None of the easy things work

This is a post about my PhD research: in fact it is a sort of public rumination, an attempt to clarify my thoughts in writing before I take the next step.

It’s also possibly an exercise in procrastination: a decision to write about what I might do next, rather than to get on with doing it, but I am going to suppress that thought for now.

I am looking for ways to make “network on chip” systems more viable as general use (or any use, I suppose) computing platforms. These systems are a hardware response to the hardware problem that is causing such difficulties for big software and hardware manufacturers alike: namely that we cannot seem to make faster computers any more.

The problem we have is that while we can still get more transistors on a chip (i.e., that “Moore’s Law” still applies), we cannot keep operating them at faster speed (i.e., “Dennard Scaling” has broken down) as they get too hot.

In response we can either build better small devices (mobile phones, tablets) or try to build faster parallel computing devices (so instead of one very fast chip we have several moderately fast chips and try to have better software that makes good use of their ability to compute things in parallel).

Network-on-chip (NoC) processors are a major step along the road of having parallel processors – we put more processing units on a single piece of silicon rather than have them co-operate via external hardware. But the software has not caught up and we just cannot keep these chips busy enough to get the benefit their parallelism might offer.

That is where I hope to make a difference, even if just at the margins. Can I find a way to make the NoC chips busier, specifically by keeping them fed with data and code from the computer memory fast enough?

I have tried the obvious and simple methods: essentially adaptations of methods that have been used for most of the last 50 years in conventional serial computer devices and the answer is ‘no’ if that is all that is on offer.

Messing about with the standard algorithms used to feed code and data to the processors hits a solid brick wall: the chips have a limited amount of ‘fast’ local memory and the time it takes to keep that refreshed with up-to-date code and data places a fundamental limit on performance.

So, while many computer science students might be familiar with “Amdahl’s Law” which stipulates that, for parallel code, the elements that have to be run in serial (even if just setting up the parallel section) place a fundamental limit on how much extra performance we can squeeze out by throwing more and more parallel processors at the problem – we have a different, if related, problem here. Because we can apply more and more parallel processors to the problem but the performance remains constant, because even though we are running parallel code, we are limited by memory performance.

This limit – which implies that as we use more processors they become individually less efficient – even hits the so-called “clairvoyant” or optimal (OPT) memory management/page replacement algorithm: OPT knows which memory page it is most efficient to replace but is still limited by the fundamental barrier of limited on-chip memory.

The limit is manifest in the straight lines we can see in the plot here – the steeper slope of OPT means it runs faster but after the first few processors are brought to bear on the problem (the number of processors being used climbs for the first few billion instructions) the rate of instructions executed per ‘tick’ (an analogue of time) is constant.

OPT and LRU compared - both fundamentally stymied by memory shortage
OPT and LRU compared – both fundamentally stymied by memory shortage

 

Getting NoCs to run faster and so releasing the benefits from the potentially massive parallelism they could bring, depends on beating this memory barrier (and lots of other things too, but one at a time!). So, what are the options?

Well, one thing I can rule out is trying to cache a particular piece of a memory page (in traditional operating systems memory is shifted about the system in blocks called pages – typically 4096 bytes long). Caches typically store memory in 16 byte “lines” – hardware reads from the backing memory store in 16 byte blocks in most cases – and so I tested to see if there was a pattern in which 16 byte line was most likely to be used (see previous blog post). My instinct from looking at the plot is that will not work.

Similarly, a look at which pages were being used doesn’t reveal any immediately obvious pattern – some pages are used heavily by code, some are not – nothing surprising there.

So, the easy things do not work. Now I need to look at the hard things.

I think I need to escape from the page paradigm – one thing to look at is the size of the memory objects that are accessed. 4k pages are simple to handle – load a block in, push it out: but they could be (probably are) very inefficient. Might it be better to base our memory caching system on object sizes? That’s what I plan to check.

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