Before I begin I would like to quickly introduce myself — my name is Sameer Puri and I am a high school student working in an internship under Dr. Heiko Enderling through the HIP IMO program at Moffitt Cancer Center.
Over the past 5 weeks, I have been developing an ABM in Java for my research. My principal goal in developing this ABM was to quickly generate tumors with 1 million cells. (And prove that Java can perform better in some cases…!)
When I started, I was working with an ABM written by @jpoleszczuk and Heiko in C++ (available here). Having a strong background in Java and little past experience with C++, it was difficult for me to understand. Structs, vectors, memcpy, and other features of C++ are not present in Java. Instead of trying to learn C++, I converted the code to Java, my language of expertise.
I decided to optimize the code before making any extensions for my research. In Java, finding slow-down points is simple, since an array of profiling tools are available to find which part of the code is slowing down the ABM. I utilized the web-based profiler WarmRoast, developed by sk89q (on GitHub here).
- One of the biggest time hogs was the Random class which was implemented in the 1990s, and after 20 years, much more efficient pseudo-random number generators have emerged. I replaced it with the XorShift generator, developed by George Marsaglia. The XorShift is theoretically 3 times faster than the default generator. However, the speed-up was far more significant — the call for a random double was so fast that it disappeared from the WarmRoast profiler’s results.
- The returnEmptyPlace method was optimized with a strategy known as pregeneration, or exchanging memory for lower CPU time. The returnEmptyPlace method looks for an empty spot around a cell in the ABM, shuffling a list of 8 directions each time. Instead, I pregenerated all 40,320 permutations of the 8 direction vectors surrounding a lattice square. Now, the returnEmptyPlace method simply uses the speedy XorShift generator to select a random permutation of the directions list to use.
- I also optimized the shuffling of the list of cells in the main simulation loop. I created a new class called SPCollections which uses the XorRandom generator for shuffling. There was a large speed-up here as well.
- Using NetBeans’ built-in hashcode generator, I added hashcodes for the Cell and Indx classes, which will speed up the usage of HashMaps or other hash-based structures.
- Since memory was not a serious concern for me, I made one optimization. The original model in C++ used the char primitive type for its lattice. I turned the lattice into a byte lattice, which has half the memory footprint (chars are 16 bits, bytes are 8 bits).
- For my research, I had to grow 10 tumors in silico with one million cells each. To speed up simulation I utilized parallel computing with an ExecutorService. With my ABM, you can specify the number of “trials” you want to run. For each trial run, a simulator instance is created and run on a separate thread. Most CPUs today have at least 4 cores. Code in most programming languages tend to make use of only one core unless programmers specify methods that allow for parallel execution of code. By creating a separate thread for each simulation, the threads can operate on separate cores and maximize CPU usage!
- To universalize my data, I saved it utilizing Java serialization. With serialization, Java can output objects to files, which can be loaded later on without needing to code a loader. Others will be able to easily load data generated using my ABM and run their own operations on it.
- I coded an image generator to automatically generate tumor images. Once a simulation is finished, the image generator takes the serialized output of the simulation and generates three types of PNG images for each dumped instance: proliferation, quiescence, and nutrition. It also creates an animated GIF to show the growth of the tumor over time. Each image has the time step (1 step = 1 hour), number of stem cells and number of regular cancer cells at the upper left corner.
Proliferation during Development
Nutrition during Development (on rim)
Quiescence during Development
You can download my ABM here