FastSAR

FastSAR forms synthetic aperture radar (SAR) images on x86 CPUs, Nvidia GPUs and Google Cloud TPUs from Python. It reads frequency-domain CPHD files, forms spotlight, stripmap and sliding spotlight images by factorized backprojection, and provides autofocus, interferometry, geolocation, map GeoTIFFs and SICD output.

Panama Canal, Pacific entrance (Umbra open data, 2023-07-18): 12,207 by 8,808 pixels in 2.9 s on an Nvidia L4 (warm former, transfers included; float32, -59.9 dB against the float64 reference of precision.md), downsampled. A c4d-highmem-16 CPU instance (16 vCPUs, 8 cores) takes 11.3 s. ISCE3, the fastest open-source code that forms this collection, would take an estimated 4.8 h on that CPU and 27 min on the L4.

Cost per 1000 Panama images (October 2026 prices) against error over the lock, port and ship regions, relative to the float64 reference (performance, comparison).

Install

Linux on x86-64 or 64-bit ARM and macOS on Apple silicon, Python 3.10 to 3.13. The CPU kernels compile on first use: with g++ and OpenMP on Linux (Debian and Ubuntu: apt install g++), with Apple's clang on macOS (the command line tools, xcode-select --install, which also serve the Metal backend of ExactFormer on Apple GPUs); the cuda extra needs an Nvidia driver for CUDA 12 and the tpu extra a Cloud TPU VM.

pip install fastsar                  # CPU (and JAX on whatever device it has)
pip install "fastsar[io]"            # io: sarpy for CPHD and SICD
pip install "fastsar[cuda]"          # cuda: CuPy for Nvidia GPUs
pip install "fastsar[tpu]"           # tpu: jax[tpu] for Cloud TPUs
pip install "fastsar[geo]"           # geo: rasterio for GeoTIFFs and DEMs
pip install "fastsar[raw]"           # raw: h5py for the NISAR raw reader
pip install "fastsar[cuda,io,geo]"   # extras combine

From a clone: pip install -e ".[cuda,io]" or uv sync. The CUDA tensor-core kernels (float16, and float32 on compute capability 8.0 and later) need nvcc from the CUDA toolkit; without it float32 runs on the CUDA cores, more slowly. On the CPU set OMP_NUM_THREADS=<physical cores> OMP_PLACES=cores OMP_PROC_BIND=close. Importing fastsar sets XLA_PYTHON_CLIENT_PREALLOCATE=false unless it is already set, so that JAX does not reserve most of a GPU's memory before the CUDA kernels run (performance.md).

Quickstart

The chain below reads a CPHD file and writes a GeoTIFF and a SICD, so it needs pip install "fastsar[io,geo]".

import fastsar
from fastsar import products

out = fastsar.form_cphd('scene_CPHD.cphd')        # mode, grid and window from the file
img = out['image']                                # complex64 [nx, ny] on a ground plane at the scene height
lat, lon, h = products.geolocate(out, 100, 200)   # pixel (100, 200) on the ground
products.write_geotiff('amp.tif', **products.geocode_image(out))   # amplitude on a north-up UTM grid
products.write_sicd('scene.nitf', out)            # complex image with its geometry

examples/chain.py runs this chain from the command line (--simulate needs no data); form_umbra.py and form_capella_stripmap.py form vendor grids.

Supported data, modes and devices

Polar format (algorithm='pfa') runs as a JAX program on any device.

Documentation

docs/README.md lists every page; start with the processing chain. Measurement records are in sar-accel-study. To cite FastSAR, use CITATION.cff.

Contributing and license

CONTRIBUTING.md explains how to run the tests (GitHub Actions runs the CPU suite; the CUDA and TPU tests run on those machines before a release). MIT license (LICENSE).