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Parallel Computing Mandelbrots

Posted: July 10, 2016
Updated: July 7, 2017


This page was originally set up to post an x86_64 SSE vector unit implementation of a Mandelbrot generator. Since then I implemented it on a 16 core Epiphany chip on a Parallella board and a Parallax Propeller FLiP module. I plan to add some more chip to this page.

Related Projects

Mandelbrots: mandel server, python Mandelbrot, Mandelbrots SIMD, j2me Mandelbrot
ASM: SSE Image Processing, JIT Compiler, Generating Java Byte Code, ARM Assembly, Easy Match, Mandelbots With SIMD

Intel x86_64 SSE

Since Intel x86 has an SIMD (single instruction, multiple data) instruction set called SSE, it's possible to load up a 128 bit register with either four 32 bit integers or four 32 bit floats (or some other configurations of data) and run math operations on all 4 elements in the register in parallel. It is possible to do Mandelbrots using integer operations, which might be be faster than floating point, but I decided to do this with floating point anyway.

I started writing the code in standard C and later converted the code to pure assembly using SSE instructions. Most of the code is pretty straight forward except for 1 thing. When a pixel has gone out of range of the Mandelbrot set (when the sqrt(Z^2) > 2) then that pixel needs to stop counting down. I did this by having a count vector that starts out as the four integers [ 1, 1, 1, 1 ] stored in the register xmm2. Using the cmpleps instruction, if any element of the vector is bigger than 4 (the sqrt part can be avoided here) then the count element for that pixel is set to 0. If all counts are set 0 the main loop is aborted. The good part of this is pixels that are near each other are probably going to abort at the same time or around the same time.

The performance difference is pretty decent. The second picture below rendered on an AMD FX-8320 CPU:

Standard C: 0.35 seconds
SSE Assembly: 0.13 seconds

Source Code: git clone

Parallella (Epiphany III)

A couple years ago I added Epiphany support to naken_asm but I never had a chance to test it. I recently aquired a Parallella board so I decided to try Mandelbrots with it.

The source code is included in the samples / epiphany directory in the naken_asm repository. The mandelbrot.asm file contains the Epiphany assembly code and the test_mandebrot.c has the host CPU (Zynq) source code. The test_mandelbrot.c program passes a real / imaginary coordinate to a non-busy core, signals the core with a USER interrupt, and waits for the core to set a "done" flag in the external shared memory area. Each core generates an entire row of the image before signaling it's ready for another data set to work on.

Update June 6, 2017: I added enough Epiphany / Parallella support to Java Grinder so that I could write the Mandelbrot code in Java and compile the byte code into Epiphany. It still uses the test_mandelbrot.c but uses in the samples/epiphany directory to run on the Epiphany cores. Looks like the Java code is a more than 2x slower. It's possible I could optimize the output to run quite a bit faster.. but for now this will do. Java Grinder is still missing support (at the time of this writing) for arrays and stuff.. but it has support for reading / writing the shared memory.

The second picture below rendered on a Parallella Microserver at the following speeds:

1 Core (assembly): 5.2 seconds
16 Cores (assembly): 0.32 seconds
16 Cores (Java): 0.78 seconds

Some intersting things I found here: The Parallella's SDK has functions for accessing each core's local memory directly. I found that if I did this while the core was running, the core would sometimes go nuts. As soon as I moved to using all external RAM, it stopped doing that. I think using the local memory probably would get better performance overall, but I just couldn't get it working right. What's odd is the local memory of each core is divided into 4 parts, so I was keeping the code in 1 segment, the signaling (coordinates and done flag) in another segment, and the actual image in another. I tried moving the signalling to external memory keeping the image in faster local RAM, but it still crashed the core. I'm probably doing something wrong?

The Mandelbrot code is written in pure assembly using the floating point unit of the Epiphany. I was able to take advantage of the fused multiply / add and fused multiply / subtract instructions. To keep the code more dense (which should help it run faster), I made the most use of registers r0 to r7 in the parts of the code that does the most calculations. Those registers can fit into instructions that are only 16 bit in size.

The Epiphany is still slower than the x86_64 SSE code shown above, but then again this chip is only running around 667MHz where the x86_64 I used was 3.5GHz. Overall this is a really neat little board and I can't wait to do some more stuff with it.

Parallax Propeller FLiP

I ended up getting a Propeller FLiP module along with a 96x64 OLED display from Parallax's website so I decided to test some Mandelbrots on this platform. First two things to note about the FLiP: it doesn't have hardware SPI so it takes a bunch of code to write to the OLED display and it doesn't have a hardware multiply instruction so I had to write a small routine in software. The SPIN software SPI is quite slow (as can be seen in the video when the screen fills with green). The Java code can render a Mandelbrot and draw it at the same speed SPIN can write green all over the display.

The video above (as captioned) shows 3 different sets of software. The first program is a combination of SPIN (sets up the LCD and fills the screen with green) and then loads a Mandelbrot program that was written in Java and compiled further to native Propeller using Java Grinder. All the code is in the samples directory of the Java Grinder git repository. As a side comment, it doesn't show up very well in the video, but that little LCD is one of the nicest looking displays I've seen: 96 x 64 Color OLED Display Module.

The second program is written in pure assembly. Again the code is started up by a SPIN program and then control is transfered to the assembly program. The source code for this program is in the naken_asm repository.

The third program uses all 8 cores of the Propeller and it definitely shows. Core 0 runs some SPIN code which arbitrates what the other 7 cores do. Core 1 is just there to control the LCD. When Core 0's SPIN code signals it, it loads the image out of shared memory and writes it to the OLED display. When Core 0 wants to render a Mandelbrot, it signals Core 2 to 7 the real and imaginary coordinates of a single line in the Mandelbrot set. Each core writes to the shared memory segment that the LCD uses to write to the OLED display. The SPIN code constantly looks for a free core to give coordinates to until there are no more lines to render and then just simply waits until they finish and tells Core 1 to draw.


mandelbrot computed with the x86 SIMD vector unit

A standard Mandelbrot computed using SSE assembly code.

mandelbrot computed with the x86 SIMD vector unit

A Mandelbrot computed using SSE assembly code zoomed in.

Parallella used to compute mandelbrots

Here is the Parallella board with a cheap little fan on top of it. The chip seems to run around 54C or so with the fan. Without it, it overheats while even trying to compile naken_asm.

Copyright 1997-2017 - Michael Kohn