From charlesreid1

Algorithmic analysis

While each data container is implemented differently, due to different tradeoffs between storage space and algorithmic complexity, it is useful to define a set of core tests for algorithmic analysis of data structures.

We begin with lists, which are arguably the simplest.

Big O complexity analysis

To perform a big O complexity analysis, we utilize random but seeded inputs that statistically sample the input space in a representative way. This ensures we aren't getting stuck measuring the runtime of a worst-case or best-case, and thus getting a skewed view of the algorithm.

Different kinds of timing tests, like adding and removing at a high, balanced rate and having a near-empty queue with high throughput.

Testing different operation times versus N, to plot on graphs and verify our code is correct.

Where do we start? Let's start with creating and removing items from a list.

Adda lotsa items, time the amortized cost of expanding..

Memory usage

No way to measure memory usage of objects in-memory, we are left on our own to performing memory profiling.

Linked Lists

Investigation of linked list scaling behavior (isee Linked Lists/Java/Timing) shows O(1) time per add or add/remove operation, both for a long sequence of add operations and a 75/25 mix of add/remove operations. Output from a timing script is shown here:

Timing script link:

Generic templated linked list being tested against the Java Collections API. TLinkedList class link: See


N, Add Amortized Walltime Per 1k Builtin (us), Add Amortized Walltime Per 1k SLL (us), Add Amortized Walltime Per 1k DLL (us)
1024, 30.273, 16.602, 30.273,
2048, 26.367, 16.602, 26.367,
4096, 31.250, 10.254, 31.250,
8192, 14.771, 13.916, 14.771,
16384, 17.700, 17.639, 17.700,
32768, 15.594, 6.989, 15.594,
65536, 15.274, 7.874, 15.274,
131072, 14.778, 8.125, 14.778,
262144, 15.751, 8.671, 15.751,

N, Add Remove Amortized Walltime Per 1k Builtin (us), Add Remove Amortized Walltime Per 1k SLL (us), Add Remove Amortized Walltime Per 1k DLL (us)
1000, 43.000, 29.000 27.000
4000, 29.000, 19.250 18.500
16000, 21.313, 18.500 18.813
64000, 21.672, 18.969 18.859
256000, 21.738, 18.906 19.586

The first column shows the size of the input, the number of elements being added or removed from the list. (N is an operations count.) This time, normalized per 1000 operations, is shown for three cases:

  • Second column - time in microseconds per 1000 operations for Java Collections LinkedList
  • Third column - time in microseconds per 1000 operations for TLinkedList, singly-linked generic linked list
  • Fourth column - time in microseconds per 1000 operations for DLinkedList, doubly-linked generic linked list

You can see that the amortized runtime, as the input size scales from thousands to millions, remains constant for all three types, except at small sizes where algorithm performance is harder to compare.