OKEAP: A framework for OpenCL kernels execution time prediction with adaptive machine learning techniques

Abstract

This paper presents a novel adaptive machine learning (ML) framework for predicting computational tasks execution time in OpenCL environments, specifically designed to address load balancing challenges within Central Processing Unit (CPU) and Graphics Processing Unit (GPU) Heterogeneous Computing Systems (HCS). Finding a universal solution for load balancing is complex given the diverse hardware configurations and computational tasks, ranging from cybersecurity to ML workloads. The focus on CPU-GPU systems enables our solution to effectively navigate the specific demands of modern heterogeneous computing environments. The framework proposed in the paper implements a regression-based deep learning network to analyze OpenCL kernels and monitor the computing environment's state. The ML model, which is a part of the proposed framework, excels at predicting kernel execution times across various devices, enabling dynamic and responsive load balancing. The method underlying the framework operation differs from traditional static code analyzer–based approaches, which specify execution devices without considering critical runtime features extracted through kernel profiling. The framework enhances performance through dynamic selection of devices based on system status real-time monitoring. The proposed solution, integrated with a comprehensive profiling system, analyzes memory access patterns, data transfer rates, and others. The regression-based task modeling approach provides precise performance measurements, avoiding the limitations of classification methods that require redundant task executions. Empirical evaluation demonstrates substantial improvements achieving up to 9.8% enhancement in load balancing efficiency during operational fine-tuning.