> Note that "minor" implementation issues like die space, routing, and gate delays, especially of 128-bit adders & shifters are non-trivial, so people aren't going to rush out and build 128-bitters for fun, just as people matched timing dates of their 64-bitters to their expected markets.
I think we're stuck with 64 bit for quite a while. The circuit size jump from 64 bit to 128 bit is significant. There's no fixed scalar or apples to apples comparison (that I'm aware of). Just for adders though, 2 bits requires 2 full adders, 4 bits requires 4 adders, 8 requires 8, etc.
Another way to look at this is that 16 bit gives addressable memory up to 65k, and quite a few programs had to deal with paging in architectures like the 8086/8088. 32 bits gave us up to 4GB addressable memory, and not it's now not uncommon that a program such as a web browser exceeds this. 64 bits would give us up to 18 exabytes of addressable memory. I'm not aware of any common programs breaking into the terabyte category (even in most research), let alone petabyte and then exabyte.
Even iterating over that many numbers becomes a large computational task. Just a quick test program:
// gcc -O3 count.c -o count
#include <stdint.h>
int main(){
uint64_t z = 0;
for(uint64_t i = 0; i < UINT64_MAX; i++) z += i;
return (int)(z % 2);
}
Using uint32_t and UINT32_MAX, it returns almost instantly. For uint64_t and UINT64_MAX you will be waiting a long time. 128 bit values? Even longer. Maybe many many cores could break that memory up, but then it makes sense to have a 64 bit system with some kind of ability to occasionally change page.Wouldn't that be the commercial LLMs? ChatGPT, Claude etc are estimated to be in the 2-10 TB range, and a large portion of the population of developed countries are using those apps commonly. Not on their own systems, but if we're in the 1995 academic perspective, multiuser systems are assumed.
I imagine the strongest reason we won't need 128-bit memory addressing is because horizontal scaling is easier. If OpenAI had needed a single addressing plane to cover all their users, 128-bit might be required. Similar to how the internet hobbles along fine with 32-bit addressing by just adding a layer of indirection to the internet with NAT.
I guess the other example of 128-bit addressing is ZFS. What are the biggest ZFS file systems? And who has more data? S3? Once you get bigger than 64-bit you want to scale out horizontally anyway and not just put it all into one flat addressable plane.
But in practice, we've produced a lot more compute. The memory / CPU ratio has increased, but most growth has been from more CPUs (and generally not shared-memory ones, but ones with their own completely separate memory space).
IPv6 is 128 bit, so we do have 128 bit ways of addressing computers, but I'd say we're still a long way from a CPU needing to address that much memory as a common case. The speed of light limits how far away memory can be from the CPU for good performance, so short of some drastically new memory technology, it seems unlikely we'll need it soon for any ordinary type of computing device.
But there was another thing - clusters of thousands/millions of machine instead of one big iron with all the memory.
As for address width, we're not even close to get full 64 bit pointers from CPUs anyway, more like 48 or 52 bits.
Wide pointers (eg 128 bit general registers) would make sense for carrying capabilities for memory safety though I guess.
While probably niche and applications that need higher precision using their own custom types anyway, they'd allow cool stuff like easy to program fractals with much higher detail than now. But also, less precision loss in many applications.
The issue is the utility of floats at different precisions. 128-bit floats have some benefits for high-end scientific applications, but the extra cost and complexity over 64-bit hardware would only make sense for specialised scientific supercomputing. So far it just hasn't been worth it.
If 64 bit isn't enough, then very quickly 128 is also not enough.
If precision is important then you will very often want systems that represent every number as an interval [a, b] meaning that the true value is between those 2 numbers. This makes you able to detect loss of precision due to e.g. d = a / (b - c) where b-c can result in a number close to zero which makes uncertainty grow. If you use this formula iteratively then precision is lost completely no matter how many bits there are in your floating point variables.