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Abstract Next-generation gravitational wave detectors such as Cosmic Explorer, the Einstein Telescope, and LISA, demand highly accurate and extensive gravitational wave (GW) catalogs to faithfully extract physical parameters from observed signals. However, numerical relativity (NR) faces significant challenges in generating these catalogs at the required scale and accuracy on modern computers, as NR codes do not fully exploit modern GPU capabilities. In response, we extend NRPy, a Python-based NR code-generation framework, to develop NRPyEllipticGPU—a CUDA-optimized elliptic solver tailored for the binary black hole (BBH) initial data problem. NRPyEllipticGPU is the first GPU-enabled elliptic solver in the NR community, supporting a variety of coordinate systems and demonstrating substantial performance improvements on both consumer-grade and HPC-grade GPUs. We show that, when compared to a high-end CPU, NRPyEllipticGPU achieves on a high- end GPU up to a sixteenfold speedup in single precision while increasing double- precision performance by a factor of 2–4. This performance boost leverages the GPU’s superior parallelism and memory bandwidth to achieve a compute-bound application and enhancing the overall simulation efficiency. As NRPyEllipticGPU shares the core infrastructure common to NR codes, this work serves as a practical guide for developing full, CUDA-optimized NR codes.
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CuPBoP: Making CUDA a Portable Language
CUDA is designed specifically for NVIDIA GPUs and is not compatible with non-NVIDIA devices. Enabling CUDA execution on alternative backends could greatly benefit the hardware community by fostering a more diverse software ecosystem. To address the need for portability, our objective is to develop a framework that meets key requirements, such as extensive coverage, comprehensive end-to-end support, superior performance, and hardware scalability. Existing solutions that translate CUDA source code into other high-level languages, however, fall short of these goals. In contrast to these source-to-source approaches, we present a novel framework, CuPBoP , which treats CUDA as a portable language in its own right. Compared to two commercial source-to-source solutions, CuPBoP offers a broader coverage and superior performance for the CUDA-to-CPU migration. Additionally, we evaluate the performance of CuPBoP against manually optimized CPU programs, highlighting the differences between CPU programs derived from CUDA and those that are manually optimized. Furthermore, we demonstrate the hardware scalability of CuPBoP by showcasing its successful migration of CUDA to AMD GPUs. To promote further research in this field, we have released CuPBoP as an open-source resource.
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- Award ID(s):
- 2016701
- PAR ID:
- 10518438
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Transactions on Design Automation of Electronic Systems
- Volume:
- 29
- Issue:
- 4
- ISSN:
- 1084-4309
- Page Range / eLocation ID:
- 1 to 25
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3659949
