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Programmers can use trampolined functions to implement tail-recursive function calls in stack-oriented programming languages. [1] In Java, trampoline refers to using reflection to avoid using inner classes, for example in event listeners. The time overhead of a reflection call is traded for the space overhead of an inner class.
caller:; make new call frame; (some compilers may produce an 'enter' instruction instead) push ebp; save old call frame mov ebp, esp; initialize new call frame; push call arguments, in reverse; (some compilers may subtract the required space from the stack pointer,; then write each argument directly, see below.
This type of stack is also known as an execution stack, program stack, control stack, run-time stack, or machine stack, and is often shortened to simply the "stack". Although maintenance of the call stack is important for the proper functioning of most software , the details are normally hidden and automatic in high-level programming languages .
*/ /* This implementation does not implement composite functions, functions with a variable number of arguments, or unary operators. */ while there are tokens to be read: read a token if the token is: - a number: put it into the output queue - a function: push it onto the operator stack - an operator o 1: while ( there is an operator o 2 at the ...
The algorithm that is presented here does not need an explicit stack; instead, it uses recursive calls to implement the stack. The algorithm is not a pure operator-precedence parser like the Dijkstra shunting yard algorithm. It assumes that the primary nonterminal is parsed in a separate subroutine, like in a recursive descent parser.
Ghidra (pronounced GEE-druh; [3] / ˈ ɡ iː d r ə / [4]) is a free and open source reverse engineering tool developed by the National Security Agency (NSA) of the United States. The binaries were released at RSA Conference in March 2019; the sources were published one month later on GitHub. [5]
Loop unrolling, also known as loop unwinding, is a loop transformation technique that attempts to optimize a program's execution speed at the expense of its binary size, which is an approach known as space–time tradeoff.
This parse stack grows rightwards. The base or bottom of the stack is on the left and holds the leftmost, oldest parse fragment. Every reduction step acts only on the rightmost, newest parse fragments. (This accumulative parse stack is very unlike the predictive, leftward-growing parse stack used by top-down parsers.)