Integrating ORNL’s HPC and Neutron Facilities with a Performance-Portable CPU/GPU Ecosystem
By:
Hahn, Steven E; Fackler, Philip W; Godoy, William F; Maheshwari, Ketan C; Morgan, Zachary J; Savici, Andrei T; Hoffmann, Christina M; Valero Lara, Pedro ; Vetter, Jeffrey S; Ferreira Da Silva, Rafael
Page Number:
2107-2117
Book Title:
SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis
Publication Date:
March 12, 2026
Publisher Location:
The Institute of Electrical and Electronics Engineers (IEEE), New Jersey, United States of America
Conference Name:
The 6th Annual Workshop on Extreme-Scale Experiment-in-the-Loop Computing held in conjunction with SC24
Conference Location:
Atlanta, Georgia, United States of America
Conference Sponsor:
IEEE Computer Society, Technical Community on High Performance Computing, Association for Computing Machinery, ACM’s Special Interest Group on High Performance Computing
We explore the development of a performance-portable CPU/GPU ecosystem to integrate two of the US Department of Energy’s (DOE’s) largest scientific instruments, the Oak Ridge Leadership Computing facility and the Spallation Neutron Source (SNS), both of which are housed at Oak Ridge National Laboratory. We select a relevant data reduction workflow use-case to obtain the differential scattering cross-section from data collected by SNS’s CORELLI and TOPAZ instruments. We compare the current CPU-only production implementation using the Garnet Python multiprocess package based on the Mantid C++ framework against our proposed CPU/GPU implementation that uses the LLVM-based, just-in-time Julia scientific language and the JACC.jl performance-portable package. Two proxy apps were developed: (i) an app for extracting relevant Mantid kernels (MDNorm) in C++ and (ii) the Julia MiniVATES.jl miniapp. We present performance results for NVIDIA A100 and AMD MI100 GPUs and AMD EPYC 7513 and 7662 CPUs. The results provide insights for future generations of data reduction software that can embrace performance portability for an integrated research infrastructure across DOE’s experimental and computational facilities.
