Create 2024-01-05-preliminary-report-initial-evaluation-of-stdpar-implementations-on-amd-gpus-for-hpc.md

Adds, "Preliminary report: Initial evaluation of StdPar implementations on AMD GPUs for HPC"

Author: codeplaymax

content/research_papers/2024/2024-01-05-preliminary-report-initial-evaluation-of-stdpar-implementations-on-amd-gpus-for-hpc.md

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+---
+contributor: max
+date: '2024-01-05T08:08:10.490000+00:00'
+title: 'Preliminary report: Initial evaluation of StdPar implementations on AMD GPUs for HPC'
+external_url: https://arxiv.org/pdf/2401.02680
+authors:
+  - name: Wei-Chen Lin
+  - name: Simon McIntosh-Smith
+  - name: Tom Deakin
+tags:
+  - sycl
+  - gpu
+  - hip
+---
+
+Recently, AMD platforms have not supported offloading C++17 PSTL (StdPar) programs to the GPU. Our previous work highlights
+how StdPar is able to achieve good performance across NVIDIA and Intel GPU platforms. In that work, we acknowledged AMD’s past 
+effort such as HCC, which unfortunately is deprecated and does not support newer hardware platforms. Recent developments by AMD, Codeplay, 
+and AdaptiveCpp (previously known as hipSYCL or OpenSYCL) have enabled multiple paths for StdPar programs to run on AMD GPUs. 
+This informal report discusses our experiences and evaluation of currently available StdPar implementations for AMD GPUs. 
+We conduct benchmarks using our suite of HPC mini-apps with ports in many heterogeneous programming models, including StdPar. 
+We then compare the performance of StdPar, using all available StdPar compilers, to contemporary heterogeneous programming models
+supported on AMD GPUs: HIP, OpenCL, Thrust, Kokkos, OpenMP, SYCL. Where appropriate, we discuss issues encountered and workarounds
+applied during our evaluation. Finally, the StdPar model discussed in this report largely depends on Unified Shared Memory (USM) performance
+and very few AMD GPUs have proper support for this feature. As such, this report demonstrates a proof-of-concept host-side userspace pagefault
+solution for models that use the HIP API. We discuss performance improvements achieved with our solution using the same set of benchmarks.