Evaluation methods

In the basics section it has been noted that the order of the statements in the program provides no information for the interpreter and the output will not depend on this order. Rather the dependencies between variables and their parameters determine the execution order.

Language interpreters and evaluation modes

Currently, three different interpreters can be used to execute programs. These interpreters do not perform eager evaluation, i.e. do not evaluate parameters (such as function calls, expressions etc.) as soon as they are interpreted. Rather the evaluation is triggered only when a certain value is actually needed. Thus, the order of evaluation is neither the order of statements in the source code nor the order of interpretation.

Another aspect of evaluation is the time of evaluation - during interpreter runs (let us call it immediate) and after the interpreter has finished (deferred evaluation).

A third aspect is the location of evaluation: we make difference between local and remote evaluation. This is when we need additional computing resources that are not available locally (i.e. local HPC cluster with a batch system and a JupyterHub instance connected to it).

The choice of interpreter determines the evaluation mode that is selected with --mode | -m command-line flags.

The evaluation of parameters is essentially governed by the evaluation policies of the evaluation modes and by the ocurrence of print and view statements in the code. See next section for more details.

Order of evaluation and short-circuiting

Some built-in function calls and some expression types have the so-called normal order evaluation, i.e. they return values after only those input parameters are evaluated that are actually needed. This is sometimes referred to as lazy evaluation. Expressions including if, or, and evaluated in this way are known as short-circuiting expressions. In contrast, other parameters may be evaluated in applicative order, i.e. their evaluation begins only after all input parameters are evaluated.

In instant (default) and deferred (--mode deferred) evaluation modes, all parameters are evaluated in normal order and the or, and and if expressions are short-circuiting. In workflow mode (--mode workflow) with no evaluation (default) or on-demand evaluation (--on-demand --autorun), the or, and and if expressions are short-circuiting when used within print statements. In all other workflow evaluation cases, i.e. on-demand evaluation of variables or with evaluate-all policy, the evaluation order is applicative and there is no short-cicuiting.

Obviously, the mode and order of evaluation do not affect the outputs of the model but rather the behavior, i.e. the performance, location, resources used, time and costs of evaluation.

Example

In the following example, the input parameter a of the if function is not evaluated because only the second input parameter, that is a string literal, is only needed and evaluated.

a = 'abc'
expr = true
b = if(expr, 'xyz', a)
print(b)

Swapping 'xyz' and a (or changing the first input to false) will cause a to be evaluated but not the string literal xyz. Only evaluation in instant or deferred modes will have this behavior. In workflow mode, because the if function is in a variable statement, both xyz and a will be evaluated before the evaluation of the if function is started, no matter of the chosen evaluation policy. However, in workflow evaluation mode with on-demand (--on-demand --autorun flags) or no-evaluation (no flags) policy, the if function is short-circuiting:

a = 'abc'
expr = true
print(if(expr, 'xyz', a))

In this mode it is also easy to check, that a is in fact not computed, with no-evaluation policy (omit --autorun flag), the statement print(a) will print n.c..