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An area of memory used for dynamic memory allocation where blocks of memory are allocated and freed in an arbitrary order and the pattern of allocation and size of blocks is not known until run time. Typically, a program has one heap which it may use for several different purposes.

Heap is required by languages in which functions can return arbitrary data structures or functions with free variables (see closure). In C functions malloc and free provide access to the heap.

Contrast stack. See also dangling pointer.


A data structure with its elements partially ordered (sorted) such that finding either the minimum or the maximum (but not both) of the elements is computationally inexpensive (independent of the number of elements), while both adding a new item and finding each subsequent smallest/largest element can be done in O(log n) time, where n is the number of elements.

Formally, a heap is a binary tree with a key in each node, such that all the leaves of the tree are on two adjacent levels; all leaves on the lowest level occur to the left and all levels, except possibly the lowest, are filled; and the key in the root is at least as large as the keys in its children (if any), and the left and right subtrees (if they exist) are again heaps.

Note that the last condition assumes that the goal is finding the minimum quickly.

Heaps are often implemented as one-dimensional arrays. Still assuming that the goal is finding the minimum quickly the invariant is

heap[i] <= heap[2*i] and heap[i] <= heap[2*i+1] for all i,

where heap[i] denotes the i-th element, heap[1] being the first. Heaps can be used to implement priority queues or in sort algorithms.


In programming, it refers to a common pool of memory that is available to the program. The management of the heap is either done by the applications themselves, allocating and deallocating memory as required, or by the operating system or other system program.
References in periodicals archive ?
Although this approach is useful for visual inspection of the data, a second, more-rigorous approach is warranted when systematic differences between heaped data and non-heaped data are less obvious.
This results from the influence of the heaped types on the slope terms.
DGP-2 is the same as the baseline DGP-1 except that the non-heaped types and heaped types have different slopes instead of different means.
While the results are not shown in Figure 4, we have also considered a DGP that combines the salient elements of DGP-1 and DGP-2 by having the parameters for the non-heaped types remain the same (set at zero), while having the heaped types have a higher mean (0.5) and a higher slope (0.01).
We now turn to two DGPs in which there is a treatment effect for heaped types while maintaining the same parameters for the non-heaped types.