Study of Automatic GPU Offloading Technology for Open IoT

TL;DR

This paper introduces an automatic GPU offloading method using genetic algorithms to enhance performance in Open IoT systems, demonstrating over 35-fold speedup in matrix computations.

Contribution

It proposes a novel GPU offloading technique for Tacit Computing in Open IoT, optimizing performance and operation costs through genetic algorithm-based loop extraction.

Findings

Achieved over 35 times performance improvement in matrix manipulation

Validated effectiveness of GPU offloading in C/C++ applications

Demonstrated rapid tuning within 1 hour

Abstract

IoT technologies have been progressed. Now Open IoT concept has attracted attentions which achieve various IoT services by integrating horizontal separated devices and services. For Open IoT era, we have proposed the Tacit Computing technology to discover the devices with necessary data for users on demand and use them dynamically. However, existing Tacit Computing does not care about performance and operation cost. Therefore in this paper, we propose an automatic GPU offloading technology as an elementary technology of Tacit Computing which uses Genetic Algorithm to extract appropriate offload loop statements to improve performances. We evaluate a C/C++ matrix manipulation to verify effectiveness of GPU offloading and confirm more than 35 times performances within 1 hour tuning time.