Towards tangent-linear GPU programs using OpenACC

Abstract

Recently, Graphics Processing Units(GPUs) have emerged as a very promisingly powerful resource in scientific computing. Algorithmic Differentiation is a technique to numerically evaluate first and higher derivatives of a function specified by a computer program efficiently up to machine precision. Derivative programs which are used to compute derivatives of functions are so-called tangent-linear program and adjoint program. This paper aims to offload any particular independent loop in tangent-linear program to GPUs. The proposed technique is OpenACC APIs for annotating an independent loop to be executed in parallel on GPUs. Our case study for OpenACC tangent-linear code shows an enormous speedup. OpenACC shows its simplicity of accelerating tangent-linear code by hiding the data movement between CPU and GPU memory.