Workshop: 2022 International Workshop on Performance Portability and Productivity (P3HPC)
Authors: Abhishek Bagusetty (Argonne National Laboratory (ANL)); Ajay Panyala (Pacific Northwest National Laboratory (PNNL)); and Gordon Brown and Jack Kirk (Codeplay Software Ltd, UK)
Abstract: Tensor contractions form the fundamental computational operation of computational chemistry and more notably, these contractions dictate the performance of widely used coupled-cluster (CC) methods in computational chemistry. In this work, we study a single-source, cross-platform C++ abstraction layer programming model, SYCL for the application related to the computational chemistry methods such as CCSD(T) coupled-cluster formalism. An existing optimized CUDA implementation was migrated to SYCL to make use of the novel algorithm that provides tractable GPU memory needs for solving high-dimensional tensor contractions for accelerating CCSD(T). We present the cross-platform performance achieved using SYCL implementations for the non-iterative triples contribution of CCSD(T) formalism which is considered as the performance bottle neck on Nvidia A100 and AMD Instinct MI250X. Additionally, we also draw comparisons of similar performance metrics from vendor-based native programming models such as CUDA and ROCm HIP.
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