Supported hipCIM functionality

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Supported hipCIM functionality#

2025-06-30

2 min read time

Applies to Linux

hipCIM 1.0.00 is based on cuCIM 25.04.00 and includes the following features:

  • Core image interface (cucim.core):

    • All primary image manipulation functions (read, write, and resample) are GPU-accelerated with CPU fallbacks.

    • Metadata operations (accessing dtype, dims, and shape) run on CPU only.

  • Image processing (cucim.skimage):

    • Nearly all transform operations (resize, rotate, and warp) are GPU-accelerated with CPU fallbacks.

    • Complete filter suite (Gaussian, median, and edge detectors) benefits from GPU acceleration.

    • Most morphological operations (erosion, dilation, and opening) are GPU-accelerated.

  • Segmentation:

    • Several advanced segmentation algorithms (felzenszwalb, quickshift, and active_contour) lack GPU acceleration.

    • Core segmentation operations such as watershed and SLIC are GPU-accelerated.

  • Color operations:

    • All color space conversions (rgb2gray, rgb2hsv, and rgb2lab) are GPU-accelerated.

    • Specialized operations for medical imaging, such as stain separation or combination, also benefit from GPU acceleration.

  • Whole slide imaging:

    • Patch extraction operations are GPU-accelerated.

    • Metadata operations run exclusively on the CPU.

  • Measurement functions:

    • Core measurement functions like region labeling are GPU-accelerated.

    • Some advanced functions like marching_cubes lack GPU acceleration.

Limitations#

  • Support for multilevel TIFF images is under development.

  • No Support for JPEG2K compression.

  • No GDS support

  • No Dask support

  • The following image processing operations are not supported:

    • affine, similarity, euclidean , threshold_niblack , threshold_sauvola, convex_hull_image ,corner_fast ,denoise_bilateral, denoise_wavelet, wiener, richardson_lucy, unsupervised_wiener, estimate_sigma, random_walker, felzenszwalb,slic, quickshift, watershed, active_contour and all exposure operations.

  • Registration:

    • All registration functions (optical flow and demons) are GPU-accelerated but typically lack CPU fallbacks.

  • Clara DL pipeline:

    • Data loading has partial GPU acceleration.

    • Most Clara transformations are GPU-accelerated with CPU fallbacks.

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