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ine migrate a sql server database from windows to linux disaster recovery disaster recovery for microsoft sql server disaster recovery for microsoft sql server on persistent disk disaster recovery for microsoft sql server on hyperdisk deploying microsoft sql server for multi regional disaster recovery back up sql server databases to a google cloud storage bucket back up sql server databases using instant snapshots cloning a microsoft sql server database on compute engine load test sql server using hammerdb redis deployment options for redis on google cloud containers containers on compute engine deploy containers on vms and managed instance groups configure options to run your container transition from the container startup agent prepare for the shutdown of the container startup agent prevent the creation of vms that use the container metadata migrate containers that were deployed on vms during vm creation openshift workloads openshift on google cloud overview plan for openshift on google cloud overview of cluster services for openshift automatic configuration validation for openshift clusters built in integrations for openshift best practices for high availability with openshift disaster recovery for openshift on google cloud disaster recovery strategies for active passive and active inactive setups with openshift microsoft windows windows workloads best practices for windows server vms setting up active directory best practices for running active directory on google cloud deploy microsoft sharepoint server on compute engine deploying microsoft exchange server 2016 on compute engine windows server perform an in place upgrade of windows server run windows server failover clustering ibm spectrum symphony integrate ibm spectrum symphony with google cloud install the compute engine symphony provider install the google kubernetes symphony provider troubleshoot ibm spectrum symphony others load testing distributed load testing using kubernetes ssh port forwarding and load testing analytics monte carlo methods using apache spark machine learning run tensorflow inference workloads with tensorrt5 and nvidia t4 gpu monitor monitor logs view audit logs view usage reports view compute engine operations migrate from activity logs to audit logs view activity logs monitor resources monitor vm and sole tenant node usage observe and monitor vms monitor gpu performance monitor gpu performance on linux vms monitor gpu performance on windows vms monitor disks monitor disk health monitor the replica states of regional disks monitor disks list of metrics for pools monitor pools monitor reservations organize resources using labels scale autoscale groups of vms about autoscaling groups of vms create and manage autoscalers scale based on cpu utilization scale based on predictions scale based on load balancing serving capacity scale based on monitoring metrics scale based on schedules use an autoscaling policy with multiple signals manage autoscalers understand autoscaler decisions view autoscaler logs autoscale node groups reserve vm capacity choose a reservation type sharing reservations best practices for shared reservations allow a project to share reservations on demand reservations about on demand reservations create an on demand reservation for a single project for multiple projects combine an on demand reservation with a cud modify an on demand reservation delete an on demand reservation future reservations about future reservations create a reservation request for a single project for multiple projects modify a reservation request delete a reservation request future reservations in calendar mode about future reservations in calendar mode create a reservation request in calendar mode delete a reservation request in calendar mode view reservations or reservation requests consume a reservation prevent vms from consuming reservations load balancing about load balancing and scaling add an instance group to a load balancer request routing to a multi region external https load balancer cross region load balancing for microsoft iis backends set up internal tcp udp load balancing build reliable and scalable applications use autohealing for highly available applications use load balancing for highly available applications use autoscaling for highly scalable applications globally autoscale a web service on compute engine patterns for scalable and resilient applications patterns for using floating ip addresses on compute engine optimize resource utilization use recommendations to manage resources apply machine type recommendations to vms configure machine type recommendations apply machine type recommendations to migs view and apply idle resources recommendations view and understand vm insights view and understand mig insights manage idle vm recommendations idle vm recommendations overview view and apply idle vm recommendations configure idle vm recommendations manage reservation recommendations reservation recommendations overview view and apply idle reservation recommendations view and apply underutilized reservation recommendations configure idle reservation recommendations configure underutilized reservation recommendations overcommit cpus on sole tenant vms manual live migration about manual live migration manually live migrate vms share sole tenant node groups next generation dynamic resource management cost savings get discounts for committed usage about commitments and committed use discounts cuds resource based cuds purchase resource based commitments without attached reservations with attached reservations for os licenses manage resource based commitments renew commitments automatically extend commitment terms merge and split commitments upgrade commitments share resource based cuds across projects get discounts for sustained usage disk performance optimize hyperdisk performance optimize persistent disk performance optimize local ssd performance workload performance set the number of threads per core customize the number of visible cpu cores analyze the cpu performance using the pmu pmu overview enable the pmu in vms manage the pmu in vms network performance network bandwidth use google virtual nic use irdma network driver use idpf network interface configure a vm with higher bandwidth reduce latency by using compact placement policies optimize tcp network communication optimize tcp network performance optimize tcp network resiliency benchmark higher bandwidth vms optimize app latency with load balancing use dpdk to improve network performance network performance and gpu vms networking and gpu machines use higher network bandwidth patterns for using multiple host nics troubleshoot general tips troubleshoot connectivity troubleshoot rdp troubleshoot ssh troubleshoot os login troubleshoot vms troubleshoot vm operations troubleshoot vm creation troubleshoot resource availability errors troubleshoot bulk api vm creation troubleshoot vm reboots and shutdowns troubleshoot vm suspension troubleshoot vm updates troubleshoot unresponsive vms troubleshoot vm startup troubleshoot fstab errors troubleshoot kernel panic collecting core dumps rescue an inaccessible vm troubleshoot cpu bus locks troubleshoot cpu soft lockups troubleshoot vm configurations troubleshoot arm vms troubleshoot gpu vms troubleshoot nvidia gpu errors generate a nvidia bug report for blackwell gpus troubleshoot nested virtualization troubleshoot using vm screenshots troubleshoot sole tenant nodes troubleshoot vm performance issues troubleshoot sudoers files troubleshoot windows vms troubleshoot windows vms collecting diagnostic information troubleshoot using the serial console troubleshoot using the serial console viewing serial port output troubleshoot instance groups troubleshoot managed instance groups migs troubleshoot os management troubleshoot licenses troubleshoot image import and export troubleshooting sles pay as you go registration troubleshooting ubuntu pro registration troubleshoot metadata server troubleshoot metadata server troubleshoot networking issues troubleshoot common networking issues troubleshoot network drivers troubleshoot vm performance issues troubleshoot storage troubleshoot disk creation troubleshoot full disks and disk resizing troubleshoot disk encryption troubleshoot nvme disks troubleshoot instant snapshots troubleshoot standard snapshots troubleshoot reservations and commitments troubleshoot reservation creation troubleshoot reservation consumption troubleshooting reservation monitoring troubleshoot reservation updates troubleshoot future reservation creation and updates troubleshoot automatic commitment renewal troubleshoot quota errors troubleshoot concurrent operation quota errors troubleshoot workload authentication troubleshoot default service accounts troubleshoot workload to workload authentication ai and ml application development application hosting compute data analytics and pipelines databases distributed hybrid and multicloud industry solutions migration networking observability and monitoring security storage access and resources management costs and usage management infrastructure as code sdk languages frameworks and tools home documentation compute compute engine guides send feedback overview of hpc clusters with enhanced cluster management capabilities stay organized with collections save and categorize content based on your preferences to create the infrastructure for tightly coupled applications that scale across multiple nodes you can create a cluster of virtual machine vm instances this guide provides a high level overview of the key considerations and steps to configure a cluster of virtual machine vm instances for high performance computing hpc workloads using dense resource allocation with h4d compute engine adds support for running massive hpc workloads by treating an entire cluster of vm instances as a single computer using topology aware placement of vms lets you access many instances within a single networking superblock and minimizes network latency you can also configure cloud rdma on these instances to maximize inter node communication performance which is crucial for tightly coupled hpc workloads note this type of configuration relies on similar features and concepts as those documented in the ai hypercomputer documentation for accelerator optimized vms with gpus you create these hpc vm clusters with h4d by reserving blocks of capacity instead of individual resources using blocks of capacity for your cluster enables enhanced cluster management capabilities hpc clusters with h4d instances can be created either with or without enhanced cluster management capabilities if you don t require enhanced cluster management capabilities features with your h4d hpc cluster or if you want to create hpc clusters using a machine series other than h4d then use the following instructions for creating hpc instances or clusters managed instance groups migs bulk api create an hpc ready vm instance cluster terminology when working with blocks of capacity the following terms are used block link a collection of sub blocks that are interconnected with a non blocking fabric providing a high bandwidth interconnect between all hosts in the cluster cluster link a collection of blocks interconnected by a high speed network fabric a cluster can scale to thousands of cpus for running large scale hpc workloads each cluster is globally unique communication across different blocks adds only one additional hop maintaining high performance and predictability even at a massive scale cluster level metadata is also available to orchestrators for intelligent large scale job placement cluster toolkit link an open source tool offered by google that simplifies the configuration and deployment for clusters that use either slurm or google kubernetes engine you use predefined blueprints to build a deployment folder that is based on the blueprint you can modify blueprints or the deployment folder to customize deployments and your software stack you then use terraform or packer to run the commands generated by cluster toolkit to deploy the cluster dense deployment link a resource request that allocates your compute instance resources physically close to each other to minimize network hops and optimize for the lowest latency network fabric link a network fabric provides high bandwidth low latency connectivity across all blocks and google cloud services in a cluster jupiter is google s data center network architecture that uses software defined networking and optical circuit switches to evolve the network and optimize its performance node or host link a single physical server machine in the data center each host has its associated compute resources such as cpus memory and network interfaces the number and configuration of these compute resources depend on the machine type compute instances are provisioned on top of a physical host orchestrator link an orchestrator automates the management of your clusters with an orchestrator you don t have to manage each vm instance in the cluster an orchestrator such as slurm or google kubernetes engine gke handles tasks like job queueing resource allocation auto scaling with gke and other day to day cluster management tasks sub blocks link a group of hosts and associated connectivity hardware that are located on a single physical rack a top of rack tor switch connects these hosts enabling extremely efficient single hop communication between any two cpus within the sub block cloud rdma facilitates this direct communication overview of cluster creation process with h4d vms to create hpc clusters on reserved blocks of capacity you must complete the following steps review available provisioning models choose a consumption option and obtain capacity choose a deployment option and orchestrator choose the operating system or cluster image create your cluster provisioning models for vm and cluster creation when creating vm instances you can use the provisioning models described in compute engine instances provisioning models to create a tightly coupled h4d instances you must use one of the following provisioning models to obtain the necessary resources for creating compute instances reservation bound you can reserve resources at a discounted price for a future date and duration at the start of your reservation period you can use the reserved resources to create vms or clusters you have exclusive access to your reserved resources for the reservation period flex start you can request discounted resources for up to seven days compute engine makes best effort attempts to schedule the provisioning of your requested resources as soon as they re available you have exclusive access to your obtained resources for your requested period spot based on availability you can immediately obtain deeply discounted resources however compute engine might stop or delete the vm instances at any time to reclaim capacity reservation bound provisionin...
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