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Simulations to calculate a single gravitational waveform (GW) can take several weeks. Yet, thousands of such simulations are needed for the detection and interpretation of gravitational waves. Future detectors will require even more accurate waveforms than those currently used. We present here the first large scale, adaptive mesh, multi-GPU numerical relativity (NR) code together with performance analysis and benchmarking. While comparisons are difficult to make, our GPU extension of the Dendro-GR NR code achieves a 6x speedup over existing state-of-the-art codes. We achieve 800 GFlops/s on a single NVIDIA A100 GPU with an overall 2.5x speedup over a two-socket, 128-core AMD EPYC 7763 CPU node with an equivalent CPU implementation. We present detailed performance analyses, parallel scalability results, and accuracy assessments for GWs computed for mass ratios q=1,2,4. We also present strong scalability up to 8 A100s and weak scaling up to 229,376 ×86 cores on the Texas Advanced Computing Center's Frontera system. 

We study the performance of CLAIRE—a diffeomorphic multi-node, multi-GPU image-registration algorithm and software—in large-scale biomedical imaging applications with billions of voxels. At such resolutions, most existing software packages for diffeomorphic image registration are prohibitively expensive. As a result, practitioners first significantly downsample the original images and then register them using existing tools. Our main contribution is an extensive analysis of the impact of downsampling on registration performance. We study this impact by comparing full-resolution registrations obtained with CLAIRE to lower resolution registrations for synthetic and real-world imaging datasets. Our results suggest that registration at full resolution can yield a superior registration quality—but not always. For example, downsampling a synthetic image from 10243 to 2563 decreases the Dice coefficient from 92% to 79%. However, the differences are less pronounced for noisy or low contrast high resolution images. CLAIRE allows us not only to register images of clinically relevant size in a few seconds but also to register images at unprecedented resolution in reasonable time. The highest resolution considered are CLARITY images of size 2816×3016×1162. To the best of our knowledge, this is the first study on image registration quality at such resolutions. 

Python's ease of use and rich collection of numeric libraries make it an excellent choice for rapidly developing scientific applications. However, composing these libraries to take advantage of complex heterogeneous nodes is still difficult. To simplify writing multi-device code, we created Parla, a heterogeneous task-based programming framework that fully supports Python's scientific programming stack. Parla's API is based on Python decorators and allows users to wrap code in Parla tasks for parallel execution. Parla arrays enable automatic movement of data between devices. The Parla runtime handles resource-aware mapping, scheduling, and execution of tasks. Compared to other Python tasking systems, Parla is unique in its parallelization of tasks within a single process, its GPU context and resource-aware runtime, and its design around gradual adoption to provide easy migration of and integration into existing Python applications. We show that Parla can achieve performance competitive with hand-optimized code while improving ease of development.