cuSignal

The RAPIDS cuSignal project leverages CuPy, Numba, and the RAPIDS ecosystem for GPU accelerated signal processing. In some cases, cuSignal is a direct port of Scipy Signal to leverage GPU compute resources via CuPy but also contains Numba CUDA and Raw CuPy CUDA kernels for additional speedups for selected functions. cuSignal achieves its best gains on large signals and compute intensive functions but stresses online processing with zero-copy memory (pinned, mapped) between CPU and GPU.

NOTE: For the latest stable README.md ensure you are on the latest branch.

Quick Start

cuSignal has an API that mimics SciPy Signal. In depth functionality is displayed in the notebooks section of the repo, but let's examine the workflow for Polyphase Resampling under multiple scenarios:

Scipy Signal (CPU)

import numpy as np
from scipy import signal

start = 0
stop = 10
num_samps = int(1e8)
resample_up = 2
resample_down = 3

cx = np.linspace(start, stop, num_samps, endpoint=False) 
cy = np.cos(-cx**2/6.0)

%%timeit
cf = signal.resample_poly(cy, resample_up, resample_down, window=('kaiser', 0.5))

This code executes on 2x Xeon E5-2600 in 2.36 sec.

cuSignal with Data Generated on the GPU with CuPy

import cupy as cp
import cusignal

start = 0
stop = 10
num_samps = int(1e8)
resample_up = 2
resample_down = 3

gx = cp.linspace(start, stop, num_samps, endpoint=False) 
gy = cp.cos(-gx**2/6.0)

%%timeit
gf = cusignal.resample_poly(gy, resample_up, resample_down, window=('kaiser', 0.5))

This code executes on an NVIDIA V100 in 13.8 ms, a 170x increase over SciPy Signal

cuSignal with Data Generated on the CPU with Mapped, Pinned (zero-copy) Memory

import cupy as cp
import numpy as np
import cusignal

start = 0
stop = 10
num_samps = int(1e8)
resample_up = 2
resample_down = 3

# Generate Data on CPU
cx = np.linspace(start, stop, num_samps, endpoint=False) 
cy = np.cos(-cx**2/6.0)

# Create shared memory between CPU and GPU and load with CPU signal (cy)
gpu_signal = cusignal.get_shared_mem(num_samps, dtype=np.float64)

%%time
# Move data to GPU/CPU shared buffer and run polyphase resampler
gpu_signal[:] = cy
gf = cusignal.resample_poly(gpu_signal, resample_up, resample_down, window=('kaiser', 0.5))

This code executes on an NVIDIA V100 in 174 ms.

cuSignal with Data Generated on the CPU and Copied to GPU [AVOID THIS FOR ONLINE SIGNAL PROCESSING]

import cupy as cp
import numpy as np
import cusignal

start = 0
stop = 10
num_samps = int(1e8)
resample_up = 2
resample_down = 3

# Generate Data on CPU
cx = np.linspace(start, stop, num_samps, endpoint=False) 
cy = np.cos(-cx**2/6.0)

%%time
gf = cusignal.resample_poly(cp.asarray(cy), resample_up, resample_down, window=('kaiser', 0.5))

This code executes on an NVIDIA V100 in 637 ms.

Installation

Conda, Linux OS

cuSignal can be installed with conda (Miniconda, or the full Anaconda distribution) from the rapidsai channel). If you're using a Jetson GPU, please follow the build instructions below

For cusignal version == 0.13:

# For CUDA 10.0
conda install -c rapidsai -c nvidia -c conda-forge \
    -c defaults cusignal=0.13 python=3.6 cudatoolkit=10.0

# or, for CUDA 10.1.2
conda install -c rapidsai -c nvidia -c numba -c conda-forge \
    cudf=0.13 python=3.6 cudatoolkit=10.1

# or, for CUDA 10.2
conda install -c rapidsai -c nvidia -c numba -c conda-forge \
    cudf=0.13 python=3.6 cudatoolkit=10.2

For the nightly verison of cusignal:

# For CUDA 10.0
conda install -c rapidsai-nightly -c nvidia -c conda-forge \
    -c defaults cusignal=0.13 python=3.6 cudatoolkit=10.0

# or, for CUDA 10.1.2
conda install -c rapidsai-nightly -c nvidia -c numba -c conda-forge \
    cudf=0.13 python=3.6 cudatoolkit=10.1

# or, for CUDA 10.2
conda install -c rapidsai-nightly -c nvidia -c numba -c conda-forge \
    cudf=0.13 python=3.6 cudatoolkit=10.2

cuSignal has been tested and confirmed to work with Python 3.6, 3.7, and 3.8.

See the Get RAPIDS version picker for more OS and version info.

Conda - Jetson Nano, TK1, TX2, Xavier, Linux OS

While there are many versions of Anaconda for AArch64 platforms, cuSignal has been tested and supports conda4aarch64. Conda4aarch64 is also described in the Numba aarch64 installation instructions. Further, it's assumed that your Jetson device is running a current edition of JetPack and contains the CUDA Toolkit.

1. Clone the repository

   # Set the localtion to cuSignal in an environment variable CUSIGNAL_HOME
   export CUSIGNAL_HOME=$(pwd)/cusignal
   
   # Download the cuSignal repo
   git clone https://github.com/rapidsai/cusignal.git $CUSIGNAL_HOME

2. Install conda4aarch64 and create the cuSignal conda environment:

   cd $CUSIGNAL_HOME
   conda env create -f conda/environments/cusignal_jetson_base.yml

3. Activate conda environment

   conda activate cusignal

4. Install cuSignal module

   cd $CUSIGNAL_HOME/python
   python setup.py install

   or

   cd $CUSIGNAL_HOME
   ./build.sh  # install cuSignal to $PREFIX if set, otherwise $CONDA_PREFIX
               # run ./build.sh -h to print the supported command line options.

5. Once installed, periodically update environment

   cd $CUSIGNAL_HOME
   conda env update -f conda/environments/cusignal_jetson_base.yml

6. Also, confirm unit testing via PyTest

   cd $CUSIGNAL_HOME/python
   pytest -v  # for verbose mode
   pytest -v -k <function name>  # for more select testing

Source, Linux OS

1. Clone the repository

   # Set the location to cuSignal in an environment variable CUSIGNAL_HOME
   export CUSIGNAL_HOME=$(pwd)/cusignal
   
   # Download the cuSignal repo
   git clone https://github.com/rapidsai/cusignal.git $CUSIGNAL_HOME

2. Download and install Anaconda or Miniconda then create the cuSignal conda environment:

   Base environment (core dependencies for cuSignal)

   cd $CUSIGNAL_HOME
   conda env create -f conda/environments/cusignal_base.yml

   Full environment (including RAPIDS's cuDF, cuML, cuGraph, and PyTorch)

   cd $CUSIGNAL_HOME
   conda env create -f conda/environments/cusignal_full.yml

3. Activate conda environment

   conda activate cusignal

4. Install cuSignal module

   cd $CUSIGNAL_HOME/python
   python setup.py install

   or

   cd $CUSIGNAL_HOME
   ./build.sh  # install cuSignal to $PREFIX if set, otherwise $CONDA_PREFIX
               # run ./build.sh -h to print the supported command line options.

5. Once installed, periodically update environment

   cd $CUSIGNAL_HOME
   conda env update -f conda/environments/cusignal_base.yml

6. Also, confirm unit testing via PyTest

   cd $CUSIGNAL_HOME/python
   pytest -v  # for verbose mode
   pytest -v -k <function name>  # for more select testing

Source, Windows OS [Expiremental]

1. Download and install Andaconda for Windows. In an Anaconda Prompt, navigate to your checkout of cuSignal.

2. Create cuSignal conda environment

   conda create --name cusignal

3. Activate conda environment

   conda activate cusignal

4. Install cuSignal Core Dependencies

   conda install numpy numba scipy cudatoolkit pip
   pip install cupy-cudaXXX
   

   Where XXX is the version of the CUDA toolkit you have installed. 10.1, for example is cupy-cuda101. See the CuPy Documentation for information on getting Windows wheels for other versions of CUDA.

5. Install cuSignal module

   cd python
   python setup.py install
   

6. [Optional] Run tests In the cuSignal top level directory:

   pip install pytest
   pytest
   

Docker - All RAPIDS Libraries, including cuSignal

For cusignal version == 0.13:

# For CUDA 10.0
docker pull rapidsai/rapidsai:cuda10.0-runtime-ubuntu18.04
docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
    rapidsai/rapidsai:cuda10.0-runtime-ubuntu18.04

For the nightly version of cusignal

docker pull rapidsai/rapidsai-nightly:cuda10.0-runtime-ubuntu18.04
docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
    rapidsai/rapidsai-nightly:cuda10.0-runtime-ubuntu18.04

Please see the RAPIDS Release Selector for more information on supported Python, Linux, and CUDA versions.

Optional Dependencies

• nvidia-docker if using Docker
• RTL-SDR or other SDR Driver/Packaging. Find more information and follow the instructions for setup here. We have also tested cuSignal integration with SoapySDR

Contributing Guide

Review the CONTRIBUTING.md file for information on how to contribute code and issues to the project.

GTC DC Slides and Presentation

You can learn more about the cuSignal stack and motivations by viewing these GTC DC 2019 slides, located here. The recording of this talk can be found at GTC On Demand