A

compare-exchangeoperation on two array elements $A[i]$ and $A[j]$, where $i < j$ has the form:`COMPARE-EXCHANGE(A, i, j) if A[i] > A[j] exchange A[i] with A[j]`

After the compare-exchange operation, we know that $A[i] \le A[j]$.

An

oblivious compare-exchange algorithmoperates solely by a sequence of prespecified compare-exchange operations. The indices of the positions compared in the sequence must be determined in advance, and although they can depend on the number of elements being sorted, they cannot depend on the values being sorted, nor can they depend on the result of any prior compare-exchange operation. For example, here is insertion sort expressed as an oblivious compare-exchange algorithm:`INSERTION-SORT(A) for j = 2 to A.length for i = j - 1 downto 1 COMPARE-EXCHANGE(A, i, i + 1)`

The

0-1 sorting lemmaprovides a powerful way to prove that an oblivious compare-exchange algorithm produces a sorted result. It states that if an oblivious compare-exchange algorithm correctly sorts all input sequences consisting of only 0s and 1s, then it correctly sorts all inputs containing arbitrary values.You will prove the 0-1 sorting lemma by proving its contrapositive: if an oblivious compare-exchange algorithm fails to sort an input containing arbitrary values, then it fails to sort some 0-1 input. Assume that an oblivious compare-exchange algorithm X fails to correctly sort the array $A[1..n]$. Let $A[p]$ be the smallest value in $A$ that algorithm X puts into the wrong location, and let $A[q]$ be the value that algorithm X moves to the location into which $A[p]$ should have gone. Define an array $B[1..n]$ of 0s and 1s as follows:

$$ B[i] = \begin{cases} 0 & \text{ if } A[i] \le A[p] \\ 1 & \text{ if } A[i] > A[p] \end{cases} $$

- Argue that $A[q] > A[p]$, so that $B[p] = 0$ and $B[q] = 1$.
- To complete the proof 0-1 sorting lemma, provide that algorithm X fails to sort array $B$ correctly.
Now you will use the 0-1 sorting lemma to prove that a particular sorting algorithm works correctly. The algorithm,

columnsort, works on a rectangular array of $n$ elements. The array has $r$ rows and $s$ columns (so that $n = rs$), subject to three restrictions:

- $r$ must be even,
- $s$ must be a divisor of r, and
- $r \ge 2s^2$.
When columnsort completes, the array is sorted in

column-major order: reading down the columns, from left to right, the elements monotonically increase.Columnsort operates in eight steps, regardless of the value of $n$. The odd steps are all the same: sort each column individually. Each even step is a fixed permutation. Here are the steps:

- Sort each column.
- Transpose the array, but reshape it back to $r$ rows and $s$ columns. In other words, turn the leftmost column into the top $r/s$ rows, in order; turn the next column into the next $r/s$ rows, in order; and so on.
- Sort each column.
- Perform the inverse of the permutation performed in step 2.
- Sort each column.
- Shift the top half of each column into the bottom half of the same column, and shift the bottom half of each column into the top half of the next column to the right. Leave the top half of the leftmost column empty. Shift the bottom half of the last column into the top last column into the top half of a new rightmost column, and leave the bottom half of this new column empty.
- Sort each column
- Perform the inverse of the permutation performed in step 6.
Figure 8.5 shows an example of the steps of columnsort with $r = 6$ and $s = 3$. (Even though this example violated the requirement that $r \ge 2s^2$, it happens to work.)

- Argue that we can treat columnsort as an oblivious compare-exchange algorithm, even if we do not know what sorting method the odd steps are.
Although it might seem hard to believe that columnsort actually sorts, you will use the 0-1 sorting lemma to prove that it does. The 0-1 dorting lemma applies because we can treat columnsort as an oblivious compare-exchange algorithm. A couple of definitions will help you apply the 0-1 sorting lemma. We say that an area of an array is

cleanif we know that it contains either all 0s or all 1s. Otherwise, the area might contain mixed 0s and 1s, and it is dirty. From here on, assume that the input array contains only 0s and 1s, and that we can treat it as an array with $r$ rows and $s$ columns.

- Prove that after steps 1-3, the array consists of some clean rows of 0s at the top, some clean rows of 1s at the bottom, and at most $s$ dirty rows between them
- Prove that after step 4, the array, read in column-major order, starts with a clean area of 0s, ends with a clean area of 1s, and has a dirty area of at most $s^2$ elements in the middle.
- Prove that steps 5-8 produce a fully sorted 0-1 output. Conclude that columnsort correctly sorts all inputs containing arbitrary values.
- Now suppose that $s$ does not divide $r$. Prove that after steps 1-3, the array consists of some clean rows of 0s at the top, some clean rows of 1s at the bottom, and at most $2s - 1$ dirty rows between them. How large must $r$ be, compared with $s$, for columnsort to correctly sort when $s$ does not divide $r$?
- Suggest a simple change to step 1 that allow us to maintain the requirement that $r \ge 2s^2$ when $s$ does not divide $r$, and prove that with your change, columnsort correctly sorts.

This one is tricky. I would not have been able to solve it by myself. I had help from those two:

We know that $A[q] > A[p]$ by definition ($A[q]$ is misplaced, but it cannot be smaller than $A[p]$, since $A[p]$ is the smallest misplaced element). From this it follows that $B[p] = 0$ and $B[q] = 1$.

To prove the rest, we need to establish that a monotonous mapping and a compare-exchange operation commutate, that is, they can be applied in any order. This makes sense, since if the mapping is applied first, the order of the elements would not change (because the mapping is monotonic) and the compare-exchange would have the same result.

An oblivious compare-exchange algorithm can be regarded as a sequence of compare-exchange operations. Thus, it doesn't matter if the monotonous mapping is applied before the fist or after the last compare-exchange operation.

Applying that to $A$ and $B$, we conclude that $B[q] = 1$ and $B[p] = 0$. We know that $q < p$, otherwise $A[q]$ there would have been a smaller misplaced element. From this we gather that $B[q] > B[p]$ and $q < p$, which means that the array is unsorted.

There is a more formal proof in the first link.

Since the even-numbered steps perform things *blindly*, we can suspect that
the algorithm has some elements of obliviousness in it.

If we perform the odd numbered steps with an oblivious compare-exchange algorithm, then columnsort is obviously oblivious and we can apply the 0-1 sorting lemma. Since we can treat those steps as "black boxes", we can replace the sorting algorithm with any other algorithm that produces the same result (that is, any sorting algorithm) and the resulting columnsort would still sort.

After the first step, each column becomes a sequences of 0s followed by a
sequence of 1s. In this sense, there is only one `0 → 1`

transition in each
column. Since $s$ divides $r$, each column will map to $r/s$ rows. One of
those rows will contain the `0 → 1`

transition. The others will contain only
0s or 1s. That is, each column will map to at most one dirty row and the rest
will be clean.

After the transposition, and second sorting, the clean rows of 0s will move to the top and the clean rows of 1s will move to the bottom. We're left with at most $s$ dirty rows in the middle.

After the reversal of the permutation, the $s$ dirty rows will map to a sequence of $s^2$. All the other elements are clean.

The dirty sequence is at least half a column long now. It either fits in one column or crosses over in the next one. All columns left of it contain only 0s and all columns right of it contain only 1s.

If the result is contained in a single column, step 5 will result in a sorting in column major mode and the subsequent steps will not interfere with it.

If not, step 6 arranges the columns in a way that the dirty subsequence will fill a single column. Sorting all column cleans it and we have a sorted array.

Note that sorting the half-columns is unnecessary - step 5 already sorted them.

If $s$ does not divide $r$, a row can contain not only a `0 → 1`

transition,
but also a `1 → 0`

transitions. There would be at most $s - 1$ of those,
resulting to a dirty region of $2s - 1$.

We can make $r$ to be at least $2(2s - 1)^2$. As for the change to step one, we can either pad the array with $+ \infty$ until $s$ divides $r$, or we can chop off a small part of the array and sort it separately. The latter will be more efficient, since it does not require moving the array.

Finally, all of that turns to be unnecessary - columnsort works without the divisibility restriction. Details can be found in the paper.

Surprising as it is, columnsort smokes the stdlib implementation of quicksort. I thought the overhead was to much, but it appears that it is not. Of course, the crossover point will vary.

stdlib sort = 3.422940 columnsort = 1.996803

#include <pthread.h> #include <math.h> #include <stdlib.h> #include <stdio.h> #define STDLIB_SORT qsort typedef unsigned int number; typedef struct { size_t start; size_t size; } column_t; typedef void column_sorter(number *, column_t *, int); void check_dimensions(size_t r, size_t s); /** * The basic column sort implementation. It does a copy of the array for steps * 3 and 5. It also does not sort the half-columns in the beginning and the * end, since that is not necessary for the correctness of the algorithm. */ void columnsort(number *A, size_t r, size_t s, column_sorter sort_columns) { size_t size = r * s; number *copy; column_t columns[s]; check_dimensions(r, s); copy = calloc(size, sizeof(number)); for (size_t i = 0; i < s; i++) { columns[i] = (column_t) {i * r, r}; } sort_columns(A, columns, s); for (size_t i = 0; i < size; i++) { copy[(i % s) * r + i / s] = A[i]; } sort_columns(copy, columns, s); for (size_t i = 0; i < size; i++) { A[i] = copy[(i % s) * r + i / s]; } sort_columns(A, columns, s); for (size_t i = 0; i < s - 1; i++) { columns[i] = (column_t) {i * r + r / 2, r}; } sort_columns(A, columns, s - 1); free(copy); } /* * A function that compares numbers, to be passed to the stdlib sort. */ int compare(const void *a, const void *b) { number *first = (number *) a; number *second = (number *) b; if (*first == *second) { return 0; } else if (*first > *second) { return 1; } else { return -1; } } /* * Verified the dimensions of the passed array. */ void check_dimensions(size_t r, size_t s) { if (r % 2) { fprintf(stderr, "r must be even\n"); exit(0); } if (r % s) { fprintf(stderr, "s must divide r\n"); exit(0); } if (r < 2 * s * s) { fprintf(stderr, "r must be grater than 2s²\n"); exit(0); } } /* * A utility function to call with the array and a column. */ void sort(number *A, column_t column) { STDLIB_SORT(A + column.start, column.size, sizeof(number), compare); } /* * Sequential sorting of columns */ void sequential_sort_columns(number *numbers, column_t *columns, int size) { for (int i = 0; i < size; i++) { sort(numbers, columns[i]); } } /* * Parallel sorting of columns. This implementation is a bit naïve - it can * reuse existing threads instead of spawning new ones every time. Furthermore, * I never explored using locking mechanisms instead of joining the threads. */ typedef struct { number *numbers; column_t column; } job_t; void *sort_job(void *pjob) { job_t *job = (job_t *) pjob; sort(job->numbers, job->column); return NULL; } void threaded_sort_columns(number *numbers, column_t *columns, int size) { void *status; pthread_t threads[size]; job_t jobs[size]; pthread_attr_t attr; pthread_attr_init(&attr); pthread_attr_setdetachstate(&attr, PTHREAD_CREATE_JOINABLE); for (int i = 0; i < size; i++) { jobs[i] = (job_t) {numbers, columns[i]}; pthread_create(&threads[i], &attr, sort_job, &jobs[i]); } for (int i = 0; i < size; i++) { pthread_join(threads[i], &status); } }