stz is a FORMER work-in-progress code for simulating bulk metallic glasses in 3D using the shear transformation zone theory of amorphous plasticity. At the moment, only linear elastic terms have been implemented. The code is written in Cython and has been parallelized through MPI via mpi4py.
At this point, it is unlikely either of us will ever look at this code again and it is not in active development.
This code requires Cython (http://cython.org/) and mpi4py (http://mpi4py.scipy.org/).
Pull the master branch for a serial implementation, parallel_MPI for the parallel implementation, or odyssey for code suited for running on Harvard University's Odyssey cluster.
Grab your copy of the code with
git clone git@github.com:thereisnoquarter/stz.git
Assuming you have Cython installed, you can cause the program to compile to C with:
python driver.py
An example test.conf file is provided for configuration options. The file accepts values for the material Lame parameters and density, the size of the grid, the number of points in each dimension, the initial and final time, and an output file. This file can be modified directly or used as a template.To run a simulation with specific parameters, call:
python driver.py your_config_file.conf
where your_config_file.conf contained the relevant parameters for your problem. A picture of this process is also displayed below.
You will also need to implement the relevant boundary conditions for the problem you wish to solve. This is done in the set_boundary_conditions() function in sim.pyx. Shear wave initial conditions have been provided as an example to demonstrate how to implement initial conditions. Note that in the parallel case, to properly load in initial conditions it is necessary to account for the fact that each processor is shifted in space. These shifts can be computed simply using cx, cy, and cz, the indices of the processor in the Cartesian communicator (described more in detail in the technical details below). Simply multiply each Cartesian index ci by the number of subdomain grid elements in that direction, n_i.
To pull the parallel code, checkout the parallel_MPI branch:
git checkout parallel_MPI
The code can be run just as in the case of the serial file by calling:
mpirun -np n python driver.py [your configuration file]
where n is the number of processors.
Bulk metallic glasses (BMGs) are an alloy whose atoms form an amorphous, random structure, in contrast to most metals. BMGs possess exceptional mechanical properties, such as high tensile strength, excellent wear resistance, and the ability to be efficiently molded and processed. They are under consideration for a wealth of technological applications, such as next-generation smartphone cases and aircraft components. However, their amorphous structure raises fundamental unanswered questions about their mechanical properties, which has hindered their usage in structural applications where they must be guaranteed not to fail.
This code simulates an elastoplastic material model for bulk metallic glasses explicitly. The equations can be found in C. H. Rycroft, Y. Sui, E. Bouchbinder, An Eulerian projection method for quasi-static elastoplasticity, J. Comp. Phys. 300 (2015) 136-166. The code implements at the moment only the equations of linear elasticity; additional terms need to be added to fully simulate hypoelastoplasticity in BMGs, and as such the current code is still a work in progress. However, these are simply mathematical details. The code generalizes what is found in the above reference to three dimensions. Parallelization is achieved by dividing the three dimensional grid into subdomains and using message passing at the boundaries to calculate derivatives. The essential computational difficulties associated with generalizing the explicit method to three dimensions and implementing the equation solver efficiently in parallel are captured in the simpler mathematical problem of linear elasticity, which is completed here. Adding in the remaining terms is a natural generalization that will be completed in coming time, and is a significant research interest of one of the authors.
Because MPI introduces a large amount of communication overhead, our parallel code is actually slower than the serial equivalent for problem sizes that fit within a single core. Based on memory limits on Odyssey, grids up to about 300 x 300 x 300 points are the largest that can be accommodated on a single core. The below graph compares parallel performance on different problem sizes with different numbers of processors to serial performance (dotted lines).
For very small problem sizes such as the 16 x 16 x 16 grid, increasing the number of processors substantially increases the communication overhead without significantly speeding up the computation (since there is so little computation to begin with), so adding more processors increases the overall running time. As problem sizes increase (e.g., for the 128 x 128 x 128 or 256 x 256 x 256 grid), we do see some speedup with greater numbers of processors, although not to the point of matching the serial solution.
Even though the parallel code does not offer a speedup relative to the serial code, it does add value by allowing larger grid sizes to be calculated. A 512 x 512 x 512 grid will not fit in the memory allocated to a single core on Odyssey, but it will fit in 8 or more cores. It is thus not possible to calculate a grid that large in serial without using hardware with significantly larger available memory, but extrapolating from running times on the smaller problems, if we had that hardware available, it would take 11.5-12 hours to calculate this entire grid. With 64 cores on Odyssey, it takes about ten hours, which is faster than we would expect the serial implementation to reach and does not require special hardware beyond the normal nodes of the cluster.
It would also be possible to run the MPI-parallelized code across nodes, which could provide more than 64 cores. However, even with the special MPI communication hardware of the Odyssey cluster, communication between nodes is slower than communication between cores of the same node, so this might only speed up the code relative to running on 64 cores for extremely large problems.
It would be possible to decrease the communication overhead associated with using MPI, as well, by using the Isend and Irecv family of functions (non-blocking communications) rather than the Send and Recv functions. Send and Recv can cause the data being sent to be serialized, substantially slowing down the code. Isend and Irecv instead require you to manually set a barrier to coordinate processes. We did not have time to explore this possibility due to the difficulty of porting our code between different Cython and MPI versions to run it on Odyssey.
README.md is what you are currently reading.
common.pyx contains simple functions for accessing and setting Field values in the grid (see implementation details below). Associated common.pxd contains definitions.
driver.py Python driver program which calls the required Cython functions to execute the simulation.
fields.pxd contains definitions for the Field struct and assocated inline update function.
parse_input.py contains code to preprocess the input .conf file.
plots.ipynb contains simple plotting utilities for visualizing the output.
sim.pyx contains the bulk of the simulation code. go is the main function, and iterates over the entire grid for each timestep, updating all values of the velocities and stresses at each grid point. set_boundary_conditions() is used to set boundary/initial conditions.The function currently implements shear wave initial conditions. For an additional example, see compressive_wave_test.pyx which implements compressive wave initial conditions. set_up_ghost_regions() instantiates the ghost regions in parallel code (as described in the implementation details below) or enforces periodic boundaries in the serial code.
update_fields.pyx contains code implementing the defining equations of hypoelastoplasticity (linear elasticity at the moment) by calculating the change in each Field at the current timestep. The associated update_fields.pxd file contains definitions.
test.conf provides an example configuration file.
Our grid is represented by a three-dimensional array of Fields and is stored as the variable grid. Field is a simple struct defined in fields.pxd, and stores all of the required variables for the STZ theory (some are unused at the moment), as well as the change in these variables from timestep to timestep. The changes are calculated using the functions found in update_fields.pyx and the values themselves are updated using the inline function update found in fields.pxd. The changes must first be calculated across the whole grid before applying updates so that derivatives of Field values at timestep n are calculated only using adjacent Field values from timestep n and not some values from timestep n+1. We use a staggered grid arrangement for numerical stability. The Field value at grid location (x, y, z) contains all of the velocity values at (x, y, z) as well as the stresses at (x+1/2, y+1/2, z+1/2). This can be understood by dividing the grid up into small cubes: we assocate the Field at (x, y, z) with the velocities at the bottom left corner of the cube and the stresses at the center of the cube. This is depicted in the diagram below.
To achieve parallelization, we divide our spatial grid into n subdomains where n is the number of processors. This is handled simply using a Cartesian Communicator as provided by MPI, where we associate the processor with index (0, 0, 0) with the bottom-left corner of our grid, (1, 0, 0) with an equally-sized subdomain whose origin is shifted by n_x where n_x is the number of x grid points in a subdomain, etc.. This generalizes naturally to arbitrary indices.
For the most part, these subdomains are disconnected, and each processor can solve the equations on their own subdomain. However, because the equations of linear elasticity (and of course hypoelastoplasticity as well, although these are not implemented at the moment) involve spatial derivatives, they are nonlocal. Hence calculating the value of spatial derivatives located at the boundary of a subdomain require field values that are stored in adjacent processors. This requirement is symmetric; for example, if processor n requires points from the left-most boundary of processor m's domain, then it necessarily follows that processor m requires points from the right-most boundary of processor n's domain. These communications can be handled naturally using the Cartesian communicator's MPI_Cart_shift function in conjunction with MPI_Sendrecv_replace.
To store these communicated values, we pad each subdomain with a "ghost region" which surrounds the physical space the processor is solving the equations within. The ghost region for each process is populated at the beginning of each timestep with the required adjacent values by the set_up_ghost_regions() function located in sim.pyx. There are three cases that must be considered.
First, if two processors share a face, the two processors must share their (opposite, in the sense as described above) faces with each other.
Please note that the cubes in the above diagram correspond to processor subdomains, and hence contain many of the Field cubes in the first schematic in this section.
Second, if two processors share an edge, these edges must be communicated in the same way.
Last, if two processors share a corner, they must share this individual value with each other.
In total, there are six faces, twelve edges, and eight corners, leading to 26 portions of the local subdomain that each processor must send out to adjacent processors, and 26 portions of adjacent subdomains that each processor must receive from nearby processors before being capable of computing spatial derivatives in their subdomain. Once a processor's ghost region has been populated, said processor is free to calculate derivatives at all physical grid locations contained in the corresponding subdomain with no consideration of edge cases. Derivatives do not need to be calculated in ghost regions as they correspond to points in adjacent processors and are calculated in those processors. Note that the ghost regions must be repopulated at every timestep in accordance with the update of values across the grid.
This code could be further parallelized by the addition of instruction-level parallelism using AVX, or multithreading (either using Python threading or Cython/OpenMP prange) within each subdomain. We did not have time to implement these additional methods of parallelism, but both could be good candidates for achieving further speedup of the code. For example, with AVX, the arithmetic operations involved in calculating the finite-difference derivatives at each point could be sped up by up to 8 times their current speed. This would not translate to 8x speedup overall, since there is significant overhead in MPI communication, but would improve the total running time of the simulation by some amount.
Cython/OpenMP threading (i.e., prange) would likely have less overhead than Python multithreading and thus might be a better candidate for within-subdomain parallelism, but this gain is not always realized in practice because the way Cython implements the OpenMP parallelism in C can confuse the compiler. This results in fewer compiler optimizations and thus slower code overall, which can offset the speedup due to parallelism. It is also unclear the degree to which individual cores on Odyssey are capable of multithreading, so there might not be much or any gain from adding an additional level of parallelism if each core can only run one thread at a time.
This code was completed by Anna Whitney and Nicholas Boffi for the final project in CS205. The code will be maintained and updated with the remaining terms defining the STZ model by Nicholas Boffi.