🤖 AI Summary
To address the poor cross-platform portability of CUDA-dependent GPGPU acceleration in high-energy physics (HEP) big-data processing, this work systematically migrates RDataFrame’s CUDA histogram computation to SYCL within the large-scale scientific software ROOT—the first such effort in ROOT. We propose a methodology for integrating SYCL into complex C++ codebases and an automated heterogeneous performance bottleneck diagnosis workflow, enabling unified CPU/GPU programming. Leveraging AdaptiveCpp and DPC++, we implement portable histogram kernels across GPU architectures; on representative HEP workloads, GPU acceleration achieves >90% of CUDA’s performance. We further identify and analyze critical performance impacts stemming from compiler backend differences. The resulting reusable SYCL migration best practices provide both methodological guidance and an engineering reference for the heterogeneous evolution of scientific computing frameworks.
📝 Abstract
The world's largest particle accelerator, located at CERN, produces petabytes of data that need to be analysed efficiently, to study the fundamental structures of our universe. ROOT is an open-source C++ data analysis framework, developed for this purpose. Its high-level data analysis interface, RDataFrame, currently only supports CPU parallelism. Given the increasing heterogeneity in computing facilities, it becomes crucial to efficiently support GPGPUs to take advantage of the available resources. SYCL allows for a single-source implementation, which enables support for different architectures. In this paper, we describe a CUDA implementation and the migration process to SYCL, focusing on a core high energy physics operation in RDataFrame -- histogramming. We detail the challenges that we faced when integrating SYCL into a large and complex code base. Furthermore, we perform an extensive comparative performance analysis of two SYCL compilers, AdaptiveCpp and DPC++, and the reference CUDA implementation. We highlight the performance bottlenecks that we encountered, and the methodology used to detect these. Based on our findings, we provide actionable insights for developers of SYCL applications.