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UID:pretalx-kvm-forum-2024-QPYVKX@pretalx.com
DTSTART;TZID=CET:20240923T114500
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DESCRIPTION:Cloud computing\, with its flexible resource allocation and lar
 ge-scale data storage\, provides an integrated underlying platform for the
  widespread application of AI\, including large-scale model training and i
 nference. However\, being different from traditional applications\, AI foc
 uses more on heterogeneous computing\, and building it on virtualization b
 rings some new issues and challenges\, including:\n1. The PCIe P2P communi
 cation efficiency between GPUs or GPUs and RDMA NICs is crucial for large-
 scale model training and inference. However\, in virtualization scenarios\
 , there will be a serious performance degradation due to the enablement of
  IOMMU.\n2. Various higher-precision (millisecond-level) monitoring agents
  are usually deployed in VMs to monitor something like PCIe bandwidth\, ne
 twork bandwidth\, etc. We found that traditional PMU virtualization cannot
  fully meet such monitoring needs. And those monitoring agents can also re
 sult in a high number of VMEXITs due to frequent PIO and RDPMC operations.
 \n\nTo address these challenges\, this topic proposes a set of solutions\,
  such as avoiding P2P TLPs being redirected to IOMMU and passthroughing co
 re and uncore PMUs to the guest\, to bridge the gap on AI infrastructure b
 etween virtualized and bare-metal environments.
DTSTAMP:20260309T213948Z
LOCATION:Hall A+B
SUMMARY:The Challenges of building AI Infra on virtualization - Xin He\, Ha
 o Hong
URL:https://pretalx.com/kvm-forum-2024/talk/QPYVKX/
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