I bought an OpenWrt One WiFi router from “Bipai Corp”, via AliExpress.

The list price was $89, I got it for $109 with shipping from China.

Trilobite: Eyewitness to Evolution

Richard Fortey, 2001, ISBN 978-0375706219

I decided my WRT3200ACM Linksys WiFi router is garbage. There’s something about the hardware that neither Linksys factory firmware, nor OpenWrt can deal with. It drops a lot of packets.

I bought an Asus AX6000 TUF gaming router, which is kind of embarrassing, but it supposedly works well with OpenWrt.

• Problem statement and some experimentation
• Explanation of the algorithm I came up with in 2021.
• Recursive merge sort algorithm to have something to compare
• Re-using linked lists while benchmarking
• Effect of address-ordered linked lists
• Traversing linked lists does not exhibit weird performance drops at specific list lengths
• Effect of size of linked list nodes tests whether virtual memory is somehow involved. It is not.
• Bottom-up implementation with linked lists from Wikipedia, has the same performance drops as my July 2021 algorithm
• Memory access effects: incrementing .Data values, or arbitrarily changing .Next pointers does not cause the performance drops, and looking for other anomalies
• Effect of garbage collection before preparing a new, unsorted, linked list
• The memory access patterns of recursive and wikipedia bottom up algorithms are identical, for linked lists that are powers-of-2 long. For other length linked lists, the memory access patterns are not.
• I wrote an iterative, explicit stack frame variation of recursive mergesort to try to get the same abrupt performance drops as the wikipedia bottom up algorithm. No joy.
• Recursive mergesort variations that explore specific differences between iterative and recursive algorithms do not impart any revelations.
• Counts of comparison operations finally reveals the cause of the performance drops.
• More counts of comparison operations, this time featuring pre-sorted and reverse-sorted initial linked lists

What happens if you compare the number of comparisons made by recursive and wikipedia bottom up algorithms if the initial list is pre-sorted (low data value to high data value) or reverse sorted (high data value to low).

My recursive mergesort algorithm, and the wikipedia bottom-up) algorithm read and write the same list nodes in the same order, for list lengths that are powers of two. Something other than mere data reading and pointer writing causes abrupt performance drops at linked list lengths of 2^N+1 nodes.

I decided to count the number of comparisons used in merging already sorted lists.

I tried several minor variations of recursive mergesort to try to understand the abrupt performance drops that my iterative mergesort code exhibits at some linked list lengths.

There’s a last time for everything. My last Wordle was number 1234.

I decided I can no longer give anything, not views, not ad views, not clicks, to the pro-Trump New York Times. I am rage quitting Wordle.

Here’s my Wordle wrap up stats.

I wrote variants of recursive mergesort that simulate function call recurision by iteration with an explicit stack of activation records. This is an effort to try to understand the abrupt performance drops that the wikipedia bottom up algorithm exhibits at some linked list lengths.

Incidentally, these two algorithms both satisfy the Daily Coding Problem problem statement. Their simulated call stack could potentially overflow for extremely long lists, but those stacks wouldn’t have to be much bigger to accomodate those long lists.