In computer science, run-time algorithm specialization is a methodology for creating efficient algorithms for costly computation tasks of certain kinds.
The methodology originates in the field of automated theorem proving and, more specifically, in the Vampire theorem prover project.
The idea is inspired by the use of partial evaluation in optimising program translation.
Many core operations in theorem provers exhibit the following pattern.
Suppose that we need to execute some algorithm
is fixed for potentially many different values of
In order to do this efficiently, we can try to find a specialization of
The specialized algorithm may be more efficient than the generic one, since it can exploit some particular properties of the fixed value
In particular, we can often identify some tests that are true or false for
The key difference between run-time specialization and partial evaluation is that the values of
is specialised are not known statically, so the specialization takes place at run-time.
Partial evaluation is applied to algorithms explicitly represented as codes in some programming language.
All we need is a concrete representation of the specialized version
This also means that we cannot use any universal methods for specializing algorithms, which is usually the case with partial evaluation.
Instead, we have to program a specialization procedure for every particular algorithm
An important advantage of doing so is that we can use some powerful ad hoc tricks exploiting peculiarities of
, which are beyond the reach of any universal specialization methods.
The specialized algorithm has to be represented in a form that can be interpreted.
as a code of a special abstract machine, and we often say that
Then the code itself can be additionally optimized by answer-preserving transformations that rely only on the semantics of instructions of the abstract machine.
Instructions of the abstract machine can usually be represented as records.
One field of such a record stores an integer tag that identifies the instruction type, other fields may be used for storing additional parameters of the instruction, for example a pointer to another instruction representing a label, if the semantics of the instruction requires a jump.
All instructions of a code can be stored in an array, or list, or tree.
Interpretation is done by fetching instructions in some order, identifying their type and executing the actions associated with this type.
In C or C++ we can use a switch statement to associate some actions with different instruction tags.
Modern compilers usually compile a switch statement with integer labels from a narrow range rather efficiently by storing the address of the statement corresponding to a value
One can exploit this by taking values for instruction tags from a small interval of integers.
are intended for long-term storage and the calls of
In such circumstances, full-scale specialization with compilation may not be suitable due to excessive memory usage.
However, we can sometimes find a compact specialized representation