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利用CXL架构提升人工智能和高性能计算应用性能.pdf

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1、1|2024 SNIA.All Rights Reserved.Increasing AI and HPC Application Performance with CXL FabricsPresented byKurtis Bowman,AMD Sandeep Dattaprasad,Astera LabsSteve Scargall,MemVerge 2|2024 SNIA.All Rights Reserved.Meet your panelists Kurtis Bowman CXL MWG Co-ChairDirector,Server System Performance at A

2、MD Steve ScargallSenior Product Manager&Software Architect at MemVerge Sandeep DattaprasadSenior Product Manager at Astera Labs3|2024 SNIA.All Rights Reserved.CXL EcosystemGrowth of CXL ecosystem since its inception 4|2024 SNIA.All Rights Reserved.CXL Specification Feature Summary5|2024 SNIA.All Rig

3、hts Reserved.CXL 3.1 Use Cases for AI&HPC6|2024 SNIA.All Rights Reserved.CXL 3.1 Trusted Security Protocol(TSP)Allows for Virtualization-based,Trusted Execution Environments(TEEs)to host Confidential Computing Workloads(CC WL)Key Capabilities:Separation between TVM*&CSPs infrastructure(VMM)Configura

4、tion of CXL deviceEncryption of sensitive data in both Host/Device memoryCryptographically verify correct configuration of trusted computing environmentBenefits:Freedom to migrate sensitive WLs to TSP-enabled CloudsCollaboration with multiple parties for sharing dataConform to Compliance&Data sovere

5、ignty programsStrengthen Application security&Software IP protection*TVM=Trusted VM 7|2024 SNIA.All Rights Reserved.Please take a moment to rate this session.Your feedback is important to us.8|2024 SNIA.All Rights Reserved.Backup9|2024 SNIA.All Rights Reserved.Igniting Innovation:The Expanding CXL S

6、oftware UniverseRecent Highlights:Linux Kernel and QEMU continue to add CXL features-up to 3.1 QEMU has CXL expansion and sharing.DCD and MHD are in development.Kernel 6.8 added NUMA QoS:read/write latency and bandwidth for devices Kernel 6.9 added Weighted NUMA Interleaving Fabr

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本文主要探讨了如何通过CXL fabrics提高AI和HPC应用程序的性能。CXL 3.1规格提供了用于AI和HPC的用例,并引入了可信安全协议(TSP),支持虚拟化基础的信任执行环境(TEEs),用于托管机密计算工作负载(CC WL)。TSP的关键功能包括在TVM和CSP的基础设施之间分离,配置CXL设备,以及在主机/设备内存中加密敏感数据,并 cryptographically 验证信任计算环境正确配置。这使得敏感工作负载可以迁移到TSP-enabled云,促进多方数据共享,符合合规性和数据主权计划,加强应用程序安全和软件IP保护。此外,文章还提到了CXL软件宇宙的扩展,如Linux内核和QEMU持续添加CXL功能,以及与CXL相关的各种软件和工具,如Fabric Management、FAMFS、MemVerge Shared Memory Object Store等,这些都有助于提高AI/ML工作负载的性能。
"CXL技术如何提升AI和HPC性能?" "CXL生态系统的发展现状和未来趋势是什么?" "如何利用CXL实现高效的数据处理和GPU资源利用?"
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