cuTWED vv0.3.0.

A linear memory CUDA adaptation of the Time Warp Edit Distance algorithm.

About

This is a novel parallel implementation of the Time Warp Edit Distance. The original algorithm was described by Marteau in: Time Warp Edit Distance with Stiffness Adjustment for Time Series Matching (2009). A variant of the code from that paper is included in reference_implementation, and was used to compare results. There is also a wiki page.

The original TWED algorithm is O(n^2) in time and space. This algorithm is roughly O(n * n/p) in time for p (CUDA) cores. Most importantly, cuTWED is linear in memory, requiring storage for roughly 6*nA + 6*nB elements.

In the process of understanding the dynamic program data dependencies in order to parallelize towards the CUDA architecture, I devised a method with improved memory access patterns and the ability to massively parallelize over large problems. This is accomplished by a procession of a three diagonal band moving across the dynamic program matrix in nA+nB-1 steps. No O(n^2) matrix is required anywhere. Note, this is not an approximation method. The full and complete TWED dynamic program computation occurs with linear storage.

The code is provided in a library that has methods for double and float precision. It admits input time series in R^N as arrays of N-dimensional arrays in C-order (time is the slow moving axis).

For typical problems computable by the original TWED implementation, utilizing cuTWED and thousands of CUDA cores achieves great speedups. More so, the linear memory footprint allows for the computation of previously intractable problems. Large problems, large systems of inputs can be computed much more effectively now.

Some speed comparisons and a more formal explanation will follow.

Getting Started

Requirements

For the CUDA code you will need NVCC, a CUDA capable card and CMake. Otherwise, the CUDA code has no dependencies.

For the Python binding pip manages the specific depends and installation of the Python interface after you have built the main CUDA C library. Generally requires numpy, pycuda, and cffi. I recommend you use virtualenv or conda to manage python.

Building

Building has two stages. First the CUDA C library is built. It can be permanently installed to your system, or just append the path to libcuTWED.so onto your LD_LIBRARY_PATH. That can be either temorarily or in your .bashrc etc.

If you would like the python bindings, I have (with great pain) formed a pip installable package for the bindings.

Building the core CUDA C library

Note you may customize the call to cmake below with flags like -DCMAKE_INSTALL_PREFIX=/opt/, or other flags you might require.

# git a copy of the source code
git clone https://github.com/garrettwrong/cuTWED
cd cuTWED

# setup a build area
mkdir build
cd build
cmake ../ # configures/generates makefiles

# make
make -j  # builds the software

This should create several files in the build directory including libcuTWED.so, *.h headers, and some other stuff. To install to your system (may require sudo):

make install

If you just want to temporarily have the library available on your linux machine you can just use the LD path. This makes no changes to your system outside the repo and this shell process.

export LD_LIBRARY_PATH=$PWD:$LD_LIBRARY_PATH

Python

Once you have the CUDA C library readied, we can use pip for the python bindings.

pip install cuTWED

If you are a developer you might prefer pip use your local checkout instead.

pip install -e .

Using cuTWED in other programs

C/C++

In C/C++ you should be able to include "cuTWED.h" and link with the shared library libcuTWED.so. This is what I do in test.x. The public methods are extern C mangled and should be usable from both C and C++ without issue.

Float (32bit) versions of all the public methods are included in the shared library. They simply have an f appended, for example, twedf is the float version of twed. You may choose which one is suitable for your application. I use floats in testf.x.

There are currently two main ways to invoke the cuTWED alogorithm, twed and twed_dev. First twed is the most common way, where you pass C arrays on the host, and the library manages device memory and transfers for you.

Alternatively, if you are already managing GPU memory, you may use twed_dev which expects pointers to memory that resides on the gpu. I have also provided malloc, copy, and free helpers in case it makes sense to reuse memory. See cuTWED.h. All logic and size checks for such advanced cases are expected to be owned by the user.

Future plans include a mode for streaming batched mode optimized for large systems.

Python

from cuTWED import twed

For Python I have included basic pip installable python bindings. I use it in example/test.py. If you are curious, these are implemented by a cffi backend which parses the C header. which is built for you by setuptools. The main python interface is in cuTWED.py. This requires that you have built the library, and have it available in your LD_LIBRARY_PATH.

I have also wrapped up the GPU only memory methods in python, using PyCUDA gpuarrays. Examples in double and single precision are in example/test_dev.py.

from cuTWED import twed_dev

License

GPLv3

Copyright 2020 Garrett Wright, Gestalt Group LLC