2.2: FastArrayStack - An Optimized ArrayStack
- Page ID
- 8438
Much of the work done by an ArrayStack involves shifting (by \(\mathtt{add(i,x)}\) and \(\mathtt{remove(i)}\)) and copying (by \(\mathtt{resize()}\)) of data. In the implementations shown above, this was done using \(\mathtt{for}\) loops. It turns out that many programming environments have specific functions that are very efficient at copying and moving blocks of data. In the C programming language, there are the \(\mathtt{memcpy(d,s,n)}\) and \(\mathtt{memmove(d,s,n)}\) functions. In the C++ language there is the \(\texttt{std::copy(a0,a1,b)}\) algorithm. In Java there is the \(\texttt{System.arraycopy}\mathtt{(s,i,d,j,n)}\) method.
void resize() { T[] b = newArray(Math.max(2*n,1)); System.arraycopy(a, 0, b, 0, n); a = b; } void add(int i, T x) { if (i < 0 || i > n) throw new IndexOutOfBoundsException(); if (n + 1 > a.length) resize(); System.arraycopy(a, i, a, i+1, n-i); a[i] = x; n++; } T remove(int i) { if (i < 0 || i > n - 1) throw new IndexOutOfBoundsException(); T x = a[i]; System.arraycopy(a, i+1, a, i, n-i-1); n--; if (a.length >= 3*n) resize(); return x; }
These functions are usually highly optimized and may even use special machine instructions that can do this copying much faster than we could by using a \(\mathtt{for}\) loop. Although using these functions does not asymptotically decrease the running times, it can still be a worthwhile optimization.
In the Java implementations here, the use of the native \(\texttt{System.arraycopy}\mathtt{(s,i,d,j,n)}\) resulted in speedups of a factor between 2 and 3, depending on the types of operations performed. Your mileage may vary.