Starlight: A kernel optimizer for GPU processing
Alberto Zeni, Emanuele Del Sozzo, Eleonora D’Arnese, Davide Conficconi, Marco Domenico Santambrogio · Journal of Parallel and Distributed Computing · 2023
Over the past few years, GPUs have found widespread adoption in many scientific domains, offering notable performance and energy efficiency advantages compared to CPUs. However, optimizing GPU high-performance kernels poses challenges given the complexities of GPU architectures and programming models. Moreover, current GPU development tools provide few high-level suggestions and overlook the underlying hardware. Here we present Starlight, an open-source, highly flexible tool for enhancing GPU kernel analysis and optimization. Starlight autonomously describes Roofline Models, examines performance metrics, and correlates these insights with GPU architectural bottlenecks. Additionally, Starlight predicts potential performance enhancements before altering the source code. We demonstrate its efficacy by applying it to literature genomics and physics applications, attaining speedups from 1.1× to 2.5× over state-of-the-art baselines. Furthermore, Starlight supports the development of new GPU kernels, which we exemplify through an image processing application, showing speedups of 12.7× and 140× when compared against state-of-the-art FPGA- and GPU-based solutions.