Game of Life in Scratch

Game of LifeA few days ago I asked for volunteers to read a book I was writing on programming, using Scratch, MIT’s visual, event-driven, programming environment.

I have not yet had any volunteers, though the flurry of online interest did get me to complete the first draft – so alpha testers still needed.

In the meantime, I have also published the version of Conway’s Game of Life the text is based around. It’s a slightly unusual implementation in that the surface of the game is, in effect, spherical┬áin effect a torus – i.e. the edges are effectively a feature of the projection of the surface but left joins to right, top to bottom and so on.

Update: Been pointed out to me that this isn’t a sphere but a torus, and so I have updated the copy.

Squashing the LRU bug

English: Typical actions taken upon a virtual-...
English: Typical actions taken upon a virtual-physical address translation. (Photo credit: Wikipedia)

Just for once I did not rush to an online forum and say I had found a bug in a product – and I was right not too.

Having tried three different cross compiler toolchains I convinced myself that the issue was plainly neither compiler (or, to be more accurate in this case, assembler) output but some process in my code that was causing corruption. And sure enough I found that I was mangling the physical addresses of my page frames.

Thanks to the way OVPsim operates – by default it provides physical mappings on demand for the full 4GB address space of a 32 bit register – this mangling did not generate a page fault, but it did mean that for certain sequences of instructions – particularly those where lots of page faults were likely to occur, memory was being corrupted.

Changing one line of assembly – so that virtual address output was written to virtual address slot and not the physical address slot in my simple list of page table entries fixed that.

So now the code works – at least I think it does!

First working assembly code in 33 years

When I say working, I mean boots into an endless loop with no ouput to the user – but then, that’s what it’s meant to do:

/* 	Microblaze start up code
	Copyright Adrian McMenamin <>, 2014
	Licensed under GPL v3

	.global _start
	.section .vectors.reset, "ax"
	.align 2
	.ent _start
	.type _start, @function

	brai	_actualstart
	.end _start

	.section .vectors.sw_exception, "ax"
	.align 2
	brai	_handle_exception

	.section .vectors.interrupt, "ax"
	.align 2
	brai	_interrupt_handler

	.section .vectors.breakpoint, "ax"
	.align 2
	brai _handle_breakpoint

	.section .vectors.hw_exception, "ax"
	.align 2
	brai	_handle_hwexception

	.section .text
	.global _actualstart
	.align 4
	.ent _actualstart
	.type _actualstart, @function

	mts	rmsr, r0
	mts	rslr, r0
	addi	r8, r0, 0xFFFFFFFF
	mts	rshr, r8

	addik	r3, r0, 0x3F
	mts	rtlbx, r3

	bri 0 /*endless loop if it ever works */

Only have to get it to do something useful now, how hard could that be? (There is a linker script to get this to work also – as it has to be loaded to address 0x00000000. Have a look at my github (mcmenaminadrian) repos.

Curses on ncurses

gdb icon, created for the Open Icon Library
gdb icon, created for the Open Icon Library (Photo credit: Wikipedia)

Every programmer will be familiar with something like this…

A little while back I wrote a program that simulates – crudely but effectively – a multicore NoC device. I use it to model the execution times of different page replacement algorithms.

The input is XML generated via a step by step trace of a working program. The actually instructions being traced do not matter – what I care about are the memory access patterns.

To allow me to test more models more quickly I have now written some R code that generates a semi-random access pattern based, very loosely indeed, on the patterns seen in the real program. The advantage is I can test against a set number of memory accesses but with a range of pseudo-random access patterns, so although I am not running models against the “real” access pattern, neither am I taking three weeks per experiment.

But when I used the artificially generated access patterns, my program crashed with a seg fault. But even more confusingly, when I ran the code in GDB, the GNU Debugger, if I stepped through the code it worked, but I just ran the code in debugger then it crashed just as it did without using the debugger.

After a few hours I realised why – in my artificial patterns, the first thing the first thread does is spawn all the other threads to be used. In real world code, of course, these spawns take place after quite some code has been executed.

Every code spawn causes the ncurses code I am using to update the screen. When using ‘real’ access patterns these updates take place comfortably after all the ncurses environment has been set up (by a separate thread), but in the artificial code, the thread updates are the first thing that get posted to the screen, even before ncurses has been set up – hence the crash.

If I step through the code then the ncurses thread runs ahead and sets up the screen before I hit the thread update code and again it works.

The solution? Use a condition variable and a mutex to ensure that nothing executes before the ncurses environment is fully established.

Not a big deal – but perhaps, at some point in the future someone struggling to understand why their code – which previously worked so well – has now stopped processing what seems to be well-formed input. Hope this helps!

After paging?

Diagram of relationship between the virtual an...
Diagram of relationship between the virtual and physical address spaces. (Photo credit: Wikipedia)

Paging and virtual memory is at the heart of just about any computing device – more complex than a DVD player – we use everyday.

Paging is the memory management system based on the idea that we can divide the real memory of our computer into a sequence of smallish (typically 4,096 bytes) of “page frames” and then load the bits of data and program in and out of those frames (in “pages”) as we need them.

So, you can have pages from various different running programs in the page frames at any given time and then use a “virtual memory” system to map the pages placed in an arbitrary frame to the memory address the program thinks the page should be resident in.

It is not the only system we could use – “segments”, which involve moving large chunks (as opposed to small pages) of memory about is one approach, while “overlays” – using part of the memory space as sort of scratchpad working area – is another. More recently, with bigger “traditional” computers very large pages have been used as a way of making, at least in theory, more efficient use of memory now measured in billions (as opposed to a few tens) of bytes.

But paging is easily the most widely used approach and has been integral to the development of “multitasking” and similar shared resources approaches to computing – because paging allows us to just have the useful bits of a program and its data in memory we can have many more programs “running” at a given time.

But my PhD research is pointing me towards some of the weaknesses of the paging approach.

At the heart of the case for paging is the idea of “locality” in a computer’s use of resources: if you use one memory address at one instant there is a high probability you will use a nearby address very soon: think of any sort of sequential document or record and you can see why that idea is grounded in very many use cases of computing devices.

Locality means that it ought to make sense to read in memory in blocks and not just one little drop at a time.

But this principle may be in opposition to efficient usage of memory when competition for space in fierce: such as for the limited local memory resources we have on a Network-on-Chip computer.

Right now I am collecting data to measure the efficiency of 4k pages on such (simulated) devices. With 16 simulated cores trying to handle up to 18 threads of execution competition for pages is intense and the evidence suggests that they are resident, in some cases at least, for many fewer “ticks” than it takes to load them from the next lowest level in the memory hierarchy. On top of that many pages show that the principle of locality can be quite a weak one – pages of code are, in general, quite likely to demonstrate high locality (especially in loops) but pages of read-write memory may not.

I don’t have all the data to hand – essentially I am transforming one 200GB XML file into another XML file which will likely be around the same size and that takes time, even on quite a high spec computer (especially when you have to share resources with other researchers). But I expect some interesting results.

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Passing by reference in C++ – a downside

Code (Photo credit: Riebart)

Once you realise what is happening this is obvious, but it took me a while…

I wanted to write some longs out as character strings in C++, so wrote some code like this:

void writeLongToFile(ofstream& xmlFile, long& value)
stringstream stringy;
stringy << value;
xmlFile << stringy.rdbuf();

But compile time error after compile timer error followed as I was told that I could not use this code with unsigned long or with const long and so on … but surely these are simple conversions I thought…

…Just me thinking like a C programmer again. Passing by reference is just that – there is no copying or conversion available and so long and unsigned long are fundamentally incompatible.

The solution? Just pass by value – afterall the saving in passing a more or less primative type like long by reference must be pretty minimal – passing the stream by reference is the real saving here (actually, it’s also practical – as we want the output to go to the real stream and not a copy: in C we’d use a pointer here).

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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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