Welcome to day 4 of Chapel’s Advent of Code 2022 series! For more context, check out our introductory Advent of Code 2022: Twelve Days of Chapel blog article for context or instructions on compiling this code.

### The Task at Hand and My Approach

In brief, the challenge for today is to read in a series of range pairs representing work assignments and determine how many of those assignments are subsets of one another for part 1 and how many overlap at all for part 2. An example range pair is 24-42,30-42. The first elf in the pair is assigned to clean up the range of sections 24-42 in camp, and the second elf is assigned the range of sections 30-42. The second range is a subset of the first range, so would be counted for both part 1 and part 2 of this challenge.

Here is the recommended, parallelized approach that we get to at the end of this blog.

aoc2022-day04-ranges.chpl
  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25   use IO; // Chapel iterator that reads in all lines from standard input // and yields 2-tuples of ranges. // Assumes that all lines are in the format "%i-%i,%i-%i". iter readSections() { var s1, e1, s2, e2: int; while readf("%i-%i,%i-%i", s1, e1, s2, e2) { yield (s1..e1, s2..e2); } } // Creates an array with all elements yielded // by the readSections iterator. var sections = readSections(); // Parallel reduction to add up the number of subsets and the number of overlaps. var sumSubset = 0; var sumOverlap = 0; forall (r1,r2) in sections with (+ reduce sumSubset, + reduce sumOverlap) { sumSubset += r1.contains(r2) || r2.contains(r1); const intersection = r1[r2]; sumOverlap += intersection.size > 0; } writeln("sumSubset = ", sumSubset); writeln("sumOverlap = ", sumOverlap); 

Chapel’s formatted IO made the code to read in the data for this challenge very succinct. In this post, I discuss how Chapel’s formatted IO works, especially within the context of the day 4 challenge. I also talk about some general problem-solving strategies and how they can be applied to this AoC challenge, including examples that show how the Chapel range feature is an excellent conceptual fit to solve this problem. I wrap up showing how to parallelize a Chapel solution to day 4.

### First Solution: Hand-coded Interval Arithmetic

Here is a succinct solution for both parts in Chapel.

use IO;

var sumSubset = 0;
var sumOverlap = 0;
var s1, e1, s2, e2: int;

while readf("%i-%i,%i-%i", s1, e1, s2, e2) {
// Check if the second section assignment is a subset
// of the first or vice versa.
if (s1<=s2 && e2<=e1) || (s2<=s1 && e1<=e2) {
sumSubset+= 1;
}
// Partial overlap: if both starts are less than
// the other end, then we have overlap
if s1<=e2 && s2<=e1 {
sumOverlap += 1;
}
}

writeln("sumSubset = ", sumSubset);
writeln("sumOverlap = ", sumOverlap);


Formatted IO is what makes reading/parsing the input so easy. The call to

readf("%i-%i,%i-%i", s1, e1, s2, e2)


does all of the work! See the blog post for day 1 for more information about the use IO; statement that enables us to use the readf procedure. The procedure readf will try to read the given formatted string (e.g., "%i-%i,%i-%i") from standard input into the provided variables, much like scanf works in the C programming language. The %is indicate integer values of any number of digits. The - and , are the dash and comma characters respectively and will be matched directly. Whitespace is just ignored between calls to readf. Since readf returns false when it can’t match the format or when it sees an end-of-file (EOF), we can use readf in a while loop to gather all of our input.

(More examples of using Chapel’s formatted IO for AOC 2022…)

For the day 1 challenge of calorie counting, readf could have been used to read in the integers. However, this approach wouldn’t have been that helpful for the challenge, because the empty line between groups of integers would just be ignored.

use IO;
var num : int;
writeln("num = ", num);
}


Here is how readf was used to read in the “character space character” format used for the rock, paper, and scissors challenge from day 2.

use IO;
var abc, xyz : string;
while readf("%s %s", abc, xyz) {
writeln("abc = ", abc, ", xyz = ", xyz);
}


The %s format character will read characters into the given variable until a whitespace character is reached.

The readf procedure can also be used for the day 3 input, but it isn’t as exciting or necessary since iterating over the strings provided by stdin.lines() or readLine() also works.

use IO;
var line : string;
writeln("line = ", line);
}


Once we are able to read in the problem input, we can work on solving the problem. My go-to approach for solving any programming problem is to think about how the current problem is similar to problems I have seen in the past. Yesterday’s advent of code problem involved determining what item showed up in two different compartments of a rucksack. Putting the items in the first compartment into a set and then checking if any of the items in the second compartment are in that set was an approach that worked well. We could do that approach here, but it would be inefficient because today’s problem has more structure. Specifically, the set of sections each elf has been assigned to clean is being specified with a range a-b, where we know all of the integers between a and b, inclusive, are in the set. Because of that, we can avoid putting all of those integers explicitly into a set to check for subsetting and partial overlap. Instead, we can rely on the mathematical properties of the range.

Finding out how to leverage existing structure in problems is an important problem-solving technique. You can start out considering the whole space of possible inputs and solutions to a problem, and then use the structure of the problem to prune that space. In other words, some of the possibilities are going to result in the same answer, so we don’t have to code the answer for all possibilities.

For today’s problems, we are comparing two ranges/intervals for each pair of elves to determine if one range is a subet of another for part 1 and to determine if there is any overlap for part 2. The code above reads the start and end of the ranges into variables so that [s1,e1] is the first range and [s2,e2] is the second range. The [s1,e1] notation indicates a set with the numbers s1 through e1 including s1 and e1. There are 48 possibilities for the relationships between the s1, e1, s2, and e2 values, assuming that s1<=e1 and s2<=e2 (e.g., s1<e1<s2<e2, s1==e1<s2<e2, …). To solve part 1, we can check if the second range is a subset of the first with (s1<=s2 && e2<=e1), or if the first range is a subset of the second with (s2<=s1 && e1<=e2). To solve part 2, we can just check if the start of the first range is less than or equal to the end of the second range and the second range start is less than or equal to the first range end, s1<=e2 && s2<=e1. Deriving this condition takes some reasoning about all possible 48 input conditions and which groups of them end up with the same answer.

### Second Solution: Range-based Approach

This solution uses Chapel ranges to reason about whether there are subsets or overlap.

There are lots of applications that involve reasoning about overlapping ranges/intervals (i.e., interval arithmetic). In Chapel, there is a built-in abstraction called a ‘range’ that makes computing on ranges/intervals even easier. Chapel ranges were developed with High Performance Computing (HPC) applications in mind, like Adaptive Mesh Refinement (AMR), where it is important to determine the intersections/overlaps of grids that model physical phenomena.

The code below shows the creation of a range representing each elf’s cleanup assignment. Then we can use the contains() method to determine if one range is a superset of another one, and the range slicing operator to determine if there is any overlap. Determining whether one range contains a specific index—or an entire range of indices as is done here—is a common operation to want to do in interval computations. The range’s built-in contains method supports such queries out of the box. Then the expression r1[r2] slices the r1 range with the r2 range. This is equivalent to range intersection and is discussed in more depth in the Chapel range documentation.

use IO;

var sumSubset = 0;
var sumOverlap = 0;
var s1, e1, s2, e2: int;

sumSubset = 0;
sumOverlap = 0;
while readf("%i-%i,%i-%i", s1, e1, s2, e2) {
// Initialize a Chapel range for each elf
var r1 = s1..e1;
var r2 = s2..e2;

// Check if the second section assignment is a subset
// of the first or vice versa.
if r1.contains(r2) || r2.contains(r1) {
sumSubset += 1;
}
// Partial overlap occurs if the intersection of the ranges is non-empty
const intersection = r1[r2];
if intersection.size>0 {
sumOverlap += 1;
}
}

writeln("sumSubset = ", sumSubset);
writeln("sumOverlap = ", sumOverlap);


### Third Solution: Parallel Approach

Now let’s look at creating a parallel solution. This means that distinct portions of the computation will be computed simultaneously to reduce overall execution time. Parallelization is important because today’s computing processors all have multiple cores, perhaps even dozens or hundreds, so without parallel computations, a large amount of a system’s processing power may go unutilized. The Chapel programming language was designed from the ground up to express parallelism (and locality, which is critical for high performance).

The example problem has quite a bit of inherent parallelism: we could potentially read the lines of input in parallel, and determining if each pair is a subset or overlaps can both be done in parallel. In the provided solutions, reading the input file in parallel is out of scope for this blog article. With Chapel, it is easy to expose the parallelism available for determining the subsets and overlaps. To do this, we create an array using an iterator (see the day 2 blog post where it talks about iterators). This is a super-powerful way to create an array without having to compute how many entries will be in the array ahead of time.

  1 2 3 4 5 6 7 8 9 10 11 12 13   use IO; // Chapel iterator that reads in all lines from standard input // and yields 2-tuples of ranges. // Assumes that all lines are in the format "%i-%i,%i-%i". iter readSections() { var s1, e1, s2, e2: int; while readf("%i-%i,%i-%i", s1, e1, s2, e2) { yield (s1..e1, s2..e2); } } // Creates an array with all elements yielded // by the readSections iterator. var sections = readSections(); 

Once we have an array, Chapel has built in ways to do a parallel forall loop over that array.

When parallelizing computations, we do have to ask the question “Is this loop actually parallel?”. The below forall loop isn’t fully parallel. Fully parallel is when all of the iterations of the loop can be executed at the same time and you will get the same answer. But the loop does have a common pattern called a reduction. We can’t overlap (aside: parallel computing requires reasoning about intervals/ranges as well!!) the increments to the sum variables because if one iteration reads between the read and write of another then we have problems. However, addition is associative and commutative. Associative means the expressions being added up can all be evaluated in parallel, but then the summation of the results needs to happen in order. Commutative means we can do the additions in any order. Chapel can leverage reduction parallelism for associative and commutative operators such as addition. The second phrase of the forall loop

with (+ reduce sumSubset, + reduce sumOverlap)


indicates that summations are being done on the sumSubset and sumOverlap variables.

 15 16 17 18 19 20 21 22 23 24 25   // Parallel reduction to add up the number of subsets and the number of overlaps. var sumSubset = 0; var sumOverlap = 0; forall (r1,r2) in sections with (+ reduce sumSubset, + reduce sumOverlap) { sumSubset += r1.contains(r2) || r2.contains(r1); const intersection = r1[r2]; sumOverlap += intersection.size > 0; } writeln("sumSubset = ", sumSubset); writeln("sumOverlap = ", sumOverlap); 

A challenge to parallelizing AoC codes, particularly during these early days, is that the computations are simple enough that the running time tends to be dominated by the overheads of reading the input from, and writing the results to, the console. In addition, if the problem size is not big enough, the overheads of creating parallelism and computing the reduction can also weigh down a parallel execution. In contrast, in the real-world HPC problems for which Chapel was designed, the computational intensity and data set sizes tend to require parallelism to be accomplished in any reasonable time at all. All that said, with a big enough dataset, and compiling the code using the Chapel compiler’s --fast flag, today’s parallel solution can be shown to outperform the serial range-based approach on my laptop. Parallelism for the win!

### Summary

That wraps up this fourth day of introducing Chapel through AoC 2022. The full code for these solutions can be browsed and downloaded from https://github.com/mstrout/adventOfCode2022. Thank you for reading this blog post, and feel free to make comments or ask questions by creating a thread in the Chapel Blog Discourse Category.