Penguin & Cluster
Cluster
Hey, I was thinking about the challenge of sorting data that’s bigger than RAM. You know, like using external merge sort or a custom hybrid. What do you think about the trade‑offs between algorithmic speed and memory fragmentation?
Penguin
External merge sort is clean because it keeps the working set in small, contiguous runs that fit in RAM, so you avoid the overhead of frequent page faults. But each pass writes a whole new file, which can fragment disk space if you’re not careful with block sizes. If you tune the run size to match your allocation granularity you keep fragmentation low, but you’re paying the cost of more I/O per element. A hybrid that keeps a modest buffer pool can reduce the number of passes and make better use of cache, but you’ll need to manage the pool carefully so it doesn’t grow beyond what the OS will efficiently keep resident. In short, faster algorithms usually mean more temporary data and the risk of fragmentation, so you have to balance the size of each run against how much RAM you can safely use without thrashing.
Cluster
Sounds like a classic memory‑bottleneck trade‑off, but if you’re really serious you could write the merge pass in Nim just to prove the compiler’s allocator is actually optimal. Or use a lightweight buffer pool in a custom C library you’ve written in Racket, because, honestly, any dependency that doesn’t compile on a 32‑bit ARM with no C99 support is a no‑go. Just keep your run size a power of two, and if you hit a fragmentation spike, you know it’s time to drop the block size from 4 MiB to 2 MiB and let the OS do its best garbage‑collection dance.
Penguin
Nice plan – Nim’s allocator is fast, but the real win comes from careful block sizing. On a 32‑bit ARM you’ll hit that 4 MiB ceiling quickly, so start with 2 MiB runs; if you see a spike in fragmentation, drop it to 1 MiB and let the OS consolidate. A lightweight C pool wrapped in Racket is fine, just keep the allocation units aligned and pre‑commit them. In the end, the trade‑off is still I/O versus RAM, so keep the run size a power of two, but be ready to shift it when the fragmentation curve climbs.
Cluster
Nice, so you’re basically doing a bit‑wise binary search on block size—exactly the kind of math I enjoy. Just remember to flush the pool every time you hit the 4 MiB boundary, otherwise the OS will think it’s a free‑floating allocation. If the fragmentation still spooks you, maybe add a sanity check that writes a dummy page to each new run; that’s how I keep the debugger happy.
Penguin
That’s the right mindset – treat the pool like a binary counter and roll it over at each 4 MiB mark, it keeps the allocator happy. Adding a dummy page per run is a clever sanity check; just make sure you don’t over‑inflate the I/O just to satisfy the debugger. Keep the block sizes tidy, flush when you hit the boundary, and the fragmentation should stay under control.