

#!/bin/bash
#SBATCH --cpus-per-task=2   # number of cores per task
#SBATCH --mem-per-cpu=1000
#SBATCH --time=00:5:00   # maximum walltime
./begin

$ sbatch job.sh
$ squeue -u user01

The command coforall will start a new task for each iteration. Each tasks will then perform all the instructions inside the curly brackets, and then all tasks will synchronize at the end. Let's write coforall.chpl:

var x = 10;
config var numthreads = 2;
writeln('This is the main task: x = ', x);
coforall taskid in 1..numthreads do {
  var c = taskid**2;
  writeln('this is task ', taskid, ': my value of c is ', c, ' and x is ', x);
}
writeln('This message will not appear until all tasks are done ...');

$ chpl coforall.chpl -o coforall
$ sed -i -e 's|./begin|./coforall|' job.sh
$ sbatch job.sh
$ squeue -u user01

Let's modify the number of threads:

$ sed -i -e 's|./coforall|./coforall --numthreads=5|' job.sh
$ sbatch job.sh
$ squeue -u user01

Messages appear in random order since they run in parallel in two threads. Let's make them appear in the right order, by writing from each thread into a string, and then printing this string in serial:

2a3
> var messages: [1..numthreads] string;
6c7
<   writeln('this is task ', taskid, ': my value of c is ', c, ' and x is ', x);
---
>   messages[taskid] = 'this is task ' + taskid + ': my value of c is ' + c + ' and x is ' + x;
8a10,11
> for i in 1..numthreads do  // serial loop, will be printed in sequential order
>   writeln(messages[i]);

Data parallelism on a single locale

As we mentioned in the previous section, Data Parallelism is a style of parallel programming in which parallelism is driven by computations over collections of data elements or their indices. The main tool for this in Chapel is a forall loop -- it'll create an appropriate number of tasks to execute a loop, dividing the loop's iterations between them.

What is the appropriate number of tasks?

  • on a single core: single task
  • on multiple cores on the same nodes: all cores, up to the number of elements or iterations
  • on multiple cores on multiple nodes: all cores, up to the problem size, given the data distribution

Consider a simple code array.chpl:

const n = 1e6: int;
var A: [1..n] real;
forall a in A do
  a += 1;

We will not run it, as it does not print anything, but let's study what it does. We update all elements of the array A. The code will run on a single node, lauching as many tasks as the number of available cores.

  • if we replace forall with for, we'll get a serial loop on a sigle core
  • if we replace forall with coforall, we'll create 1e6 tasks (definitely an overkill!)

Consider a simple code forall.chpl that we'll run inside our 2-core job. We have a range of indices 1..1000, and they get broken into groups that are processed by different tasks:

var count = 0;
forall i in 1..1000 with (+ reduce count) {   // parallel loop
  count += i;
}
writeln('count = ', count);

$ chpl forall.chpl -o forall
$ sed -i -e 's|./coforall --numthreads=5|./forall|' job.sh
$ sbatch job.sh
$ squeue -u user01

We get a sum 1..1000 which is 500500.

Exercise

We computed the sum of integers from 1 to 1000 in parallel. How many cores did the code run on? Looking at the code or its output, we don't know. Most likely, on two cores available to us inside the job. But we can actually check that!

Solution

(1) replace count += i; with count = 1;
(2) change the last line to writeln('actual number of threads = ', count);

$ chpl forall.chpl -o forall
$ sbatch job.sh
$ squeue -u user01

We should get the actual CPU count!

Running Chapel code on multiple locales

$ . /home/centos/startMultiLocale.sh
$ which chpl

Let's start by writing the following code locales.chpl:

writeln(Locales);

$ chpl locales.chpl -o locales   # notice now we have two executables!
$ nano job.sh
  add line `#SBATCH --nodes=2` to
  replace `./forall` with `./locales`
$ sbatch job.sh
$ squeue -u user01

We get an error message! Need to specify the number of locales!

$ sed -i -e 's|./locales|./locales -nl 2|' job.sh
$ sbatch job.sh
$ squeue -u user01

Ignore the warnings and study the output:

LOCALE0 LOCALE1

Now let's add this to our code 'locales.chpl`:

for loc in Locales do   // this is still a serial program
  on loc do             // run the next line on locale `loc`
    writeln("this locale is named ", here.name[1..6]);   // `here` is the locale on which the node is running

$ chpl locales.chpl -o locales
$ sbatch job.sh
$ squeue -u user01

Now let's replace the last for loop with the following:

use Memory;
for loc in Locales do
  on loc {
    writeln("locale #", here.id, "...");
    writeln("  ...is named: ", here.name);
    writeln("  ...has ", here.numPUs(), " processor cores");
    writeln("  ...has ", here.physicalMemory(unit=MemUnits.GB, retType=real), " GB of memory");
    writeln("  ...has ", here.maxTaskPar, " maximum parallelism");
  }
~~~ {.bash}
$ chpl locales.chpl -o locales
$ sbatch job.sh
$ squeue -u user01

Data parallelism on multiple locales

In a new code domains.chpl, let us define an n^2 domain called mesh. It is defined by the single task in our code and is therefore defined in memory on the same node (locale 0) where this task is running. For each of n^2 mesh points, let us print out

  1. m.locale.id = the ID of the locale holding that mesh point (should be 0)
  2. here.id = the ID of the locale on which the code is running (should be 0)
  3. here.maxTaskPar = the number of available cores (max parallelism with 1 task/core) (should be 2)
config const n = 8;
const mesh: domain(2) = {1..n, 1..n};  // a 2D domain defined in shared memory on a single locale
forall m in mesh do   // go in parallel through all n^2 mesh points
  writeln(m, ' ', m.locale.id, ' ', here.id, ' ', here.maxTaskPar);

$ chpl domains.chpl -o domains
$ sed -i -e 's|./locales -nl 2|./domains -nl 2|' job.sh
$ sbatch job.sh
$ squeue -u user01

(1, 1) 0 0 2
(5, 1) 0 0 2
(1, 2) 0 0 2
(5, 2) 0 0 2
(1, 3) 0 0 2
...

Now, let's introduce two important properties of Chapel domains:

  1. Domains can be used to define arrays of variables of any type on top of them.
  2. Domains can span multiple locales.

Let us now define an n^2 distributed (over several locales) domain distributedMesh mapped to locales in blocks. On top of this domain we define a 2D block-distributed array A of strings mapped to locales in exactly the same pattern as the underlying domain. Let us print out

(1) a.locale.id = the ID of the locale holding the element a of A
(2) here.name = the name of the locale on which the code is running
(3) here.maxTaskPar = the number of cores on the locale on which the code is running

Instead of printing these values to the screen, we will store this output inside each element of A as a string:
a = int + string + int
is a shortcut for
a = "%i".format(int) + string + "%i".format(int)

use BlockDist; // use standard block distribution module to partition the domain into blocks
config const n = 8;
const mesh: domain(2) = {1..n, 1..n};
const distributedMesh: domain(2) dmapped Block(boundingBox=mesh) = mesh;
var A: [distributedMesh] string; // block-distributed array mapped to locales
forall a in A { // go in parallel through all n^2 elements in A
  // assign each array element on the locale that stores that index/element
  a = a.locale.id + '-' + here.name[1..5] + '-' + here.maxTaskPar + '  ';
}
writeln(A);

$ chpl domains.chpl -o domains
$ sbatch job.sh
$ squeue -u user01

0-node1-2   0-node1-2   0-node1-2   0-node1-2   0-node1-2   0-node1-2   0-node1-2   0-node1-2
0-node1-2   0-node1-2   0-node1-2   0-node1-2   0-node1-2   0-node1-2   0-node1-2   0-node1-2
0-node1-2   0-node1-2   0-node1-2   0-node1-2   0-node1-2   0-node1-2   0-node1-2   0-node1-2
0-node1-2   0-node1-2   0-node1-2   0-node1-2   0-node1-2   0-node1-2   0-node1-2   0-node1-2
1-node2-2   1-node2-2   1-node2-2   1-node2-2   1-node2-2   1-node2-2   1-node2-2   1-node2-2
1-node2-2   1-node2-2   1-node2-2   1-node2-2   1-node2-2   1-node2-2   1-node2-2   1-node2-2
1-node2-2   1-node2-2   1-node2-2   1-node2-2   1-node2-2   1-node2-2   1-node2-2   1-node2-2
1-node2-2   1-node2-2   1-node2-2   1-node2-2   1-node2-2   1-node2-2   1-node2-2   1-node2-2

Finally, let us count the number of threads by adding the following to our code:

var count = 0;
forall a in A with (+ reduce count) { // go in parallel through all n^2 elements
  count = 1;
}
writeln("actual number of threads = ", count);

$ chpl domains.chpl -o domains
$ sbatch job.sh
$ squeue -u user01

...
actual number of threads = 4