Create lessons-learned-migrating-cuda-to-sycl-a-hep-case-study-with-root-rdataframe.md

Adds, "Lessons Learned Migrating CUDA to SYCL: A HEP Case Study with ROOT RDataFrame"

Author: codeplaymax

content/research_papers/2024/lessons-learned-migrating-cuda-to-sycl-a-hep-case-study-with-root-rdataframe.md

@@ -0,0 +1,26 @@
+---
+contributor: max
+date: '2024-01-24T08:08:10.490000+00:00'
+title: 'Lessons Learned Migrating CUDA to SYCL: A HEP Case Study with ROOT RDataFrame'
+external_url: https://arxiv.org/pdf/2310.16122
+authors:
+  - name: Jolly Chen
+  - name: Monica Dessole
+  - name: Ana Lucia Varbanescu
+tags:
+  - sycl
+  - gpu
+  - cuda
+  - heterogeneous-programming
+---
+
+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.