Maximize GPU network bandwidth in Autopilot mode clusters

This page shows you how to maximize network bandwidth and throughput for high-performance GPU workloads in Google Kubernetes Engine (GKE) Autopilot clusters by using GPUDirect-TCPXO, GPUDirect-TCPX, gVNIC, and multi-networking. If you use Standard clusters, see Maximize GPU network bandwidth in Standard mode clusters.

This page is intended for machine learning (ML) engineers and platform administrators who facilitate ML workloads. Before reading this page, ensure that you're familiar with networking technologies, such as network interface cards (NICs) and TCP, and with accelerator technologies like the NVIDIA Collective Communications Library (NCCL).

Artificial intelligence (AI), ML, and high performance computing (HPC) applications require powerful acceleration to optimize performance by reducing job completion times. For example, ML models that focus on conversational AI and image generation require high scalability and compute power.

About Google Cloud GPU supercomputers

Google Cloud has accelerator-optimized supercomputers that are built for scalable, massive models. These machines have the following benefits:

  • Eight NVIDIA H100 GPUs per machine.
  • Up to 200 Gbps bandwidth on the primary NIC.
  • Secondary NICs (up to eight on A3 Mega machine types and up to four on A3 High machine types), each supporting up to 200 Gbps bandwidth for GPU data transfer.

For a full list of benefits, see A3 machine series in the Compute Engine documentation.

Your GKE workload must use all available GPUs and all available secondary NICs on a single node and use a significant portion of the available bandwidth. The solution described in this document is ideal for workloads that require high performance, high throughput, and low latency.

Required features and capabilities for maximized bandwidth

To maximize your network bandwidth in GPU supercomputer nodes, use all of the following features:

  • GPUDirect networking stack: The A3 machine series supports two networking stacks for custom, remote direct memory access (RDMA):
    • On A3 High machine types and NVIDIA H100 GPUs, utilize GPUDirect-TCPX to reduce the overhead required to transfer packet payloads to and from GPUs, which significantly improves throughput at scale compared to GPUs that don't use GPUDirect.
    • On A3 Mega machine types and NVIDIA H100 Mega GPUs, utilize GPUDirect-TCPXO which further improves GPU to VM communication.
  • gVNIC: Enable GPUDirect capabilities such as packet header splitting, flow steering, and buffer management. gVNIC is required to use GPUDirect-TCPX or GPUDirect-TCPXO. For details about gVNIC, see Increase network traffic speed for GPU nodes.
  • Multi-networking: Add secondary NICs to the accelerator-optimized machine. Each NIC is associated with a separate subnet in its own VPC to avoid conflicts. For details about multi-network support, see Setup multi-network support for Pods.
  • Placement policies: Use a resource placement policy to place all GPU nodes for a specific workload on physically close servers to minimize latency. For details, see Define compact placement for GKE nodes.

Procedure outline

To use all of these capabilities together, you'll do the following:

  1. Create Virtual Private Cloud (VPC)s and subnets
  2. Create the GKE environment.
  3. Install the GPUDirect binary and the NCCL plugin
  4. Deploy the NRI device injector plugin
  5. Deploy a test workload to verify GPUDirect setup

Before you begin

Before you start, make sure you have performed the following tasks:

  • Enable the Google Kubernetes Engine API.
    • Enable Google Kubernetes Engine API
  • If you want to use the Google Cloud CLI for this task, install and then initialize the gcloud CLI. If you previously installed the gcloud CLI, get the latest version by running gcloud components update.
  • Ensure that you have enough quota for H100 GPUs. To request more quota, see GPU quotas.

Requirements

The following requirements apply to both GPUDirect-TCPX and GPUDirect-TCPXO unless otherwise indicated.

  • Your cluster must use GKE version 1.31.1-gke.1621000 or later.
  • Your GPU nodes must use NVIDIA driver version 535 or later.
  • You must use GKE Dataplane V2.

Limitations

The following limitations apply:

  • GPUDirect-TCPX and GPUDirect-TCPXO are not supported with multi-instance GPUs, GPU time-sharing, or NVIDIA MPS.
  • You can't use NCCL FastSocket.
  • Your GKE workload must use all available GPUs and all available secondary NICs on a single node. Multiple pods cannot use GPUDirect-TCPX or GPUDirect-TCPXO on a single node.
  • You can only use the a3-highgpu-8g and the a3-megagpu-8g machine types. Other A3 machine types aren't supported.

Create VPCs and subnets

Create separate VPC networks in your project for each virtual NIC that you'll add to your nodes. Each VPC network must have a subnet and a firewall rule that allows internal network traffic.

  1. Create the VPC networks for GPUDirect in your project, each with a subnet and a firewall rule. Choose the GPUDirect-TCPX tab for A3 High machine types, or choose the GPUDirect-TCPXO tab for A3 Mega machine types, then complete the following instructions:

    GPUDirect-TCPXO

    To maximize your bandwidth, we recommend that you create eight new networks.

    for N in $(seq 1 8); do
gcloud compute networks create PROJECT_ID-net-$N \
    --subnet-mode=custom \
    --mtu=8244

gcloud compute networks subnets create PROJECT_ID-sub-$N \
    --network=PROJECT_ID-net-$N \
    --region=REGION \
    --range=SUBNET_RANGE

gcloud compute firewall-rules create PROJECT_ID-internal-$N \
  --network=PROJECT_ID-net-$N \
  --action=ALLOW \
  --rules=tcp:0-65535,udp:0-65535,icmp \
  --source-ranges=SOURCE_RANGE
done

    Replace the following:

    • PROJECT_ID: your Google Cloud project ID.
    • REGION: the Compute Engine region for each subnet.
    • SUBNET_RANGE: the IP address range of each subnet in CIDR notation. This example command iterates for eight subnets, so you should use a variable to change the IP address for each subnet. For example, specify 192.168.$N.0/24 so that the first subnet uses 192.168.1.0/24, the second subnet uses 192.168.2.0/24, and so on.
    • SOURCE_RANGE: The source IP address range for the firewall rule to allow ingress traffic, in CIDR notation. For example, 192.168.0.0/16.

    GPUDirect-TCPX

    To maximize your bandwidth, we recommend that you create four new networks.

    for N in $(seq 1 4); do
gcloud compute networks create PROJECT_ID-net-$N \
    --subnet-mode=custom \
    --mtu=8244

gcloud compute networks subnets create PROJECT_ID-sub-$N \
    --network=PROJECT_ID-net-$N \
    --region=REGION \
    --range=SUBNET_RANGE

gcloud compute firewall-rules create PROJECT_ID-internal-$N \
  --network=PROJECT_ID-net-$N \
  --action=ALLOW \
  --rules=tcp:0-65535,udp:0-65535,icmp \
  --source-ranges=SOURCE_RANGE
done

    Replace the following:

    • PROJECT_ID: your Google Cloud project ID.
    • REGION: the Compute Engine region for each subnet.
    • SUBNET_RANGE: the IP address range of each subnet in CIDR notation. This example command iterates for four subnets, so you should use a variable to change the IP address for each subnet. For example, specify 192.168.$N.0/24 so that the first subnet uses 192.168.1.0/24, the second subnet uses 192.168.2.0/24, etc.
    • SOURCE_RANGE: The source IP address range for the firewall rule to allow ingress traffic, in CIDR notation. For example, 192.168.0.0/16.

  2. Verify that the networks were created:

    gcloud compute networks list

Create the GKE environment

Create a new GKE cluster that uses multi-networking (Preview). You can't update an existing cluster to use multi-networking.

GPUDirect-TCPXO

  1. Choose an available GKE version that supports GPUDirect-TCPXO. To list the versions, run this command:

    gcloud container get-server-config \
    --format="yaml(validMasterVersions)" \
    --region=REGION \
    --project=PROJECT_ID

    Replace the following:

    • REGION: the compute region for the cluster control plane.
    • PROJECT_ID: your Google Cloud project ID.

  2. Create a cluster:

    gcloud beta container clusters create-auto CLUSTER_NAME \
    --project=PROJECT_ID \
    --location=LOCATION \
    --cluster-version=VERSION \
    --enable-multi-networking \
    --workload-policies=allow-net-admin

    Replace the following:

    • CLUSTER_NAME: the name of your new cluster.
    • VERSION: a GKE version that supports GPUDirect-TCPXO, as described in Requirements.
    • LOCATION: the Compute Engine location for the cluster.

  3. Create Network and GKENetworkParamSet resources in the cluster that correspond to the VPC networks and subnetworks that you created:

    kubectl apply -f - <<EOF
apiVersion: networking.gke.io/v1
kind: Network
metadata:
  name: vpc1
spec:
  parametersRef:
    group: networking.gke.io
    kind: GKENetworkParamSet
    name: vpc1
  type: Device
---
apiVersion: networking.gke.io/v1
kind: Network
metadata:
  name: vpc2
spec:
  parametersRef:
    group: networking.gke.io
    kind: GKENetworkParamSet
    name: vpc2
  type: Device
---
apiVersion: networking.gke.io/v1
kind: Network
metadata:
  name: vpc3
spec:
  parametersRef:
    group: networking.gke.io
    kind: GKENetworkParamSet
    name: vpc3
  type: Device
---
apiVersion: networking.gke.io/v1
kind: Network
metadata:
  name: vpc4
spec:
  parametersRef:
    group: networking.gke.io
    kind: GKENetworkParamSet
    name: vpc4
  type: Device
---
apiVersion: networking.gke.io/v1
kind: Network
metadata:
  name: vpc5
spec:
  parametersRef:
    group: networking.gke.io
    kind: GKENetworkParamSet
    name: vpc5
  type: Device
---
apiVersion: networking.gke.io/v1
kind: Network
metadata:
  name: vpc6
spec:
  parametersRef:
    group: networking.gke.io
    kind: GKENetworkParamSet
    name: vpc6
  type: Device
---
apiVersion: networking.gke.io/v1
kind: Network
metadata:
  name: vpc7
spec:
  parametersRef:
    group: networking.gke.io
    kind: GKENetworkParamSet
    name: vpc7
  type: Device
---
apiVersion: networking.gke.io/v1
kind: Network
metadata:
  name: vpc8
spec:
  parametersRef:
    group: networking.gke.io
    kind: GKENetworkParamSet
    name: vpc8
  type: Device
---
apiVersion: networking.gke.io/v1
kind: GKENetworkParamSet
metadata:
  name: vpc1
spec:
  vpc: PROJECT_ID-net-1
  vpcSubnet: PROJECT_ID-sub-1
  deviceMode: NetDevice
---
apiVersion: networking.gke.io/v1
kind: GKENetworkParamSet
metadata:
  name: vpc2
spec:
  vpc: PROJECT_ID-net-2
  vpcSubnet: PROJECT_ID-sub-2
  deviceMode: NetDevice
---
apiVersion: networking.gke.io/v1
kind: GKENetworkParamSet
metadata:
  name: vpc3
spec:
  vpc: PROJECT_ID-net-3
  vpcSubnet: PROJECT_ID-sub-3
  deviceMode: NetDevice
---
apiVersion: networking.gke.io/v1
kind: GKENetworkParamSet
metadata:
  name: vpc4
spec:
  vpc: PROJECT_ID-net-4
  vpcSubnet: PROJECT_ID-sub-4
  deviceMode: NetDevice
---
apiVersion: networking.gke.io/v1
kind: GKENetworkParamSet
metadata:
  name: vpc5
spec:
  vpc: PROJECT_ID-net-5
  vpcSubnet: PROJECT_ID-sub-5
  deviceMode: NetDevice
---
apiVersion: networking.gke.io/v1
kind: GKENetworkParamSet
metadata:
  name: vpc6
spec:
  vpc: PROJECT_ID-net-6
  vpcSubnet: PROJECT_ID-sub-6
  deviceMode: NetDevice
---
apiVersion: networking.gke.io/v1
kind: GKENetworkParamSet
metadata:
  name: vpc7
spec:
  vpc: PROJECT_ID-net-7
  vpcSubnet: PROJECT_ID-sub-7
  deviceMode: NetDevice
---
apiVersion: networking.gke.io/v1
kind: GKENetworkParamSet
metadata:
  name: vpc8
spec:
  vpc: PROJECT_ID-net-8
  vpcSubnet: PROJECT_ID-sub-8
  deviceMode: NetDevice
EOF

    These resources tell GKE to configure the NICs for GPU traffic in passthrough mode. GKE doesn't apply built-in networking programming using eBPF to this traffic.

GPUDirect-TCPX

  1. Create a cluster:

    gcloud beta container clusters create-auto CLUSTER_NAME \
    --project=PROJECT_ID \
    --location=LOCATION \
    --cluster-version=VERSION \
    --enable-multi-networking \
    --workload-policies=allow-net-admin

    Replace the following:

    • CLUSTER_NAME: the name of your new cluster.
    • VERSION: a GKE version that supports GPUDirect-TCPX, as described in Requirements.
    • LOCATION: the Compute Engine location for the cluster.

  2. Create Network and GKENetworkParamSet resources in the cluster that correspond to the VPC networks and subnetworks that you created:

    kubectl apply -f - <<EOF
apiVersion: networking.gke.io/v1
kind: Network
metadata:
  name: vpc1
spec:
  parametersRef:
    group: networking.gke.io
    kind: GKENetworkParamSet
    name: vpc1
  type: Device
---
apiVersion: networking.gke.io/v1
kind: Network
metadata:
  name: vpc2
spec:
  parametersRef:
    group: networking.gke.io
    kind: GKENetworkParamSet
    name: vpc2
  type: Device
---
apiVersion: networking.gke.io/v1
kind: Network
metadata:
  name: vpc3
spec:
  parametersRef:
    group: networking.gke.io
    kind: GKENetworkParamSet
    name: vpc3
  type: Device
---
apiVersion: networking.gke.io/v1
kind: Network
metadata:
  name: vpc4
spec:
  parametersRef:
    group: networking.gke.io
    kind: GKENetworkParamSet
    name: vpc4
  type: Device
---
apiVersion: networking.gke.io/v1
kind: GKENetworkParamSet
metadata:
  name: vpc1
spec:
  vpc: PROJECT_ID-net-1
  vpcSubnet: PROJECT_ID-sub-1
  deviceMode: NetDevice
---
apiVersion: networking.gke.io/v1
kind: GKENetworkParamSet
metadata:
  name: vpc2
spec:
  vpc: PROJECT_ID-net-2
  vpcSubnet: PROJECT_ID-sub-2
  deviceMode: NetDevice
---
apiVersion: networking.gke.io/v1
kind: GKENetworkParamSet
metadata:
  name: vpc3
spec:
  vpc: PROJECT_ID-net-3
  vpcSubnet: PROJECT_ID-sub-3
  deviceMode: NetDevice
---
apiVersion: networking.gke.io/v1
kind: GKENetworkParamSet
metadata:
  name: vpc4
spec:
  vpc: PROJECT_ID-net-4
  vpcSubnet: PROJECT_ID-sub-4
  deviceMode: NetDevice
EOF

    These resources tell GKE to configure the NICs for GPU traffic in passthrough mode. GKE doesn't apply built-in networking programming using eBPF to this traffic.

Install the GPUDirect binary and configure NCCL

This section shows you how to install the GPUDirect binary, based on your A3 machine type (GPUDirect-TCPX for A3 High, GPUDirect-TCPXO for A3 Mega) and a specific NCCL library version using a DaemonSet.

GPUDirect-TCPXO

This DaemonSet does the following:

  1. Pre-installation to setup GPUDirect-TCPXO related configurations.
  2. Installs the NCCL library and GPUDirect-TCPXO binary on the node.
  3. Stores the library and the binary in the /home/kubernetes/bin/nvidia/lib64 directory on the VM. By default, GKE mounts this directory into the /usr/local/nvidia/lib64 path in GPU containers that need to use NCCL and GPUDirect-TCPXO.

To install the binary and configure NCCL, do the following steps:

  1. Review the nccl-tcpxo-installer-autopilot.yaml Daemonset manifest in GitHub.

  2. Create a dedicated namespace:

    kubectl create ns gpudirect-system

  3. Deploy the DaemonSet:

    kubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/master/gpudirect-tcpxo/nccl-tcpxo-installer-autopilot.yaml

    The NCCL plugin takes approximately two minutes to start running.

GPUDirect-TCPX

This DaemonSet does the following:

  1. Installs the NCCL library and GPUDirect-TCPX binary on the node.
  2. Stores the library and the binary in the /home/kubernetes/bin/nvidia/lib64 directory on the VM. By default, GKE mounts this directory into the /usr/local/nvidia/lib64 path in GPU containers that need to use NCCL and GPUDirect-TCPX.

To install the binary and configure NCCL, do the following:

  1. Review the nccl-tcpx-installer-autopilot.yaml Daemonset manifest in GitHub.

  2. Create a dedicated namespace:

    kubectl create ns gpudirect-system

  3. Deploy the DaemonSet:

    kubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/master/gpudirect-tcpx/nccl-tcpx-installer-autopilot.yaml

    The NCCL plugin takes approximately two minutes to start running.

Deploy NRI device injector plugin

This section shows you how to install the NRI device injector by using a DaemonSet. Both H100 GPU machine types install the same NRI device injector plugin. This plugin does the following:

  1. Enables Node Resource Interface (NRI) on the node that has H100 GPUs. NRI is enabled by default on GKE version 1.29 and later.
  2. Deploys a NRI device injector plugin container that injects GPU devices into containers specified by Pod annotations.

To install the plugin, do the following:

  1. Review the nri-device-injector-autopilot.yaml Deployment manifest in GitHub.

  2. Deploy the DaemonSet:

    kubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/master/nri_device_injector/nri-device-injector-autopilot.yaml

    The NCCL plugin takes approximately two minutes to start running.

Deploy a test workload

In this section, you deploy a sample workload to verify that NCCL and GPUDirect-TCPX or GPUDirect-TCPXO work as expected. This sample workload does the following:

  1. Deploys two Pods, each of which runs in a node that has H100 GPUs.
  2. Deploys a sidecar container in each Pod to let those Pods use GPUDirect-TCPXO or GPUDirect-TCPX.

To deploy this sample workload, do the following:

GPUDirect-TCPXO

This workload includes a sidecar container named the tcpxo-daemon, which runs a service that lets the Pod use GPUDirect-TCPXO. You must add this sidecar container to any Pods in your own environment that need to use GPUDirect-TCPXO. For a snippet of the required fields to add to your manifests, see Add GPUDirect to your manifest.

  1. Review the nccl-test-latest-autopilot.yaml manifest in GitHub.

  2. Deploy two Pods with the test workload:

    kubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/master/gpudirect-tcpxo/nccl-test-latest-autopilot.yaml

  3. After the Pods deploy, trigger an all-gather test:

    kubectl exec --stdin --tty --container=nccl-test nccl-test-host-1 -- /scripts/allgather.sh nccl-host-1 nccl-host-2

    The output is similar to the following:

    #                                                              out-of-place                       in-place
#        size         count      type   redop    root     time   algbw   busbw #wrong     time   algbw   busbw #wrong
#         (B)    (elements)                               (us)  (GB/s)  (GB/s)            (us)  (GB/s)  (GB/s)
            0             0     float    none      -1     0.24    0.00    0.00      0     0.18    0.00    0.00      0
            0             0     float    none      -1     0.19    0.00    0.00      0     0.17    0.00    0.00      0
            0             0     float    none      -1     0.17    0.00    0.00      0     0.17    0.00    0.00      0
            0             0     float    none      -1     0.17    0.00    0.00      0     0.17    0.00    0.00      0
            0             0     float    none      -1     0.17    0.00    0.00      0     0.17    0.00    0.00      0
          256             4     float    none      -1    235.2    0.00    0.00      0    235.1    0.00    0.00      0
          512             8     float    none      -1    241.0    0.00    0.00      0    236.1    0.00    0.00      0
         1024            16     float    none      -1    236.3    0.00    0.00      0    233.3    0.00    0.00      0
         2048            32     float    none      -1    234.1    0.01    0.01      0    233.4    0.01    0.01      0
         4096            64     float    none      -1    237.1    0.02    0.02      0    235.3    0.02    0.02      0
         8192           128     float    none      -1    236.2    0.03    0.03      0    235.2    0.03    0.03      0
        16384           256     float    none      -1    236.6    0.07    0.06      0    238.5    0.07    0.06      0
        32768           512     float    none      -1    237.9    0.14    0.13      0    238.8    0.14    0.13      0
        65536          1024     float    none      -1    242.3    0.27    0.25      0    239.4    0.27    0.26      0
       131072          2048     float    none      -1    263.0    0.50    0.47      0    275.1    0.48    0.45      0
       262144          4096     float    none      -1    279.2    0.94    0.88      0    269.9    0.97    0.91      0
       524288          8192     float    none      -1    273.5    1.92    1.80      0    273.5    1.92    1.80      0
      1048576         16384     float    none      -1    315.1    3.33    3.12      0    314.1    3.34    3.13      0
      2097152         32768     float    none      -1    319.2    6.57    6.16      0    311.5    6.73    6.31      0
      4194304         65536     float    none      -1    331.8   12.64   11.85      0    331.3   12.66   11.87      0
      8388608        131072     float    none      -1    356.3   23.54   22.07      0    353.8   23.71   22.23      0
     16777216        262144     float    none      -1    409.1   41.01   38.45      0    405.2   41.40   38.81      0
     33554432        524288     float    none      -1    451.4   74.34   69.69      0    447.7   74.94   70.26      0
     67108864       1048576     float    none      -1    713.4   94.07   88.19      0    713.8   94.01   88.13      0
    134217728       2097152     float    none      -1   1122.1  119.62  112.14      0   1116.3  120.23  112.72      0
    268435456       4194304     float    none      -1   1785.8  150.32  140.92      0   1769.2  151.72  142.24      0
    536870912       8388608     float    none      -1   2859.7  187.74  176.00      0   2852.6  188.20  176.44      0
   1073741824      16777216     float    none      -1   5494.1  195.44  183.22      0   5568.2  192.83  180.78      0
   2147483648      33554432     float    none      -1    10841  198.09  185.71      0    10798  198.88  186.45      0
   4294967296      67108864     float    none      -1    21453  200.21  187.70      0    21490  199.86  187.37      0
   8589934592     134217728     float    none      -1    42603  201.63  189.03      0    42670  201.31  188.73      0
# Out of bounds values : 0 OK
# Avg bus bandwidth    : 45.7587
#

GPUDirect-TCPX

This workload includes a sidecar container named the tcpx-daemon, which runs a service that lets the Pod use GPUDirect-TCPX. You must add this sidecar container to any Pods in your own environment that need to use GPUDirect-TCPX. For a snippet of the required fields to add to your manifests, see Add GPUDirect to your manifest.

  1. Review the nccl-config.yaml ConfigMap manifest in GitHub. This manifest deploys scripts that initialize an NCCL all-gather test and sets NCCL-specific configuration settings.

  2. Review the nccl-test-latest-autopilot.yaml Deployment manifest in GitHub.

  3. Deploy the ConfigMap and the test workload:

    kubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/master/gpudirect-tcpx/nccl-config.yaml
kubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/master/gpudirect-tcpx/nccl-test-latest-autopilot.yaml

  4. Run the following commands to trigger an NCCL all-gather test for the nodes:

    kubectl exec \
  --stdin --tty --container=nccl-test nccl-test-host-1 \
  -- /configs/allgather.sh nccl-host-1 nccl-host-2

    The output is similar to the following:

    #                                                              out-of-place                       in-place
#       size         count      type   redop    root     time   algbw   busbw #wrong     time   algbw   busbw #wrong
#        (B)    (elements)                               (us)  (GB/s)  (GB/s)            (us)  (GB/s)  (GB/s)
    1048576         16384     float    none      -1    696.8    1.50    1.41      0    729.0    1.44    1.35      0
    2097152         32768     float    none      -1    776.4    2.70    2.53      0    726.7    2.89    2.71      0
    4194304         65536     float    none      -1    774.3    5.42    5.08      0    805.1    5.21    4.88      0
    8388608        131072     float    none      -1    812.1   10.33    9.68      0    817.6   10.26    9.62      0
   16777216        262144     float    none      -1   1035.2   16.21   15.19      0   1067.8   15.71   14.73      0
   33554432        524288     float    none      -1   1183.3   28.36   26.59      0   1211.8   27.69   25.96      0
   67108864       1048576     float    none      -1   1593.4   42.12   39.49      0   1510.5   44.43   41.65      0
  134217728       2097152     float    none      -1   2127.8   63.08   59.13      0   2312.7   58.03   54.41      0
  268435456       4194304     float    none      -1   3603.0   74.50   69.85      0   3586.2   74.85   70.17      0
  536870912       8388608     float    none      -1   7101.7   75.60   70.87      0   7060.9   76.03   71.28      0
# Out of bounds values : 0 OK
# Avg bus bandwidth    : 29.8293

Use required NCCL configuration settings to improve performance

The following key-value pairs are the required NCCL configuration settings for GPUDirect-TCPX and GPUDirect-TCPXO. When deploying your workloads that use NCCL, set them as environment variables to optimize performance.

GPUDirect-TCPXO

## required

"NCCL_FASTRAK_CTRL_DEV=eth0",
"NCCL_FASTRAK_IFNAME=eth1,eth2,eth3,eth4,eth5,eth6,eth7,eth8",
"NCCL_SOCKET_IFNAME=eth0",
"NCCL_CROSS_NIC=0",
"NCCL_ALGO=Ring,Tree",
"NCCL_PROTO=Simple",
"NCCL_MIN_NCHANNELS=4",
"NCCL_TUNER_PLUGIN=libnccl-tuner.so",
"NCCL_TUNER_CONFIG_PATH=/usr/local/nvidia/lib64/a3plus_tuner_config.textproto",
"NCCL_SHIMNET_GUEST_CONFIG_CHECKER_CONFIG_FILE=/usr/local/nvidia/lib64/a3plus_guest_config.textproto",
"NCCL_DYNAMIC_CHUNK_SIZE=524288",
"NCCL_P2P_NET_CHUNKSIZE=524288",
"NCCL_P2P_PCI_CHUNKSIZE=524288",
"NCCL_P2P_NVL_CHUNKSIZE=1048576",
"NCCL_FASTRAK_NUM_FLOWS=2",
"NCCL_FASTRAK_USE_SNAP=1",
"NCCL_FASTRAK_PLUGIN_ACCEPT_TIMEOUT_MS=600000",
"NCCL_FASTRAK_ENABLE_CONTROL_CHANNEL=0",
"NCCL_BUFFSIZE=8388608",
"CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7",
"NCCL_NET_GDR_LEVEL=PIX",
"NCCL_FASTRAK_ENABLE_HOTPATH_LOGGING=0",
"NCCL_FASTRAK_USE_LLCM=1",
"NCCL_NVLS_ENABLE=0"
## recommended, to log NCCL errors
"NCCL_DEBUG=WARN",
"NCCL_DEBUG_SUBSYS=INIT,NET,ENV,COLL,GRAPH"

Optionally, you can set all the configurations at once by following these steps:

  1. Add the following key-value pair as an environment variable in your workload container manifest:

    NCCL_LIB_DIR="/usr/local/nvidia/lib64"

  2. Ensure the nccl-env-profile.sh script is executed when your workload container starts. For example, you can do this in your Pod specification by overriding the container's command to include the following:

    source ${NCCL_LIB_DIR}/nccl-env-profile.sh

GPUDirect-TCPX


"NCCL_SOCKET_IFNAME=\"eth0\"",
"NCCL_ALGO=Ring",
"NCCL_PROTO=Simple",
"NCCL_CROSS_NIC=0",
"NCCL_NET_GDR_LEVEL=PIX",
"NCCL_P2P_PXN_LEVEL=0",
"NCCL_GPUDIRECTTCPX_SOCKET_IFNAME=eth1,eth2,eth3,eth4",
"NCCL_GPUDIRECTTCPX_CTRL_DEV=eth0",
"NCCL_DYNAMIC_CHUNK_SIZE=524288",
"NCCL_P2P_NET_CHUNKSIZE=524288",
"NCCL_P2P_PCI_CHUNKSIZE=524288",
"NCCL_P2P_NVL_CHUNKSIZE=1048576",
"NCCL_BUFFSIZE=4194304",
"NCCL_NSOCKS_PERTHREAD=4",
"NCCL_SOCKET_NTHREADS=1",
"NCCL_GPUDIRECTTCPX_TX_BINDINGS=\"eth1:8-21,112-125;eth2:8-21,112-125;eth3:60-73,164-177;eth4:60-73,164-177\"",
"NCCL_GPUDIRECTTCPX_RX_BINDINGS=\"eth1:22-35,126-139;eth2:22-35,126-139;eth3:74-87,178-191;eth4:74-87,178-191\"",
"NCCL_GPUDIRECTTCPX_PROGRAM_FLOW_STEERING_WAIT_MICROS=500000"

Add GPUDirect to your manifests

This section shows the required fields that you must add to your Kubernetes manifests for your Pods to use GPUDirect.

For Autopilot mode, you must also select the appropriate GPUs in your Pod manifests so that GKE provisions the hardware. For H100 Mega GPUs, use GPUDirect-TCPXO. For H100 GPUs, use GPUDirect-TCPX.

Add the following node selectors to your Pod:

nodeSelector:
  cloud.google.com/gke-accelerator: GPU_NAME
  cloud.google.com/gke-gpu-driver-version: latest

Replace GPU_NAME with the name of the GPU. Supported values are as follows:

  • nvidia-h100-mega-80gb
  • nvidia-h100-80gb

Depending on the type of GPUDirect, do the following:

GPUDirect-TCPXO

  1. Add the following annotations to the Pod metadata. 

    metadata:
  annotations:
    devices.gke.io/container.tcpxo-daemon: |+
      - path: /dev/nvidia0
      - path: /dev/nvidia1
      - path: /dev/nvidia2
      - path: /dev/nvidia3
      - path: /dev/nvidia4
      - path: /dev/nvidia5
      - path: /dev/nvidia6
      - path: /dev/nvidia7
      - path: /dev/nvidiactl
      - path: /dev/nvidia-uvm
      - path: /dev/dmabuf_import_helper
    networking.gke.io/default-interface: 'eth0'
    networking.gke.io/interfaces: |
      [
        {"interfaceName":"eth0","network":"default"},
        {"interfaceName":"eth1","network":"vpc1"},
        {"interfaceName":"eth2","network":"vpc2"},
        {"interfaceName":"eth3","network":"vpc3"},
        {"interfaceName":"eth4","network":"vpc4"},
        {"interfaceName":"eth5","network":"vpc5"},
        {"interfaceName":"eth6","network":"vpc6"},
        {"interfaceName":"eth7","network":"vpc7"},
        {"interfaceName":"eth8","network":"vpc8"}
      ]

  2. Add the following fields to the Pod specification:

    spec:
  volumes:
  - name: libraries
    hostPath:
      path: /home/kubernetes/bin/nvidia/lib64
  - name: sys
    hostPath:
      path: /sys
  - name: proc-sys
    hostPath:
      path: /proc/sys
  - name: aperture-devices
    hostPath:
      path: /dev/aperture_devices

  3. Add the following container to the manifest to run the tcpxo-daemon service. Replace (TCPXO_DAEMON_IMAGE) with the latest image us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpxo/tcpgpudmarxd-dev:v1.0.11:

    - name: tcpxo-daemon
  image: TCPXO_DAEMON_IMAGE
  imagePullPolicy: Always
  command: ["/bin/sh", "-c"]
  args:
    - |
      set -ex
      chmod 755 /fts/entrypoint_rxdm_container.sh
      /fts/entrypoint_rxdm_container.sh --num_hops=2 --num_nics=8 --uid= --alsologtostderr
  securityContext:
    capabilities:
      add:
        - NET_ADMIN
        - NET_BIND_SERVICE
  volumeMounts:
    - name: libraries
      mountPath: /usr/local/nvidia
    - name: sys
      mountPath: /hostsysfs
    - name: proc-sys
      mountPath: /hostprocsysfs

  4. Add the following environment variable to every GPU container:

    env:

- name: NCCL_FASTRAK_LLCM_DEVICE_DIRECTORY
  value: /dev/aperture_devices

  5. Add the following volumeMounts to every GPU container. Without aperture_devices setups, privileged:true is required for GPU containers:

    volumeMounts:
  - name: aperture-devices
    mountPath: /dev/aperture_devices

  6. Add environment variables to configure NCCL options. For details, see Use recommended NCCL configuration settings to improve performance.

A completed Pod specification looks like the following:

apiVersion: v1
kind: Pod
metadata:
name: a3plus-workloads
annotations:
  devices.gke.io/container.tcpxo-daemon: |+
    - path: /dev/nvidia0
    - path: /dev/nvidia1
    - path: /dev/nvidia2
    - path: /dev/nvidia3
    - path: /dev/nvidia4
    - path: /dev/nvidia5
    - path: /dev/nvidia6
    - path: /dev/nvidia7
    - path: /dev/nvidiactl
    - path: /dev/nvidia-uvm
    - path: /dev/dmabuf_import_helper
  networking.gke.io/default-interface: 'eth0'
  networking.gke.io/interfaces: |
    [
      {"interfaceName":"eth0","network":"default"},
      {"interfaceName":"eth1","network":"vpc1"},
      {"interfaceName":"eth2","network":"vpc2"},
      {"interfaceName":"eth3","network":"vpc3"},
      {"interfaceName":"eth4","network":"vpc4"},
      {"interfaceName":"eth5","network":"vpc5"},
      {"interfaceName":"eth6","network":"vpc6"},
      {"interfaceName":"eth7","network":"vpc7"},
      {"interfaceName":"eth8","network":"vpc8"}
    ]
...
containers:
  - name: tcpxo-daemon
    image: TCPXO_DAEMON_IMAGE
    imagePullPolicy: Always
    command: ["/bin/sh", "-c"]
    args:
      - |
        set -ex
        chmod 755 /fts/entrypoint_rxdm_container.sh
        /fts/entrypoint_rxdm_container.sh --num_hops=2 --num_nics=8 --uid= --alsologtostderr
    securityContext:
      capabilities:
        add:
          - NET_ADMIN
          - NET_BIND_SERVICE
    volumeMounts:
      - name: libraries
        mountPath: /usr/local/nvidia
      - name: sys
        mountPath: /hostsysfs
      - name: proc-sys
        mountPath: /hostprocsysfs
    
  - name: main-application-container
...
   
      - name: NCCL_FASTRAK_LLCM_DEVICE_DIRECTORY
        value: /dev/aperture_devices
    securityContext:
    volumeMounts:
      - name: aperture-devices
        mountPath: /dev/aperture_devices
    resources:
      limits:
        nvidia.com/gpu: 8
volumes:
  - name: libraries
    hostPath:
      path: /home/kubernetes/bin/nvidia
  - name: sys
    hostPath:
      path: /sys
  - name: proc-sys
    hostPath:
      path: /proc/sys
  - name: aperture-devices
    hostPath:
      path: /dev/aperture_devices

GPUDirect-TCPX

  1. Add the following annotations to the Pod metadata. 

    metadata:
  annotations:
    devices.gke.io/container.tcpx-daemon: |+
      - path: /dev/nvidia0
      - path: /dev/nvidia1
      - path: /dev/nvidia2
      - path: /dev/nvidia3
      - path: /dev/nvidia4
      - path: /dev/nvidia5
      - path: /dev/nvidia6
      - path: /dev/nvidia7
      - path: /dev/nvidiactl
      - path: /dev/nvidia-uvm
    networking.gke.io/default-interface: 'eth0'
    networking.gke.io/interfaces: |
      [
        {"interfaceName":"eth0","network":"default"},
        {"interfaceName":"eth1","network":"vpc1"},
        {"interfaceName":"eth2","network":"vpc2"},
        {"interfaceName":"eth3","network":"vpc3"},
        {"interfaceName":"eth4","network":"vpc4"},
      ]

  2. Add the following fields to the Pod specification:

    spec:
  volumes:
  - name: libraries
    hostPath:
      path: /home/kubernetes/bin/nvidia/lib64
  - name: sys
    hostPath:
      path: /sys
  - name: proc-sys
    hostPath:
      path: /proc/sys

  3. Add the following container to the manifest to run the tcpx-daemon service:

    - name: tcpx-daemon
  image: us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpx/tcpgpudmarxd-dev:v2.0.9
  command:
    - /tcpgpudmarxd/build/app/tcpgpudmarxd
    - --gpu_nic_preset
    - a3vm
    - --gpu_shmem_type
    - fd
    - --uds_path
    - /run/tcpx
    - --setup_param
    - \"--verbose 128 2 0 \"
  securityContext:
    capabilities:
        add:
          - NET_ADMIN
  volumeMounts:
    - name: libraries
      mountPath: /usr/local/nvidia/lib64
    - name: tcpx-socket
      mountPath: /run/tcpx
    - name: sys
      mountPath: /hostsysfs
    - name: proc-sys
      mountPath: /hostprocsysfs

  4. Add the following volume mounts to any containers that request GPUs:

    volumeMounts:
- name: tcpx-socket
  mountPath: /tmp
- name: libraries
  mountPath: /usr/local/nvidia/lib64

  5. Add environment variables to configure NCCL options. For details, see the Use recommended NCCL configuration settings to improve performance section in this document.

A completed Pod specification looks like the following:

apiVersion: v1
kind: Pod
metadata:
name: a3-gpu-workloads-example
labels:
  name: a3-gpu-workloads-example
annotations:
  devices.gke.io/container.tcpx-daemon: |+
        - path: /dev/nvidia0
        - path: /dev/nvidia1
        - path: /dev/nvidia2
        - path: /dev/nvidia3
        - path: /dev/nvidia4
        - path: /dev/nvidia5
        - path: /dev/nvidia6
        - path: /dev/nvidia7
        - path: /dev/nvidiactl
        - path: /dev/nvidia-uvm
  networking.gke.io/default-interface: 'eth0'
  networking.gke.io/interfaces: |
    [
      {"interfaceName":"eth0","network":"default"},
      {"interfaceName":"eth1","network":"vpc1"},
      {"interfaceName":"eth2","network":"vpc2"},
      {"interfaceName":"eth3","network":"vpc3"},
      {"interfaceName":"eth4","network":"vpc4"}
    ]
spec:
containers:
  - name: tcpx-daemon
    image: us-docker.pkg.dev/gce-ai-infra/gpudirect-tcpx/tcpgpudmarxd-dev:v2.0.11
    imagePullPolicy: Always
    command:
      - /tcpgpudmarxd/build/app/tcpgpudmarxd
      - --gpu_nic_preset
      - a3vm
      - --gpu_shmem_type
      - fd
      - --uds_path
      - /run/tcpx
      - --setup_param
      - \"--verbose 128 2 0 \"
    securityContext:
capabilities:
        add:
          - NET_ADMIN
    volumeMounts:
      - name: libraries
        mountPath: /usr/local/nvidia/lib64
        readOnly: true
      - name: tcpx-socket
        mountPath: /run/tcpx
      - name: sys
        mountPath: /hostsysfs
      - name: proc-sys
        mountPath: /hostprocsysfs
    
  - name: a3-gpu-workloads-example
    ...
    volumeMounts:
      - name: tcpx-socket
        mountPath: /tmp
      - name: libraries
        mountPath: /usr/local/nvidia/lib64
        readOnly: true
    resources:
      limits:
        nvidia.com/gpu: 8
    
...
volumes:
  - name: libraries
    hostPath:
      path: /home/kubernetes/bin/nvidia/lib64
  - name: tcpx-socket
    emptyDir:
  - name: sys
    hostPath:
      path: /sys
  - name: proc-sys
    hostPath:
      path: /proc/sys

What's next