AlgorandSIMULATION v1.0
Sequential Engines(17)
Parallel Engines

Timsort

sequentialRAM

A hybrid stable sorting algorithm derived from Merge Sort and Insertion Sort, designed to perform exceptionally well on real-world data containing pre-existing ordered sequences (runs).

SIMULATION STEP PROGRESSStep 1 / 1
SPEED (50%)
SIZE (8)
💡 Tip: Enter integers between 1 and 99. Max size is 16.
Code Workspace
1procedure TimSort(A: array, RUN_SIZE)
2 n := length(A)
3 for i := 0 to n-1 step RUN_SIZE do
4 insertionSort(A, i, min(i + RUN_SIZE - 1, n - 1))
5 end for
6 size := RUN_SIZE
7 while size < n do
8 for left := 0 to n-1 step 2 * size do
9 mid := left + size - 1
10 right := min(left + 2 * size - 1, n - 1)
11 if mid < right then merge(A, left, mid, right)
12 end for
13 size := size * 2
14 end while
15end procedure
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RUNTIME COST METRICS
Comparisons0
Memory Writes0
Memory Reads0
Elements Swapped0
Computational Model:RAM (Single Core)
Theoretical Bound:O(N log N)
Space Complexity:O(N)
Communication Cost:0 (Shared Bus)
THEORETICAL ANALYSIS & RESEARCH

Conceptual Focus: A hybrid stable sorting algorithm derived from Merge Sort and Insertion Sort, designed to perform exceptionally well on real-world data containing pre-existing ordered sequences (runs).

Real-world Industrial Applications:
  • Default sorting in Python (list.sort, sorted)
  • Default sorting for non-primitive arrays in Java (Arrays.sort)
  • High performance production standard library sorting