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23 pages, 1205 KB

Open AccessArticle

RegRes: An Exploratory Study of Cross-Platform and Cross-Code Performance Prediction Using Regression Models

by Jie Li, Gui Zhao, Biqing Zeng, Jingxin Liu and Fangjie Chen

Appl. Sci. 2026, 16(2), 910; https://doi.org/10.3390/app16020910 - 15 Jan 2026

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In the era of heterogeneous computing environments, including diverse hardware platforms and programming paradigms, accurate performance prediction of software applications is essential for efficient resource allocation, cost optimization, and informed deployment decisions. However, traditional methods often require platform-specific measurements, which are resource-intensive and limited by data scarcity in low-performance or novel code contexts. This exploratory study addresses these challenges by leveraging transfer learning with regression models to enable cross-platform (from Intel x86 to ARM M1) and cross-code performance predictions. Using the Renaissance benchmark suite (renaissance-gpl-0.15.0.jar), we systematically evaluate eight traditional machine learning models and a deep neural network across scenarios of data transfer and fine-tuning. Key findings demonstrate that transfer learning significantly improves prediction accuracy, with tree-based models like Extra Trees achieving high R² scores (0.92) and outperforming DNNs in robustness, particularly under noisy or data-scarce conditions. The study provides empirical insights into model effectiveness, highlights the superiority of transfer settings, and offers practical guidance for software engineers to reduce measurement overheads and enhance optimization processes. Full article

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29 pages, 11989 KB

Open AccessArticle

Accidental Choices—How JVM Choice and Associated Build Tools Affect Interpreter Performance

by Jonathan Lambert, Rosemary Monahan and Kevin Casey

Computers 2022, 11(6), 96; https://doi.org/10.3390/computers11060096 - 14 Jun 2022

Cited by 1 | Viewed by 4824

Abstract

Considering the large number of optimisation techniques that have been integrated into the design of the Java Virtual Machine (JVM) over the last three decades, the Java interpreter continues to persist as a significant bottleneck in the performance of bytecode execution. This paper examines the relationship between Java Runtime Environment (JRE) performance concerning the interpreted execution of Java bytecode and the effect modern compiler selection and integration within the JRE build toolchain has on that performance. We undertook this evaluation relative to a contemporary benchmark suite of application workloads, the Renaissance Benchmark Suite. Our results show that the choice of GNU GCC compiler version used within the JRE build toolchain statistically significantly affects runtime performance. More importantly, not all OpenJDK releases and JRE JVM interpreters are equal. Our results show that OpenJDK JVM interpreter performance is associated with benchmark workload. In addition, in some cases, rolling back to an earlier OpenJDK version and using a more recent GNU GCC compiler within the build toolchain of the JRE can significantly positively impact JRE performance. Full article

(This article belongs to the Special Issue Code Generation, Analysis and Quality Testing)

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