In addition to its powerful tools for parallel and distributed computing, Chapel has a very pleasant and unique mix of features for general-purpose programming. Among those features is Chapel’s support for generics and type-level computation. In particular, Chapel lets you define functions that perform computations on types at compile-time. This allows one to specialize the behavior of a particular generic function depending on its type arguments. Since types are [note: Certain types in Chapel actually have a runtime component. This is specifically true for arrays, where the array's domain — the indices it allows, and their distribution across various nodes — is a runtime value. This supports resizing and dynamic allocation of arrays, but it does muddy the waters a bit in terms of what "compile-time type computation" means. ] there’s generally no runtime cost to operating on them. In other words: we can write functions that accept a type argument, which has no runtime costs and allows us to customize the behavior of the function.

There are infinitely many ways to make use of this feature, but here I will present a concrete example. We’ll look at a certain subset of linear multistep methods, which are a family of related numerical algorithms. The algorithms from this subset have different numbers of initial parameters and have different behavior, but the behavior and initial parameters are all derived from each method’s list of coefficients. Thus, by passing a type-level list of coefficients to a generic linear multistep method evaluator, we’ll be able to select which algorithm we want to use — without writing the code for each particular method explicitly. Moreover, since the list of coefficients is a type-level construct, each resulting function will perform as well as an explicitly-written implementation would.

### Euler’s Method

Suppose we have a (first-order) differential equation. Such a differential equation has the form: $$y' = f(t, y)$$ That is, the value of the derivative of $$y$$ (aka $$y’$$) at any point $$t$$ depends also on the value of $$y$$ itself. You want to figure out what the value of $$y$$ is at any point $$t$$. However, there is no general solution to such an equation. In the general case, we’ll have to settle for approximations.

Here is a simple approximation. Suppose that we knew a starting point for our $$y$$. For instance, we could know that at $$t=0$$, the value of $$y$$ is $$y(t) = y(0) = 1$$. With this, we have both $$t$$ and $$y$$, just what we need to figure out the value of $$y’(0)$$: $$y'(0) = f(0, y(0)) = f(0,1)$$

Okay, but how do we get $$y(t)$$ for any $$t$$? All we have is a point and a derivative at that point. To get any further, we have to extrapolate. At our point, the value of $$y$$ is changing at a rate of $$y’(0)$$. It’s not unreasonable to guess, then, that after some distance $$h$$, the value of $$y$$ would be: $$y(0+h) \approx y(0) + hy'(0) = y(0) + hf(0,y(0))$$ Importantly, $$h$$ shouldn’t be large, since the further away you extrapolate, the less accurate your approximation becomes. If we need to get further than $$0+h$$, we can use the new point we just computed, and extrapolate again: $$y(0+2h) \approx y(0+h) + hf(0+h,y(0+h))$$ And so on and so forth. The last thing I’m going to do is clean up the above equations. First, let’s stop assuming that our initial $$t=0$$; that doesn’t work in the general case. Instead, we’ll use $$t_0$$ to denote the initial value of $$t$$. Similarly, we can use $$y_0$$ to denote our (known a priori) value of $$y(t_0)$$. From then, observe that we are moving in discrete steps, estimating one point after another. We can denote the first point we estimate as $$(t_1, y_1)$$, the second as $$(t_2, y_2)$$, and so on. Since we’re always moving our $$t$$ by adding $$h$$ to it, the formula for a general $$t_n$$ is easy enough: $$t_n = t_0 + nh$$ We can get the formula for $$y_n$$ by looking at our approximation steps above. $$\begin{gather*} y_1 = y_0 + hf(t_0,y_0) \\ y_2 = y_1 + hf(t_1,y_1) \\ ... \\ y_{n+1} = y_n + hf(t_n,y_n) \end{gather*}$$

What we have arrived at is Euler’s method. It works pretty well, especially considering how easy it is to implement. We could write it in Chapel like so:

 1 2 3 4 5 6 7 8 9 proc runEulerMethod(step: real, count: int, t0: real, y0: real) { var y = y0; var t = t0; for i in 1..count { y += step*f(t,y); t += step; } return y; }

Just nine lines of code!

### Linear Multistep Methods

In Euler’s method, we use only the derivative at the current point to drive our subsequent approximations. Why does that have to be the case, though? After a few steps, we’ve computed a whole history of points, each of which gives us information about $$y$$’s derivatives. Of course, the more recent derivatives are more useful to us (they’re closer, so using them would reduce the error we get from extrapolating). Nevertheless, we can still try to mix in a little bit of “older” information into our estimate of the derivative, and try to get a better result that way. Take, for example, the two-step Adams-Bashforth method:

$$y_{n+2} = y_{n+1} + h\left[\frac{3}{2}f(t_{n+1}, y_{n+1})-\frac{1}{2}f(t_n,y_n)\right]$$

In this method, the two last known derivatives are used for computing the next point. More generally, an $$s$$-step linear multistep method uses the last $$s$$ points to compute the next, and always takes a linear combination of the derivatives and the $y_n$ values themselves. The general form is:

$$\sum_{j=0}^s a_jy_{n+j} = h\sum_{j=0}^s b_jf(t_{n+j}, y_{n+j})$$

Let’s focus for a second only on methods where the very last $$y$$ value is used to compute the next. Furthermore, we won’t allow our specifications to depend on the slope at the point we’re trying to compute (i.e., when computing $$y_{n+s}$$, we do not look at $$f(t_{n+s}, y_{n+s})$$. The methods we will consider will consequently have the following form:

$$y_{n+s} = y_{n+s-1} + h\sum_{j=0}^{s-1} b_jf(t_{n+j}, y_{n+j})$$

Importantly, notice that a method of the above form is pretty much uniquely determined by its list of coefficients. The number of steps $$s$$ is equal to the number of coefficients $$b_j$$, and the rest of the equation simply “zips together” a coefficient with a fixed expression representing the derivative at a particular point in the past.

### Type-Level Coefficient Lists

What we would like to do now is to be able to represent different methods in the above family at compile-time. That is, we would like to be able to fully describe a method using a Chapel type, feed it to a function that implements the method, and pay nothing in terms of runtime overhead. By encoding information about a numerical method at compile-time, we can also ensure that the function is correctly invoked. For instance, Euler’s method needs a single initial coordinate to get started, $$y_{0}$$, but the two-step Adams-Bashforth method above requires both $$y_{0}$$ and another coordinate $$y_{1}$$. If we were to describe methods using a runtime construct, such as perhaps a list, it would be entirely possible for a user to feed in a list with not enough points, or too many. We can be smarter than that.

Okay, but how do we represent a list at compile-time? Chapel does have params, which are integer, real, boolean, or string values that can occur at the type-level and are known when a program is being compiled. However, params currently only support primitive values; a list is far from a primitive value!

What is necessary is a trick used quite frequently in Haskell land. We can create two completely unrelated records, just to tell the compiler about two new types:

 11 12 13 14 15 record empty {} record cons { param head: real; type tail; }

The new cons type is generic. It needs two things: a real for the head field, and a type for the tail field. Coming up with a real is not that hard; we can, for instance, write:

type t1 = cons(1.5, ?);

Alright, but what goes in the place of the question mark? The second argument of cons is a type, and a fully-instantiated cons is itself a type. Thus, perhaps we could try nesting two conss:

type t2 = cons(1.5, cons(-0.5, ?));

Once again, the first argument to cons is a real number, and coming up with one of those is easy.

type t3 = cons(1.5, cons(-0.5, ?));

I hope it’s obvious enough that we can keep repeating this process to build up a longer and longer chain of numbers. However, we need to do something different if we are to get out of this cons-loop. That’s where empty comes in.

type t4 = cons(1.5, cons(-0.5, empty));

Now, t4 is a fully-concrete type. Note that we haven’t yet — nor will we — actually allocate any records of type cons or empty; the purpose of these two is solely to live at the type level. Specifically, if we encode our special types of linear multistep methods as lists of coefficients, we can embed these lists of coefficients into Chapel types by using cons and empty. Check out the table below.

Method List Chapel Type Expression
Degenerate case $$\varnothing$$ empty
Euler’s method $$1$$ cons(1.0, empty)
Two-step Adams-Bashforth $$\frac{3}{2}, -\frac{1}{2}$$ cons(3.0/2.0, cons(-0.5, empty))

As I mentioned earlier, the number of steps $$s$$ a method takes (which is also the number of initial data points required to get the method started) is the number of coefficients. Thus, we need a way to take a type like t4 and count how many coefficients it contains. Fortunately, Chapel allows us to write functions on types, as well as to pattern match on types’ arguments. An important caveat is that matching occurs when a function is called, so we can’t somehow write myType == cons(?h, ?t). Instead, we need to write a function that definitely accepts a type cons with some arguments:

 17 18 proc length(type x: cons(?w, ?t)) param do return 1 + length(t);

The above function works roughly as follows:

length(cons(1.5, cons(-0.5, empty)))

• When resolving the call to length, ?w gets matched with 1.5 and ?t gets matched with cons(-0.5, empty). When the return expression is evaluated, we end up with the following:
1 + length(t)

• Since t was matched with cons(-0.5, empty), the previous code snippet is the same as:
1 + length(cons(-0.5, empty))

• We now resolve the new call to length: ?w gets matched with -0.5, ?t gets matched with empty. Evaluation proceeds like last time.
1 + 1 + length(empty)

Here, we could we get stuck; what’s length(empty)? We need another overload for length that handles this case.

 20 21 proc length(type x: empty) param do return 0;

With this, the final expression will effectively become 1 + 1 + 0 = 2, which is indeed the length of the given list.

Another thing we’re going to need is the ability to get a coefficient at a particular location in the list. We’ll use 1 to get the first element, 2 to get the second, and so on. Such a function is pretty simple, though it does also use pattern-matching via ?w and ?t:

 23 24 25 26 27 28 29 proc coeff(param x: int, type ct: cons(?w, ?t)) param { if x == 1 { return w; } else { return coeff(x-1, t); } }

### A Generic Evaluator

Alright, now’s the time to get working on the actual implementation of linear multistep methods. A function implementing them will need a few arguments:

• A type argument representing the linear multi-step method being implemented. This method will be encoded using our cons lists.
• A real representing the step size $$h$$.
• An int representing how many iterations we should run.
• A real representing the starting x-coordinate of the method $$t_0$$. We don’t need to feed in a whole list $$t_0, \ldots, t_{s-1}$$ since each $$t_{i}$$ is within a fixed distance $$h$$ of its neighbors.
• As many real coordinates as required by the method, $$y_0, \ldots, y_{s-1}$$.

Here’s the corresponding Chapel code:

 31 32 proc runMethod(type method, step: real, count: int, start: real, n: real ... length(method)): real {

That last little bit, n: real ... length(method), is an instance of Chapel’s variable arguments functionality. Specifically, this says that runMethod takes multiple real arguments, the exact number of which is given by the expression length(method). We’ve just seen that length computes the number of coefficients in a list, so the function will expect exactly as many $$y_i$$ y-coordinates as there are coefficients.

The actual code is straightforward.

 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 param coeffCount = length(method); // Repeat the methods as many times as requested for i in 1..count { // We're computing by adding h*b_j*f(...) to y_n. // Set total to y_n. var total = n(coeffCount - 1); // 'for param' loops are unrolled at compile-time -- this is just // like writing out each iteration by hand. for param j in 1..coeffCount do // For each coefficient b_j given by coeff(j, method), // increment the total by h*bj*f(...) total += step * coeff(j, method) * f(start + step*(i-1+coeffCount-j), n(coeffCount-j)); // Shift each y_i over by one, and set y_{n+s} to the // newly computed total. for param j in 0..< coeffCount - 1 do n(j) = n(j+1); n(coeffCount - 1) = total; } // return final y_{n+s} return n(coeffCount - 1); }

That completes our implementation. All that’s left is to actually use our API, by applying it to some function! I’ll use Wikipedia’s page on linear multi-step methods to find some examples to compare against. That page runs all the methods with the differential equation $$y’ = y$$, for which the corresponding function f is:

 57 58 proc f(t: real, y: real) do return y;

Next, we need to encode the various methods:

 60 61 62 type euler = cons(1.0, empty); type adamsBashforth = cons(3.0/2.0, cons(-0.5, empty)); type someThirdMethod = cons(23.0/12.0, cons(-16.0/12.0, cons(5.0/12.0, empty)));

Then, we can run the methods as follows (with initial coordinate $$(0, 1)$$, as Wikipedia does).

 64 writeln(runMethod(euler, step=0.5, count=4, start=0, 1));
Console output
5.0625

Wikipedia confirms that 5.0625 is the correct answer! To test Adams-Bashforth, pick a second initial point from Euler’s method.

 66 67 writeln(runMethod(adamsBashforth, step=0.5, count=3, start=0, 1, runMethod(euler, step=0.5, count=1, start=0, 1)));
Console output
6.02344

Once again, 6.0234 is the correct answer!

Of course, this is just a toy example, and finding an approximate solution to $$y’ = y$$ is neither novel nor particularly difficult. However, the goal was not to approximate the differential equation, but to demonstrate some of the advanced general-purpose features that Chapel provides. Using these features, we were able to implement an evaluator for a whole family of numerical methods, allowing us to describe a method in only a line of code, and execute it as fast as a hand-written implementation.