Efficiency of Prolog

The key point about Prolog is that the PURE things are fast and the
imperative things are slow, which is the direct opposite of languages
like Pascal. (Richard O'Keefe, Prolog Digest, 1987)


Is Prolog fast? Is it slow? Will it be fast enough for your task? (Most likely: yes.)

Prolog is a programming language, and like for any programming language, there are many different ways to implement it. For example, we can compile a Prolog program to abstract or concrete machine code, or we can interpret a Prolog program with various techniques. The most popular implementation technique for Prolog is compiling it to abstract machine code. This is similar to the most common implementation methods of other languages like Pascal, Lisp and Java. The Warren Abstract Machine (WAM) is among the most well-known target machines for Prolog. Other popular choices are the ZIP and TOAM. Some implementations also compile to native code.

For example, in GNU Prolog, the predicate list_length/2 is translated to the following abstract machine instructions, represented using a Prolog term:




It is such instructions that are actually interpreted when you invoke this predicate in any way. In fact, GNU Prolog even goes beyond this, and further translates such instructions to native code.

Throughout the following, keep in mind that efficiency is a property of a Prolog implementation, not of the language itself!

We distinguish at least two kinds of efficiency: Both kinds of efficiency are of considerable importance in practice, also when programming in Prolog.

Space efficiency

To understand memory usage of Prolog programs, you need to have a basic understanding of how Prolog programs are executed. See Richard O'Keefe's book The Craft of Prolog for a very nice introduction.

Video: Memory usage of Prolog programs

The concrete memory usage depends on the Prolog compiler or interpreter you are using, the optimizations it applies, and which virtual machine architecture is used.

However, almost all Prolog compilers and interpreters distinguish at least the following memory areas: You can test how your Prolog system behaves when you—for example—use more global stack than is available:
?- length(_, E), L #= 2^E, portray_clause(L), length(Ls, L), false.
ERROR: Out of global stack
Prolog does not provide any means to manipulate system memory directly. For this reason, large classes of mistakes that are typical for lower-level languages cannot arise at all when programming in Prolog. For example, with Prolog, you cannot double-free a pointer, you cannot access an invalid reference, and you cannot accidentally write into unforeseen regions of the system memory. Since you cannot control any low-level aspects of program execution, the Prolog implementation must automatically perform some tasks for you.

Prolog implementations automatically apply various optimizations to reduce their memory consumption. The most important optimizations are: Tail recursion optimization is a special case of tail call optimization.

Prolog implementations typically apply garbage collection to automatically reclaim areas of the stack that can be safely used again.

Time efficiency

The time efficiency of any Prolog program depends a lot on the compiler or interpreter you are using, and available Prolog implementations differ significantly regarding performance. Some Prolog implementors have made heroic efforts to achieve peak performance in a large number of benchmarks. See for example Aquarius Prolog by Peter Van Roy, and his accompanying PhD thesis Can Logic Programming Execute as Fast as Imperative Programming?. Even today, A Prolog Benchmark Suite for Aquarius by Ralph Haygood is frequently used by Prolog programmers to assess their systems' performance. Some Prolog vendors are prioritizing other features over performance.

However, a few general points hold for all Prolog implementations: First, Prolog programs are typically much more general than comparable programs in other languages. For example, Prolog implementations support logic variables, which are much more general than variables in most other programming languages. In addition, Prolog supports built-in backtracking and nondeterminism, which also need additional mechanisms. The overhead of this generality is, in almost all cases, acceptably small. Look at it this way: If you need these features, then a roughly comparable overhead would have been necessary to implement them in other languages that do not ship with these features.

Second, pure Prolog lacks destructive updates to terms. For this reason, implementing (for example) efficient matrix operations requires special support from the Prolog compiler. Alternatively, at least logarithmic overhead is necessary to express updates in a pure way. Prolog programs are efficient under the so-called tree model of computation. Whether Prolog programs can be as efficient as possible under the so-called pointer model is an open research problem. It is known that Constraint Handling Rules (CHR) satisfy this property, so each imperative algorithm can be implemented with asymptotically optimal efficiency with CHR.

Third, the choice of algorithm typically has far larger impact on running time than any particular implementation. You may make a Prolog program run 10 times faster by rewriting it in C++, but you typically will not make it 100 or 1000 times faster, for example.

Nevertheless, Prolog is sometimes associated with being slow. In our experience, the main reasons for this are:
  1. Exponential-time algorithms can be conveniently encoded in Prolog.
  2. Beginners sometimes accidentally write Prolog programs that do not even terminate, then call them "slow".
In response to point (1), we can only say that exponential-time algorithms will eventually turn unacceptably slow in any programming language, using any implementation method. Also, many Prolog systems provide built-in methods and libraries that let you solve large classes of combinatorial optimization tasks very efficiently. In practice, the performance of these specialised mechanisms is often far more important than the overall speed of the entire system.

In response to point (2), we point to declarative ways of reasoning about nontermination of Prolog programs via failure slicing. When you run a program that is "too slow", first apply this technique to ensure that the program even terminates, or at least may terminate.

In general, regarding efficiency, Prolog is at least as acceptable a choice as for example Python or Java for most projects, and will typically perform in roughly the same way.

Writing efficient Prolog code

Here are a few tips for obtaining efficient Prolog code:
  1. Before even thinking about performance, make sure that your predicates describe only the intended objects and terminate for those cases where you expect them to terminate. When beginners complain about performance of Prolog, they are typically writing predicates that don't terminate. Use ?- Goal, false. to test universal termination of Goal.
  2. Delegate as much of the work as possible to the underlying Prolog engine. For example, if you need efficient look-up in a database, represent your data in such a way that the Prolog system's indexing mechanism solves the task for you. As another example, if you need efficient search and backtracking, using the built-in mechanisms will likely be more efficient than formulating the search within Prolog.
  3. Always aim for clean data structures. These let you symbolically distinguish different cases and make automatic argument indexing applicable.
  4. If you cannot use argument indexing, use if_/3 to distinguish different cases, preserving generality and determinism.
  5. If your predicate depends on the outcome of an arithmetic comparison between integers, consider using zcompare/3 to reify the comparison. This makes its result available as an atom (<, = or >) which is again amenable to argument indexing.
  6. Perform pruning early to reduce the size of the search space. A simple heuristic is to place those goals first that always terminate, such as CLP(FD) constraints and dif/2.
  7. Learn more about memoization.
  8. Consider using macros and partial evaluation to produce more efficient code at compilation time.
Good performance is often important. However, do not get carried away with micro-optimizations. For example, leaving an occasional choice point is often completely acceptable in user programs. Likewise, reporting redundant solutions is not inherently bad. For beginners, it is easy to get dragged into procedural aspects too early. Do not fall into this trap! Instead, focus on those aspects of your programs where your attention is most warranted. Let the machine and Prolog system take care of the more trivial aspects. Not everything needs to be implemented with utmost efficiency.

Most importantly: Never sacrifice correctness for performance! When writing Prolog programs, the most elegant solutions are often also among the most efficient.

Therefore, when writing Prolog code, always aim for elegant and general solutions. If they are not efficient enough even though you have followed the advice on this page, it may be a sign that you need a new language construct from which many other Prolog programmers could potentially benefit too. In such cases, ask the Prolog community for more information! For example, the newsgroup comp.lang.prolog is a good place for such discussions.

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