Zhongjie Chen

Zhongjie Chen, Wentao Zhang, Yulong Tang et al.

The Linux kernel is permeated with constant values that are critical to system performance. Many of these constants, referred to as perf-consts, are magic numbers with brittle assumptions on hardware and workloads. Unfortunately, there is no capability of in-situ tuning of perf-const values on deployed kernels. This paper rethinks OS performance tunability. We present Xkernel, a system that offers a safe, efficient, and programmable interface for in-situ tuning of any perf-consts directly on a running kernel. Xkernel transforms any perf-const into a tunable knob on demand using a novel approach called Scoped Indirect Execution (SIE). SIE captures precise binary boundaries where a perf-const enters system state and redirects control to synthesized instructions that update the state as if new values were used. Xkernel goes beyond version atomicity when updating perf-consts to guarantee side-effect safety, a property notably absent in existing kernel update mechanisms. Case studies on various OS subsystems demonstrate significant performance benefits of tuning perf-consts which is made possible by Xkernel.

Yi Zou, Mengying Shi, Zhongjie Chen et al.

ESGReveal is an innovative method proposed for efficiently extracting and analyzing Environmental, Social, and Governance (ESG) data from corporate reports, catering to the critical need for reliable ESG information retrieval. This approach utilizes Large Language Models (LLM) enhanced with Retrieval Augmented Generation (RAG) techniques. The ESGReveal system includes an ESG metadata module for targeted queries, a preprocessing module for assembling databases, and an LLM agent for data extraction. Its efficacy was appraised using ESG reports from 166 companies across various sectors listed on the Hong Kong Stock Exchange in 2022, ensuring comprehensive industry and market capitalization representation. Utilizing ESGReveal unearthed significant insights into ESG reporting with GPT-4, demonstrating an accuracy of 76.9% in data extraction and 83.7% in disclosure analysis, which is an improvement over baseline models. This highlights the framework's capacity to refine ESG data analysis precision. Moreover, it revealed a demand for reinforced ESG disclosures, with environmental and social data disclosures standing at 69.5% and 57.2%, respectively, suggesting a pursuit for more corporate transparency. While current iterations of ESGReveal do not process pictorial information, a functionality intended for future enhancement, the study calls for continued research to further develop and compare the analytical capabilities of various LLMs. In summary, ESGReveal is a stride forward in ESG data processing, offering stakeholders a sophisticated tool to better evaluate and advance corporate sustainability efforts. Its evolution is promising in promoting transparency in corporate reporting and aligning with broader sustainable development aims.