High-performance computing on shared-memory/multi-core architectures could suffer from non-negligible performance bottlenecks due to coordination algorithms, which are nevertheless necessary to ensure the overall correctness and/or to support the execution of housekeeping operations, e.g. to recover computing resources (e.g., memory). Although more complex in design/development, a paradigm switch from classical coordination algorithms to wait-free ones could significantly boost the performance of HPC applications.
In this paper we explore the relevance of this paradigm shift in shared-memory architectures, by focusing on the context of Parallel Discrete Event Simulation, where the Global Virtual Time (GVT) represents a fundamental coordination algorithm. It allows to compute the lower bound on the value of the logical time passed through by all the entities participating in a parallel/distributed computation. Hence it can be used to discriminate what events belong to the past history of the computation---thus being considered as committed---and allowing for memory recovery (e.g. of obsolete logs that were taken in order to support state recoverability) and non-revokable operations (e.g. I/O).
We compare the reference (blocking) algorithm for shared memory, the one proposed by by Fujimoto and Hybinette \cite{Fuj97}, with an innovative wait-free implementation, emphasizing on what design choices must be made to enforce this paradigm shift, and what are the performance implications of removing critical sections in coordination algorithms.
Virtualizing the Stampede2 Supercomputer with Applications to HPC in the Cloud
在得克萨斯州高级计算中心（TACC）开发的方法进行了描述和演示，用于自动化的弹性，虚拟集群模拟冲压2高性能计算（HPC）系统的建设。该集群可以在几分钟内在Jetstream自助云系统上构建和/或扩展，并共享原始Stampede 2的许多属性，包括：i）通用身份管理，ii）访问相同的文件系统，iii）等效的软件应用程序堆栈和模块系统，iv）通过Slurm的类似作业调度界面。我们测量了我们的虚拟集群上的一些常见的科学应用程序的解决方案的时间与Stampede 2上的等效运行，并开发了性能相似或可接受的应用程序配置文件。对于这样的应用程序，虚拟集群提供了一种有效的“云爆发”形式，有可能显著改善总体周转时间，特别是当Stampede 2遇到长队列等待时间时。此外，虚拟集群可用于测试和调试，而不会直接影响Stampede 2。最后，我们讨论了科学网关如何利用TACC Jobs API Web服务来透明地向最终用户集成这种云爆发技术。
Impossibility Results for Strong Linearizability: The Difficulty of Consistent Refereeing
Technion - Israel Institute of Technology（以色列理工学院）；Universidad Nacional Autónoma de México (UNAM)（墨西哥国立自治大学）；LIX, Ecole Polytechnique, CNRS and Institut Polytechnique de Paris（巴黎综合理工学院LIX实验室、法国国家科学研究中心和巴黎理工学院）
Enabling Hybrid HPCQC Workflows with a Heterogeneous Software Stack
Leibniz Supercomputing Centre (LRZ)（莱布尼茨超级计算中心）；IQM Quantum Computers (IQM)（IQM量子计算机公司）；Argonne National Laboratory (ANL)（阿贡国家实验室）；Munich Quantum Valley (MQV)（慕尼黑量子谷）
Improving the Efficiency of a Deep Reinforcement Learning-Based Power Management System for HPC Clusters Using Curriculum Learning
Department of Computer Science and Electronics, Universitas Gadjah Mada（加雅达大学计算机科学与电子系）；Cyberscience Center, Tohoku University（东大网络科学中心）；Department of Industrial Management, National Taiwan University of Science and Technology（台湾科技大学工业管理系）
University Pierre et Marie Curie - Paris 6（巴黎第六大学（皮埃尔和玛丽·居里大学））；LIP6-CNRS 7606（法国国家科学研究中心LIP6实验室（7606））；BGU（本-古里安大学）；Technion-Israel Institute of Technology（以色列理工学院）
