Exascale systems, expected to emerge by the end of the next decade, will require the exploitation of billion-way parallelism at multiple hierarchical levels in order to achieve the desired sustained performance. The task of assessing future machine performance is approached by identifying the factors which currently challenge the scalability of parallel applications. It is suggested that the root cause of these challenges is the incoherent coupling between the current enabling technologies, such as Non-Uniform Memory Access of present multicore nodes equipped with optional hardware accelerators and the decades older execution model, i.e., the Communicating Sequential Processes (CSP) model best exemplified by the message passing interface (MPI) application programming interface. A new execution model, ParalleX, is introduced as an alternative to the CSP model. In this paper, an overview of the ParalleX execution model is presented along with details about a ParalleX-compliant runtime system implementation called High Performance ParalleX (HPX). Scaling and performance results for an adaptive mesh refinement numerical relativity application developed using HPX are discussed. The performance results of this HPX-based application are compared with a counterpart MPI-based mesh refinement code. The overheads associated with HPX are explored and hardware solutions are introduced for accelerating the runtime system.
Ecomap: Sustainability-Driven Optimization of Multi-Tenant DNN Execution on Edge Servers
School of Electrical, Computer and Biomedical Engineering, Southern Illinois University（南伊利诺伊大学电气、计算机与生物医学工程学院）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
Scalable Runtime Architecture for Data-driven, Hybrid HPC and ML Workflow Applications
Rutgers, the State University of New Jersey（罗格斯大学（新泽西州立大学））；Brookhaven National Laboratory（布鲁克海文国家实验室）；Princeton Plasma Physics Laboratory（普林斯顿等离子体物理实验室）；Princeton University（普林斯顿大学）
本文扩展 RADICAL-Pilot，提出支持基于服务执行的可扩展运行时架构，以实现跨本地与远程 HPC/云资源的 HPC/ML 无缝耦合和低开销并发 ML 执行。
LAFA: Agentic LLM-Driven Federated Analytics over Decentralized Data Sources
Shanghai Jiao Tong University（上海交通大学）；Peng Cheng Laboratory（鹏城实验室）；The University of Hong Kong（香港大学）；Zhejiang University（浙江大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Houston（休斯顿大学）
