alt Hacker NewsBeing I/O bound is usually a result of bad engineering practices though. If you're I/O bound, that either means the problem doesn't require much computation - which is possible but fairly rare, or more likely that your code is so unoptimised that barely any computation gets carried out while your code is waiting on memory/disk/network.
"I can't do anything because my program is I/O-bound" is more of an excuse / mental justification of why your program is slow instead of an honest reason for so.
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ETL processes are heavily I/O bound, especially when you're trying to shuttle data from one enterprise system to another enterprise system. It's also common when the culture of data exchange from the regulator all the way down to the companies doing the actual work is batch processing where large amounts of data are shared once a day as opposed to real time. Excel spreadsheets are the norm, not the exception. Requests for data to be sent over via XML or JSON are mainly because my employer wanted to make it easier to process the data ourselves, but the regulators actually expect spreadsheets.
Most of the stuff I work on is almost exclusively network I/O bound. I wouldn't say it's a _result_ of bad engineering practices, though. One group decided on a particular system that's also public-facing, and the group I actually support prefers a more internal-facing system. It also doesn't help that the budgets for both projects are completely separate and firewalled from each other by law. Growth opportunities don't apply here because I deal with a captive market with legally-forced customers.
No, I don't think the way you're characterizing this is accurate. I/O is inherently very slow compared to computation. And many programs genuinely don't have any useful computation to do while waiting for I/O - because the result of that I/O operation contains the information needed for the program to even make its next decision.
Such programs are not necessarily impossible to optimize. One common optimization is to use an event loop, allowing just a few threads to handle thousands of concurrent operations. Because while a thread is waiting for I/O in one request or unit of work, in the meantime it moves on to work on processing another request/unit. Another common optimization is batching/grouping of I/O calls.
Plenty of scientific simulations end up being IO or communications bound when at scale (e.g. 30k CPU cores). Can hide latency to a certain degree but basically any algorithm that uses timestepping must halt at some point to allow data to flow around the nodes, or to dump data to disk for visualisation or checkpointing.
In saying that there are some novel and very clever algorithms that continue on without seemingly necessary boundary data, that then self correct when the data comes through, thus completely hiding the latency at the cost (in both accuracy and time) of running a correction process.
The computers at the time had very little ram. The IBM System/360 didn’t get 1MB until 1968. I suspect a lot of programs were I/O bound just to be able to work at all. Most modern engineers cannot conceive of doing anything useful with 64KB and I think it’s a mistake to project modern practices 50 years into the past.
> If you're I/O bound, that either means the problem doesn't require much computation - which is possible but fairly rare
This is backwards. I bet that by count, many more programs are written in domains where they're necessarily IO bound than the inverse. Anything that uses the network for its core functionality, anything reliant on a datasource whose aggregate contents are O(memory)+ size, or anything reliant on slow peripherals (lots of embedded software) are in this class.
Scientific simulations, HFT algorithms, video games, LLMs, etc.--the stuff in the other class--aren't inconsequential, but they're dwarfed in number by the class of software that spends 99+% of its time waiting for IO. Hell, entire programming languages (node.js) have been created in response to that proportion.