Clever memory usage through the years

A friend and I were recently talking about the really clever tricks people have used to get the most out of memory. I thought I’d share my favorites, and would love to hear yours too!

Interleaving on drum memory

Back in the ye olde days before I’d been born (we’re talking the 50s / 60s here), working memory commonly took the form of rotating magnetic drums. These would spin at a constant speed, and a fixed head would read from memory when the correct part of the drum passed it by, a bit like a primitive platter disk. Because each revolution took a few milliseconds, programmers took to manually arranging information non-sequentially on the drum, timing when an instruction or memory address would need to be accessed, then spacing information accordingly around the edge of the drum, thus reducing the access delay. Similar techniques were still used on hard disks and floppy disks into the 90s, but have become irrelevant with modern disk technologies.

The Hashlife algorithm

Conway’s Game of Life has attracted numerous implementations over the years, but Bill Gosper’s Hashlife algorithm is particularly impressive. Taking advantage of the repetitive nature of many cellular automata, it uses a quadtree structure to store the hashes of pieces of the overall grid. Over time there are fewer and fewer new structures which need to be evaluated, so it starts to run faster with larger grids, drastically outperforming other algorithms both in terms of speed and the size of grid which can be simulated. The actual amount of memory used is huge, but it’s used in a clever way, so makes the list :-).

Elite’s procedural generation

Ok, so this isn’t exactly a memory optimization – more a storage optimization – but it gets an honorable mention anyway. When writing Elite, David Braben and Ian Bell wanted to build a rich world which gamers could explore, but their 22K memory was something of a limitation (for comparison that’s about the size of my avatar picture at the top of this page). They procedurally generated all the characteristics of the 2048 planets in their virtual universe, including the names, which were stitched together using a lookup table of parts of names. In fact the original plans were for 2^52 planets, but it was decided that that was probably too many. Oh, and they did that all in assembly language. Other games of the time used similar techniques too – The Sentinel’s landscape generation algorithm being another example.

Modern Garbage Collectors

Garbage collection in managed languages like Java and .NET ensures that most of the time, developers stop needing to care about how they use and clean up memory as the garbage collector handles it automatically. Achieving this without killing performance is a near-miraculous feat of software engineering. Much like when learning chemistry, you find that every time you think you understand how the garbage collector works, it turns out to be a mere simplification; that there are yet more complexities and heuristics to help it run efficiently. Of course introducing memory problems is still possible (and there are tools like our memory profiler to help if that happens to you) but they’re much, much rarer.

A cautionary note

In the examples above, there were good and well understood reasons for the optimizations, but cunningly optimized code has usually had to trade away readability and maintainability to achieve its gains. Trying to optimize memory usage without being pretty confident that there’s actually a problem is doing it wrong.

So what have I missed? Tell me about the ingenious (or stupid) tricks you’ve seen people use.