🎉 News!! Our paper, "PrestoZL: A GPU-Accelerated High-Throughput Jerk Search Toolkit for Binary Pulsars", has been published in The Astrophysical Journal Supplement Series (APJS)! We are grateful to Prof. Scott Ransom for his invaluable advice and insightful feedback, which greatly contributed to the improvement of this work. You can find the paper here.

PrestoZL is a highly optimized, GPU-based pulsar search and analysis software developed by the team at the Astronomical Computing Research Center of Zhejiang Lab. It was developped based on the Scott Ransom's PRESTO v4.0. The key difference between them lies in the GPU optimization of the most time-consuming "Jerk Search" module, which has been tailored for GPU parallel processing pipelines. The search speed of the GPU-based Jerk Search in PrestoZL can be accelerated by several tens of times faster than CPU-based Jerk Search in PRESTO, while maintaining the search results that are fully identical to PRESTO C, including the number of detected pulsars, as well as the output search parameters and signal-to-noise ratio (SNR) values.

PrestoZL is particularly suitable for searching pulsars using large (zmax, wmax) parameters with long observation times. Table below shows the end-to-end processing times of different methods when processing a 1816.77-second observation in M5 dataset (NGC 5904) using a single process with zmax=200 and wmax=300. PrestoZL and PrestoZL-pipeline(a pipelined version) achieves 51.19 to 56.38 times speedup over PRESTO C respectively.

Test Environment: one A40 40G GPU，20 core CPU.

We also opensource a GPU-accelerated version of De-dispersion in prepsubband_cu.c with the performance far exceeds the CPU-based prepsubband.c. It can be 100 times faster than the CPU based de-dispersion version, and the processing results are fully consistent with the code in PRESTO's prepsubband.c.

There are three ways to set up the PrestoZL running environment.

We have prepared a pre-built Docker image: zjlabastro/prestozl:v1.0，it is an environment-ready image to run PrestoZL. Ensure you have Docker and nvidia-container-toolkit installed on your system, you can follow the instructions here. Then the image can be fetched as follow:

Download pre-built docker image from dockerhub.

docker pull zjlabastro/prestozl:v1.0

*If you have trouble in visiting dockerhub, download the Image from BaiduNetdisk, and follow readme.txt.

Run a Containter from the Image. -v mounts directories from host file system into the docker container for data share.

docker run -itd --name=prestozl_latest --gpus all --network=host -v /path/to/your/host/dir:/home/soft/prestodata zjlabastro/prestozl:v1.0 /bin/bash

Get into the Container and run PrestoZL.

docker exec -it prestozl_latest /bin/bash

If you have PRESTO v4.0 environment on your machine, and really DO NOT want to use Docker. Try the solution here.

We have provided Dockerfile to build the PrestoZL Image by yourself. Follow the instruction here.

accelsearch_cu.c serves as the entry point for the PrestoZL's Jerk Search. The command has been expanded from the PRESTO to include a batchsize parameter, which batching the calculation of several rstep in each while iteration. This parameter doesn't need to be explicitly set, its default value is batchsize=8. The lower the batchsize is ,the less GPU memory will be used. If your GPU memory is limited or use larger search parameters, we recommend to set the batchsize=1 first and prioritizing an increase in the number of processes. Other usages remain consistent with the PRRESTO . Here's an example:

accelsearch_cu -zmax 200 -wmax 500 -sigma 5.0 -numharm 16 -batchsize 1 yourFFTfile.fft

To use multiple processes(set by -P) to process many FFT files in /yourPathtoFFTfiles concurrently:

ls /yourPathtoFFTfiles/*.fft |  xargs -P 8 -n 1 accelsearch_cu -zmax 200 -wmax 500 -sigma 3.0 -numharm 16 -batchsize 1

To run PrestoZL-pipeline, you can use the python script at bin/accelsearch_pipeline_cu.py. --pool_size refers to the number of concurrent running process in a GPU, each process is an FFT file processing pipeline, --directory refers to the directory of the input fft files, --batchsize is as the same meaning with PrestoZL. Here's an example:

accelsearch_pipeline_cu.py --pool_size 8 --directory /yourPathtoFFTfiles --zmax 200 --wmax 500 --sigma 3.0 --numharm 16 --batchsize 1

To run the GPU-accelerated version of de-dispersion, you can use the command prepsubband_cu, other arguments are the same with the prepsubband used in PRESTO. Here's an example:

prepsubband_cu -nobary -numout 262144 -nsub 3280 -lodm 59.4 -dmstep 0.1 -numdms 564 -downsamp 1 -mask your_rfifind.mask -o /path/to/outputdir/withprefix /yourpathtoFITSfiles/my.fits

The usage of other commands are the same with PRESTO.

The following individuals contributed to this project, (listed in alphabetical order): Chen Huaxi, Mao Kuang, Pan Qiuhong, Tang Zhaorong, Tang Xuefei, Wang Qi, Wang Pei

If you have any question, be free to contact me: [email protected]

If you use the code in your experiment, please cite PrestoZL's paper, thank you.

@article{Mao_2025,
doi = {10.3847/1538-4365/adf4e5},
url = {https://dx.doi.org/10.3847/1538-4365/adf4e5},
year = {2025},
month = {sep},
publisher = {The American Astronomical Society},
volume = {280},
number = {1},
pages = {36},
author = {Mao, Kuang and Tang, Zhaorong and Pan, Qiuhong and Wang, Pei and Chen, Huaxi and Ransom, Scott M. and Li, Di and Tang, Xuefei and Wang, Qi and Feng, Yi and Chen, Lei and Quan, Donghui and Ren, Zujie},
title = {PrestoZL: A GPU-accelerated High-throughput Jerk Search Toolkit for Binary Pulsars},
journal = {The Astrophysical Journal Supplement Series}
}