Dear Colleagues,
We are pleased to invite you to submit original contributions to the Performance Evaluation Special Issue on Performance Analysis and Evaluation of Systems for Artificial Intelligence. You can find the detailed information at the following link,
https://www.sciencedirect.com/journal/performance-evaluation/about/call-for-papers#performance-analysis-and-evaluation-of-systems-for-artificial-intelligence

A shortened version of the CfP is attached at the end of the email. We look forward to receiving your submissions.

Guest Editors
Anshul Gandhi
Bo Jiang
Shaolei Ren

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Submission Open Date: 22nd September 2023
Manuscript Submission Deadline: 2nd February 2024
Latest Acceptance Deadline for all papers: 2nd August 2024

Artificial Intelligence and Machine Learning (AI/ML) applications are widely employed today in almost all sectors of society, ranging from everyday online applications (e.g., video streaming, e-commerce) to emerging tools for enterprises (e.g., large language models, inference serving platforms). AI/ML models that enable these applications are quickly growing in size, featuring billions of parameters. Consequently, the computer systems used to train, run, and serve predictions from these models, henceforth referred to as Systems for AI/ML, have high performance requirements and are expensive to procure and operate, both in terms of monetary cost and environmental impact. For example, training a large AI/ML model requires computer system resources (servers, GPUs, TPUs) that consume megawatt-hours of electricity and emit tons of greenhouse gasses.
There is thus an urgent need to study and optimize the performance of Systems for AI/ML, characterizing resource management and techniques for training and testing, their implications on accuracy requirements of the AI/ML models, and develop methods to effectively size and deploy these systems in production environments (e.g., at the edge). The performance analysis and evaluation of the resources and algorithms used to train, test, and operate an AI/ML system can provide a deeper understanding of the behavior and operation of such systems, allowing researchers to then develop solutions for optimizing resource efficiency.
This special issue solicits unpublished works on performance analysis and evaluation research on the timely topic of Systems for AI/ML. The special issue will highlight novel approaches to analysis, modeling, and evaluation of Systems for AI/ML, as well as specific applications and emerging architectures designed for Systems for AI/ML. The special issue is intended for researchers, engineers, and practitioners who study and work on Systems for AI/ML, as well as those interested in performance analysis and modeling in general. We welcome submissions that study performance and resource management in Systems for AI/ML or those that present novel algorithms, techniques, or solutions to improve the efficiency and sustainability of Systems for AI/ML. While both theoretical and experimental approaches are welcome, attention will be paid in the review process on rigor and quantitative analysis.
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