About Me

Michael Zucchi

 B.E. (Comp. Sys. Eng.)

  also known as Zed
  to his mates & enemies!

notzed at gmail >
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Saturday, 09 September 2017, 04:53

zcl 0.6

Yes it still lives. I've just uploaded an update to zcl

A bunch of bugfixes, new build system, more robustness, and OpenCL 2.1 support.

There are still some thing i'm experimenting with - primarily the functional/task stuff as it's just not flexible enough - but it's stable and robust and easy to work with so i'm no longer using JOCL for anything at work.

On a personal note I still haven't really gotten back into hacking and i had a short sojourn into facebookland so i haven't had much to write about. It's mostly been work, very poor sleep, and drinking! Oh and I started wearing kilts ...

Tagged java, opencl.
Friday, 21 October 2016, 05:50

Zed's not dead

I just haven't been coding or doing really anything terribly interesting lately!

And so it goes.

Update: 29.11.16 And so it goes ...

Tagged biographical.
Friday, 03 June 2016, 07:04

Using GNU make to build Java software

I finally finished writing an article about Java make i started some time ago, multiple times. I was going through cleaning up a new release of dez (still pending) and decided to fill it out with the junit stuff and then write it up what I actually ended up with.

The following few lines is now the complete makefile for dez. This supports `jar' (normal build target), `sources' (ide source jar), `javadoc' (ide javadoc jar), dist (complete rebuildable source), and now even `test' or `check' (unit and integration tests via JUnit 4) targets. The stuff included from java.make is reusable and is under 200 lines once you exclude voluminous comments and documentation.

java_PROGRAMS = dez

dez_VERSION=-1
dez_JAVA_SOURCES_DIRS=src
dez_TEST_JAVA_SOURCES_DIRS=test

DIST_NAME=dez
DIST_VERSION=-1.3
DIST_EXTRA=COPYING.AGPL3 README Makefile

include java.make

The article is over on my home page at Using GNU Make for java under my software articles section.

Tagged code, dez, gnu, hacking, java.
Sunday, 29 May 2016, 05:04

Images, Pixels, Java Streams

This morning I wrote and published article about writing an image container class for Java which supports efficient use of Streams. It is on my local home page under Pixels - Java Images, Streams.

Although there is much said of it, there is still quite a bit unsaid about how many wrong-footed experiments it took to accomplish the seemingly obvious final result. The code itself is now (or will be) part of an unpublished library I apparently started writing just over 12 months ago for reasons I can no longer recall. It doesn't have enough guts to make publishing it worthwhile as yet.

I'm also still playing with fft code and toying with some human-computer-interaction ideas.

Tagged code, hacking, java.
Sunday, 15 May 2016, 09:40

It was the best of times, it was the worst of times. More fft, more bang for your buck. Same old less bang for your buck.

After posting the result I kept experimenting with the code I `live blogged' about yesterday. I did some linlining but was primarily experimenting with multi-threading. I also looked at a decimation in time algorithm (which i kept fucking up until I got it working today), and a coupe of other things too, as will become apparent.

First a picture of a thousand words.

Now the words.

This picture shows the CPU load over time (i'm sure you all know what that is) as I ran a specific set of tests on a test against 8x runs of a 2^24 complex forward transform. The lines represent each core available on this computer. I put some 2s sleep calls between steps to make them distinguishable. Additionally each horizontal pixel represents 250ms which is is about the minimum sampling time which gives usable results.

Refer to earlier posts as to what the names mean.

That's the setup out of the way, the first 3rd or so of the plot. It's important but not as important as the next bit.

Well that's it I suppose. It utilises more of the available cpu resource and executes in a shorter time. But how was that achieved?

As Deane would say, ``Well Rob, i'm glad you asked''.

It only required a couple of quite simple steps. Firstly I copied the "radix4" routine into the inner loop of "radix4_pass". The jvm compiler wont do this itself without some options and it makes quite a difference of itself. Then I copied this to another "radix6_pass" which takes additional arguments that defines a sub-set of a full transform to calculate. I then just invoke this in parts from 4x separate threads, and keep doing that sort of thing until I hit the "logSplit" point and subsequently proceed as before. It was a quick-and-dirty and could be cleaned up but probably wont add much performance.

It was a bit of mucking about with the addressing logic but once done it's actually a fairly minor change: yet it results in the best performance by far.

At this point i've explored all the isssues and am working on a complete implementation which ties it all together. I think I will write two implementations: one using fully expanded tables for "ultimate performance" and another which calculates the Wn exponents on the fly for "ultimate size". Today I got a DIT algorithm working so I will fill out the API with forward/inverse, pairs-of-real, real-input, perhaps in-order results, and a couple of other useful things to aid convolution performance. Oh and 2D of all that.

But now for a little rant.

Why is software still so fucking shithouse?

So as part of this effort, I ended up having to write my own cpu load monitor. The only one I had available on slackware only has a tiny graph and is mostly just a GUI version of top. The dark slate blue is user time, the grey area is idle, crimson is cpu load, irq is medium sea green and the io wait is golden rod. The kernel doesn't report particularly accurate values in /proc/stat but it sufficed.

But I had intended to annotate this image with some nice 'callouts' and shadowed boxes and whatnot so i wouldn't have to write those 1000 words just to explain what it was showing. However ...

gimp has turned into a "professional photographer editing suite" - i.e. a totally useless piece of junk for most of the planet (and pro photographers wont use it anyway?). So the only other application i had handy was openoffice "dot org" (pretentious twats) draw. I even started to track down the dependencies of inkscape to build that but gtkmm? Yeah ok. But openoffce: Jesus H fucking-A-cunt-of-a-christ, what a load of shit that is. I can't imagine how may millions of dollars that piece of rubbish has cost in terms of developer hours and wasted customer time (aka luser `productivity') but i'm astounded by just how terrible it is. It runs very slow. Has some weird-arse modality/GUI update bullshit going on. Is a total pain to use (in terms of number of mouse clicks required to do the most trivial of operations). And above that it's just buggy as hell. I'd call up the voluminous settings "dialogue" to change a background colour and then it would decide to throw any changes i'd made if i didn't explicitly set it every time.

But it's in good company and about as shit as any `office' software has ever been since the inane marketroid idea to lock users into fucktastic `software ecosystems' was first conceived of. Fuck micro$oft and the fucking hor$e it fucking rode in on.

I was so pissed off I spent the next 4+ hours (till 5am) working on my own structured graphical editor. Ok maybe that's a bit manic and it'll probably go about as far as the last 4 times I did the same thing the last 4 times I also tried using a bit of similar software to accomplish a similar seemingly-simple goal ... but ``like seriously''?

To phrase it in the parlance of our time: what in the actual fuck?

Tagged hacking, java, rants.
Saturday, 14 May 2016, 11:03

Writing a FFT implementation for Java, in real-time

Just for something a bit different this morning I had an idea to do a record of developing software from the point of view of a "live blog". I was somewhat inspired by a recent video I saw of Media Molecules where they were editing shader routines for their outstandingly impressive new game "Dreams" on a live video stream.

Obviously I didn't quite do that but I did have a hypothesis to test and ended up with a working implementation to test that hypothesis, and recorded the details of the ups and downs as I went.

Did I get a positive or negative answer to my question?

To get the answer to that question and to get an insight into the daily life of one cranky developer, go have a read yourself.

Tagged code, hacking, java.
Thursday, 12 May 2016, 18:16

twiddle dweeb twiddle dumb

I started writing a decent post with some detail but i'll just post this plot for now.

Ok, some explanation.

I tried to scale the performance by dividing the execution time per transform by N log2 N. I then normalised using a fudge factor so the fastest N=16 is about 1.0.

My first attempt just plotted the relative ratio to jtransforms but that wasn't very useful. It did look a whole lot better though because I used gnuplot, but this time i was lazy and just used openoffice. Pretty terrible tool though, it feels as clumsy as using microsoft junk on microsoft windows 95. Although I had enough pain getting good output from gnuplot via postscript too, but nothing a few calls to netpbm couldn't fix (albeit with it's completely pointless and useless "manual" pages which just redirect you to a web page).

Well, some more on the actual information in the picture:

So it turned out and turns out that the twiddle factors are the primary performance problem and not the data cache. At least up to N=2^20. I should have known this as this was what ffts was addressing (if i recall correctly).

Whilst a single table allows for quick lookup "on paper", in reality it quickly becomes a wildly sparse lookup which murders the data cache. Even attempting to reduce its size has little benefit and too much cost; however 'tab_1' does beat 'tab_0' at the end. While fully pre-calculating the tables looks rather poor "on paper" in practice it leads to the fastest implementation and although it uses more memory it's only about twice a simple table, and around the same size as the data it is processing.

In contrast, the semi-recursive implementation only have a relatively weak bearing on the execution time. This could be due to poor tuning of course.

The rotation implementation adds an extra 18 flops to a calculation of 34 but only has a modest impact on performance so it is presumably offset by a combination of reduced address arithmetic, fewer loads, and otherwise unused flop cycles.

The code is surprisingly simple, I think? There is one very ugly routine for the 2nd to lass pass but even that is merely mandrualic-inlining and not complicated.

Well that's forward, I suppose I have to do inverse now. It's mostly just the same in reverse so the same architecture should work. I already wrote a bunch of DIT code anyway.

And i have some 2D stuff. It runs quite a bit faster than 1D for the same number of numbers (all else being equal) - in contrast to jtransforms. It's not a small amount either, it's like 30% faster. I even tried using it to implement a 1D transform - actually got it working - but even with the same memory access pattern as the 2D code it wasn't as fast as the 1D. Big bummer for a lot of effort.

It was those bloody twiddle factors again.

Update: I just realised that i made a bit of a mistake with the way i've encoded the tables for 'tab0' which has propagated from my first early attempts at writing an fft routine.

Because i started with a simple direct sine+cosine table I just appended extra items to cover the required range when i moved from radix-2 to radix-4. But all this has meant is i have a table which is 3x longer than it needs to be for W^1 and that W^2 and W^3 are sparsely located through it. So apart from adding complexity to the address calculation it leads to poor locality of reference in the inner loop.

It still drops off quite a bit after 2^16 though to just under jtransforms at 2^20.

Tagged hacking, java.
Tuesday, 10 May 2016, 13:15

radix-4 stuff

Well I did some more mucking about with the fft code. Here's some quick results for the radix-4 code.

First the runtime per-transform, in microseconds. I ran approximately 1s worth of a single transfer in a tight loop and took the last of 3 runs. All algorithms executed sequentially in the same run.

                16          64         256         1024         4096       16384        65536       262144
                                                                                                
jtransforms  0.156       1.146       6.058       27.832      135.844     511.098    3 037.140   14 802.328
dif4         0.160       0.980       5.632       27.503      138.077     681.006    3 759.005   20 044.719
dif4b        0.136       0.797       4.713       22.994      120.915     615.623    3 175.115   17 875.563
dif4b_col    0.143       0.797       4.454       21.835      117.659     593.314    3 020.144   22 341.453
dif4c        0.087       0.675       4.255       21.720      115.550     576.798    2 775.360   15 248.578
dif4bc       0.083       0.616       3.760       19.596      108.028     547.334    2 810.118   16 308.047
dif4bc_col   0.137       0.622       3.699       19.629      107.954     550.483    2 820.234   16 323.797

And the same information presented as a percentage of jtransforms' execution time with my best implementation highlighted.

              16          64          256         1024        4096        16384       65536       262144
                                                                                                
jtransforms   100.0       100.0       100.0       100.0       100.0       100.0       100.0       100.0
dif4          102.4       85.5         93.0        98.8       101.6       133.2       123.8       135.4
dif4b          86.7       69.6         77.8        82.6        89.0       120.5       104.5       120.8
dif4b_col      91.3       69.6         73.5        78.5        86.6       116.1        99.4       150.9
dif4c          55.9       58.9         70.2        78.0        85.1       112.9        91.4       103.0
dif4bc         53.3       53.8         62.1        70.4        79.5       107.1        92.5       110.2
dif4bc_col     87.7       54.3         61.1        70.5        79.5       107.7        92.9       110.3

Executed with the default java options.

$ java -version
java version "1.8.0_92"
Java(TM) SE Runtime Environment (build 1.8.0_92-b14)
Java HotSpot(TM) 64-Bit Server VM (build 25.92-b14, mixed mode)

CPU is kaveri clocked at minimum (1.7Ghz) with only a single DIMM, it is quite slow as you can see.

A summary of the algorithms follow.

jtransforms
jtransforms 2.2, complexForward() routine.
dif4
A radix-4 implemented as two nested radix-2 operations. 42 flops.
dif4b
A radix-4 implemented directly with common subexpressions explicitly encoded. 36 flops.
dif4b_col
The first approximately half passes are the same as dif4b, but the second half swap the order of the loop in order to exploit locality of reference, a longer inner loop, and re-using of twiddle factors.
difc
The first N-2 passes attempt to re-use twiddle factors across pairs of results situated at (i,N-i). The N-1 pass is hand-coded for the 4 cases which only require 3 constants for the twiddle factors.
dif4bc
The first N-2 passes are the same as dif4b. The N-1 pass is hand coded (==dif4c).
dif4bc_col
A combination of dif4bc and dif4b_col.

The final (trivial) pass is hand-coded in all cases. dif4 requireds N/2 complex twiddle factors and the rest require N/2+N/4 due to the factorisation used.

In all cases only a forward complex transform which leaves the results unordered (bit-reversed indexed) is implemented.

Thoughts

In most cases the calculations are expanded in full inside inner loops, apart from the W^0_N case. In all other cases the compiler generated slower execution if it was modularised, sometimes significantly slow (10%+). I suspect that even the radix-4 kernel is starting to exhaust available registers and scheduling slots together with the java memory model. The code also includes numerous possibly questionable and sometimes a little baffling micro-optimisations. I haven't validated the results yet apart from a test case which "looks wrong" when things go awry but i have reasonable confidence it is functioning correctly.

After I found a decent reference I did start on a radix-8 kernel but i realised why there is so little about writing software to do it; it just doesn't fit on the cpu in a high level language. Even the radix-4 kernel seems to be a very tight fit for java on this cpu.

Still playing ...

Tagged hacking, java.
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