We present data structures and algorithms for native implementations of discrete convolution operators over Adaptive Particle Representations (APR) of images on parallel computer architectures. The APR is a content-adaptive image representation that locally adapts the sampling resolution to the image signal. It has been developed as an alternative to pixel representations for large, sparse images as they typically occur in fluorescence microscopy. It has been shown to reduce the memory and runtime costs of storing, visualizing, and processing such images. This, however, requires that image processing natively operates on APRs, without intermediately reverting to pixels. Designing efficient and scalable APR-native image processing primitives, however, is complicated by the APR's irregular memory structure. Here, we provide the algorithmic building blocks required to efficiently and natively process APR images using a wide range of algorithms that can be formulated in terms of discrete convolutions. We show that APR convolution naturally leads to scale-adaptive algorithms that efficiently parallelize on multi-core CPU and GPU architectures. We quantify the speedups in comparison to pixel-based algorithms and convolutions on evenly sampled data. We achieve pixel-equivalent throughputs of up to 1 TB/s on a single Nvidia GeForce RTX 2080 gaming GPU, requiring up to two orders of magnitude less memory than a pixel-based implementation.
翻译:在平行计算机结构图像的适应性粒子图示(APR)上,我们为本地实施离散的离散共解操作者提供数据结构和算法,用于在平行计算机结构图像的适应性图象(APR)中,我们为本地实施离散的离散操作者提供数据结构和算法。APR是一种内容适应性图像表示,使取样分辨率与图像信号相适应,这是当地将抽样分辨率与图像信号相适应的一种内容适应性图像表示法,开发该模型是为了替代在荧光显微镜中通常出现的大型、稀散图像的像素表示法,用来减少存储、视觉化和处理这类图像的记忆和运行时间成本。然而,这要求图像处理本身在PRARC上运行,而没有中间恢复到像素。但是,由于RAR的不规则记忆结构,设计高效和可缩缩放的APR-NVI图像处理原始图像处理法,这里提供高效和可升级的算法所需的算法构件。我们用Nix-Nix-Nix-NLA的缩算算算算算方法,我们通过Gix-al-al-Nix-lievalal 的缩算算算算算算算算算出了Nx1的缩成的加速的缩定的缩成的缩成的缩算法。