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Parallelstore high-performance file service for HPC and AI is GA | Google Cloud Blog
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data-p="%.@."cloudblog","products/storage-data-transfer/parallelstore-high-performance-file-service-for-hpc-and-ai-is-ga","en"]" data-node-index="0;0" jsmodel="hc6Ubd" view c-wiz data-ogpc><div class="T4LgNb " jsname="a9kxte"><div jsname="qJTHM" class="kFwPee"><article class="nRhiJb-qJTHM" jsaction="rcuQ6b:npT2md" jscontroller="kxO7ab"><section class="nRhiJb-DARUcf"><div class="Wdmc0c nRhiJb-DbgRPb-wNfPc-cGMI2b"><div class="Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-qWD73c nRhiJb-BFbNVe-r8s4j-bMElCd dIsJJe" track-name="storage & data transfer"track-type="tag">Storage & Data Transfer</div><div class="nRhiJb-ObfsIf"><div class="nRhiJb-kR0ZEf-OWXEXe-GV1x9e-R6PoUb"></div><div class="nRhiJb-kR0ZEf-OWXEXe-GV1x9e-EehZO nRhiJb-fmcmS-oXtfBe"><h1 class="Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-ibL1re"><div class="Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-ibL1re"><span class="FewWi"></span>Parallelstore is now GA, fueling the next generation of AI and HPC workloads</div></h1></div></div><div class="nRhiJb-fmcmS-oXtfBe dEogG">October 5, 2024</div></div></section><div class="EKklye"><div class="nRhiJb-DARUcf ZWw7T"><div class="npzWPc"><div class="dzoHJ"><div class="nRhiJb-DX2B6 nRhiJb-DX2B6-OWXEXe-h30Snd"><div class="nRhiJb-j5y3u"><ul class="nRhiJb-Qijihe phRaUe" role="list"><li class="hpHPGf"><a class="nRhiJb-ARYxNe" href="https://x.com/intent/tweet?text=Parallelstore%20is%20now%20GA,%20fueling%20the%20next%20generation%20of%20AI%20and%20HPC%20workloads%20@googlecloud&url=https://cloud.google.com/blog/products/storage-data-transfer/parallelstore-high-performance-file-service-for-hpc-and-ai-is-ga" track-name="x"track-type="social share"track-metadata-eventdetail="x.com/intent/tweet?text=Parallelstore is now GA, fueling the next generation of AI and HPC workloads%20@googlecloud&url=cloud.google.com/blog/products/storage-data-transfer/parallelstore-high-performance-file-service-for-hpc-and-ai-is-ga"track-metadata-module="social icons" target="_blank" rel="noopener"><svg 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track-metadata-module="left rail"track-metadata-module_headline="google cloud summit series" track-name="register"track-type="button"track-metadata-position="cta"track-metadata-eventdetail="cloudonair.withgoogle.com/summit_series_24?utm_source=cgc-blog&utm_medium=blog&utm_campaign=summit-series-2024&utm_content=summit-series-left-hand-rail-cta&utm_term=-">Register </a></div></div><div class="OYL9D nRhiJb-kR0ZEf-OWXEXe-GV1x9e-OiUrBf" jsname="tx2NYc"><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><p><span style="vertical-align:baseline">Organizations use artificial intelligence (AI) and high-performance computing (HPC) applications to process massive datasets, run complex simulations, and train generative models with billions of parameters for diverse use cases such as LLMs, genomic analysis, quantitative analysis, or real-time sports analytics. These workloads place big performance demands on their storage systems, requiring high throughput and I/O performance that scales and that maintains sub-millisecond latencies, even when thousands of clients are concurrently reading and writing the same shared files.</span></p> <p><span style="vertical-align:baseline">To power these next-generation AI and HPC workloads, we </span><a href="https://cloud.google.com/blog/products/storage-data-transfer/storage-announcements-at-next24"><span style="text-decoration:underline;vertical-align:baseline">announced</span></a><span style="vertical-align:baseline"> Parallelstore at Google Cloud Next 2024, and today, we are excited to announce that it is now generally available. Built on the </span><a href="https://docs.daos.io/v2.6/" rel="noopener" target="_blank"><span style="text-decoration:underline;vertical-align:baseline">Distributed Asynchronous Object Storage (DAOS) architecture</span></a><span style="vertical-align:baseline">, </span><a href="https://cloud.google.com/parallelstore"><span style="text-decoration:underline;vertical-align:baseline">Parallelstore</span></a><span style="vertical-align:baseline"> combines a fully distributed metadata and key-value architecture to deliver high-performance throughput and IOPS.</span></p> <p><span style="vertical-align:baseline">Read on to learn how Parallelstore serves the needs of complex AI and HPC workloads, allowing you to maximize </span><a href="https://cloud.google.com/blog/products/ai-machine-learning/goodput-metric-as-measure-of-ml-productivity"><span style="text-decoration:underline;vertical-align:baseline">goodput</span></a><span style="vertical-align:baseline"> and GPU/TPU utilization, programmatically move data in and out of Parallelstore, and provision </span><a href="https://cloud.google.com/kubernetes-engine"><span style="text-decoration:underline;vertical-align:baseline">Google Kubernetes Engine</span></a><span style="vertical-align:baseline"> and </span><a href="https://cloud.google.com/products/compute"><span style="text-decoration:underline;vertical-align:baseline">Compute Engine</span></a><span style="vertical-align:baseline"> resources.</span></p> <h3><strong style="vertical-align:baseline">Maximize goodput and GPU/TPU utilization</strong></h3> <p><span style="vertical-align:baseline">To overcome the performance limitations of traditional parallel file systems, Parallelstore uses a distributed metadata management system and a key-value store architecture. Parallelstore’s high-throughput parallel data access minimizes latency and I/O bottlenecks, and allows it to saturate the network bandwidth of individual compute clients. This efficient data delivery maximizes goodput to GPUs and TPUs, a critical factor for optimizing AI workload costs. Parallelstore can also provide continuous read/write access to thousands of VMs, GPUs and TPUs, satisfying modest-to-massive AI and HPC workload requirements. </span></p> <p><span style="vertical-align:baseline">For a 100 TiB deployment, the maximum Parallelstore deployment, throughput scales to ~115 GiB/s, ~3 million read IOPS, ~1 million write IOPS, and a low-latency of ~0.3 ms. This means that Parallelstore is also a good platform for small files and random, distributed access across a large number of clients. For AI use cases, Parallelstore’s performance with small files and metadata operations enables up to 3.9x faster training times and up to 3.7x higher training throughput compared to native ML framework data loaders, as measured by Google Cloud benchmarking.</span></p> <h3><strong style="vertical-align:baseline">Programmatically move data in and out of Parallelstore </strong></h3> <p><span style="vertical-align:baseline">Many AI and HPC workloads store data in </span><a href="https://cloud.google.com/storage"><span style="text-decoration:underline;vertical-align:baseline">Cloud Storage</span></a><span style="vertical-align:baseline"> for data preparation or archiving. You can use Parallelstore’s integrated import/export API to automate movement of the data you’d like to import to Parallelstore for processing. With the API, you can ingest massive datasets from Cloud Storage into Parallelstore at ~20GB/s for files larger than 32MB, and at ~5,000 files per second for files under 32MB.</span></p></span></section><section class="nRhiJb-DbgRPb-II5mzb-cGMI2b"><section><div class="kg99be" jscontroller="vkZm2d" jsaction="rcuQ6b:npT2md" data-text-to-copy="gcloud alpha parallelstore instances import-data $INSTANCE_ID --location=$LOCATION --source-gcs-bucket-uri=gs://$BUCKET_NAME [--destination-parallelstore-path="/"] --project= $PROJECT_ID"><div jsaction="click:VTVhdd" class="GaRnv"><svg class="" viewBox="0 0 20 20" role="presentation" aria-hidden="true" height=20 widdht=20><path d="M7.979 14.667c-.458 0-.84-.153-1.146-.459a1.53 1.53 0 0 1-.458-1.125V3.938c0-.459.153-.84.458-1.146.306-.306.688-.459 1.146-.459h6.625c.445 0 .823.156 1.136.469.312.313.468.691.468 1.136v9.145c0 .445-.156.82-.468 1.125a1.564 1.564 0 0 1-1.136.459H7.979Zm0-1.334h6.625c.083 0 .15-.024.198-.073a.239.239 0 0 0 .073-.177V3.938a.265.265 0 0 0-.073-.198.268.268 0 0 0-.198-.073H7.979a.268.268 0 0 0-.198.073.266.266 0 0 0-.073.198v9.145c0 .07.024.129.073.177a.267.267 0 0 0 .198.073ZM4.875 17.75a1.53 1.53 0 0 1-1.125-.458 1.532 1.532 0 0 1-.458-1.125V5.729h1.333v10.438c0 .07.024.128.073.177a.24.24 0 0 0 .177.073h7.937v1.333H4.875ZM7.708 3.667v9.666-9.666Z" fill="#1a73e8"/></svg></div><div jscontroller="M2lJBf" jsaction="rcuQ6b:npT2md" data-enable-content-editable-input-style="false" data-enable-line-wrapping data-read-only><div class="hg3Lgc qs41qe BEXrzd" data-loadingmessage="Loading..." jscontroller="GFartf" jsaction="animationend:kWijWc;dyRcpb:dyRcpb" data-active="true" jsname="aZ2wEe"><div class="eBrXtc" aria-live="polite" jsname="vyyg5">Loading...</div><div jsname="Hxlbvc" class="JdM54e"><div class="aopPX ZqnFk co39ub"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX fxjES Cn087"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX ZHXbZe hfsr6b"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX fDBOYb EjXFBf"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div></div></div><div dir="ltr"><textarea jsname="bqeLof" class="Fdco1c">gcloud alpha parallelstore instances import-data $INSTANCE_ID --location=$LOCATION --source-gcs-bucket-uri=gs://$BUCKET_NAME [--destination-parallelstore-path="/"] --project= $PROJECT_ID</textarea></div></div></div><p class="nRhiJb-cHYyed nRhiJb-DbgRPb-R6PoUb-ma6Yeb TxA6Xc"><p>Figure 1: Parallelstore Import gCloud API</p></p></section></section><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><p><span style="vertical-align:baseline">When an AI training job or HPC workload is complete, you can export results programmatically to Cloud Storage for further assessment or longer-term storage. You can also automate data transfers via the API, minimizing manual intervention and streamlining data pipelines.</span></p></span></section><section class="nRhiJb-DbgRPb-II5mzb-cGMI2b"><section><div class="kg99be" jscontroller="vkZm2d" jsaction="rcuQ6b:npT2md" data-text-to-copy="gcloud alpha parallelstore instances export-data $INSTANCE_ID --location=$LOCATION --destination-gcs-bucket-uri=gs://$BUCKET_NAME [--source-parallelstore-path="/"]"><div jsaction="click:VTVhdd" class="GaRnv"><svg class="" viewBox="0 0 20 20" role="presentation" aria-hidden="true" height=20 widdht=20><path d="M7.979 14.667c-.458 0-.84-.153-1.146-.459a1.53 1.53 0 0 1-.458-1.125V3.938c0-.459.153-.84.458-1.146.306-.306.688-.459 1.146-.459h6.625c.445 0 .823.156 1.136.469.312.313.468.691.468 1.136v9.145c0 .445-.156.82-.468 1.125a1.564 1.564 0 0 1-1.136.459H7.979Zm0-1.334h6.625c.083 0 .15-.024.198-.073a.239.239 0 0 0 .073-.177V3.938a.265.265 0 0 0-.073-.198.268.268 0 0 0-.198-.073H7.979a.268.268 0 0 0-.198.073.266.266 0 0 0-.073.198v9.145c0 .07.024.129.073.177a.267.267 0 0 0 .198.073ZM4.875 17.75a1.53 1.53 0 0 1-1.125-.458 1.532 1.532 0 0 1-.458-1.125V5.729h1.333v10.438c0 .07.024.128.073.177a.24.24 0 0 0 .177.073h7.937v1.333H4.875ZM7.708 3.667v9.666-9.666Z" fill="#1a73e8"/></svg></div><div jscontroller="M2lJBf" jsaction="rcuQ6b:npT2md" data-enable-content-editable-input-style="false" data-enable-line-wrapping data-read-only><div class="hg3Lgc qs41qe BEXrzd" data-loadingmessage="Loading..." jscontroller="GFartf" jsaction="animationend:kWijWc;dyRcpb:dyRcpb" data-active="true" jsname="aZ2wEe"><div class="eBrXtc" aria-live="polite" jsname="vyyg5">Loading...</div><div jsname="Hxlbvc" class="JdM54e"><div class="aopPX ZqnFk co39ub"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX fxjES Cn087"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX ZHXbZe hfsr6b"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX fDBOYb EjXFBf"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div></div></div><div dir="ltr"><textarea jsname="bqeLof" class="Fdco1c">gcloud alpha parallelstore instances export-data $INSTANCE_ID --location=$LOCATION --destination-gcs-bucket-uri=gs://$BUCKET_NAME [--source-parallelstore-path="/"]</textarea></div></div></div><p class="nRhiJb-cHYyed nRhiJb-DbgRPb-R6PoUb-ma6Yeb TxA6Xc"><p>Figure 2: Parallelstore Export gCloud API</p></p></section></section><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><h3><strong style="vertical-align:baseline">Programmatically provision GKE resources through the CSI driver</strong></h3> <p><span style="vertical-align:baseline">It’s easy to efficiently manage high-performance storage for containerized workloads through Parallelstores’ GKE CSI driver. You can dynamically provision and manage Parallelstore file systems as persistent volumes or access existing Parallelstore instances in Kubernetes workloads, directly within your GKE clusters using familiar Kubernetes APIs. This reduces the need to learn and manage a separate storage system, so you can focus on optimizing resources and lowering TCO. </span></p></span></section><section class="nRhiJb-DbgRPb-II5mzb-cGMI2b"><section><div class="kg99be" jscontroller="vkZm2d" jsaction="rcuQ6b:npT2md" data-text-to-copy="apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: parallelstore-class provisioner: parallelstore.csi.storage.gke.io volumeBindingMode: Immediate reclaimPolicy: Delete allowedTopologies: - matchLabelExpressions: - key: topology.gke.io/zone values: - us-central1-a"><div jsaction="click:VTVhdd" class="GaRnv"><svg class="" viewBox="0 0 20 20" role="presentation" aria-hidden="true" height=20 widdht=20><path d="M7.979 14.667c-.458 0-.84-.153-1.146-.459a1.53 1.53 0 0 1-.458-1.125V3.938c0-.459.153-.84.458-1.146.306-.306.688-.459 1.146-.459h6.625c.445 0 .823.156 1.136.469.312.313.468.691.468 1.136v9.145c0 .445-.156.82-.468 1.125a1.564 1.564 0 0 1-1.136.459H7.979Zm0-1.334h6.625c.083 0 .15-.024.198-.073a.239.239 0 0 0 .073-.177V3.938a.265.265 0 0 0-.073-.198.268.268 0 0 0-.198-.073H7.979a.268.268 0 0 0-.198.073.266.266 0 0 0-.073.198v9.145c0 .07.024.129.073.177a.267.267 0 0 0 .198.073ZM4.875 17.75a1.53 1.53 0 0 1-1.125-.458 1.532 1.532 0 0 1-.458-1.125V5.729h1.333v10.438c0 .07.024.128.073.177a.24.24 0 0 0 .177.073h7.937v1.333H4.875ZM7.708 3.667v9.666-9.666Z" fill="#1a73e8"/></svg></div><div jscontroller="M2lJBf" jsaction="rcuQ6b:npT2md" data-enable-content-editable-input-style="false" data-enable-line-wrapping data-read-only><div class="hg3Lgc qs41qe BEXrzd" data-loadingmessage="Loading..." jscontroller="GFartf" jsaction="animationend:kWijWc;dyRcpb:dyRcpb" data-active="true" jsname="aZ2wEe"><div class="eBrXtc" aria-live="polite" jsname="vyyg5">Loading...</div><div jsname="Hxlbvc" class="JdM54e"><div class="aopPX ZqnFk co39ub"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX fxjES Cn087"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX ZHXbZe hfsr6b"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX fDBOYb EjXFBf"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div></div></div><div dir="ltr"><textarea jsname="bqeLof" class="Fdco1c">apiVersion: storage.k8s.io/v1 kind: StorageClass metadata: name: parallelstore-class provisioner: parallelstore.csi.storage.gke.io volumeBindingMode: Immediate reclaimPolicy: Delete allowedTopologies: - matchLabelExpressions: - key: topology.gke.io/zone values: - us-central1-a</textarea></div></div></div><p class="nRhiJb-cHYyed nRhiJb-DbgRPb-R6PoUb-ma6Yeb TxA6Xc"><p>Figure 3: Example of the Parallelstore CSI Driver creating a Storage Class</p></p></section></section><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><p><span style="vertical-align:baseline">In the coming months, you’ll be able to preload data from Cloud Storage via the fully managed GKE Volume Populator, which automates the preloading of data from Cloud Storage directly into Parallelstore during the PersistentVolumeClaim provisioning process. This helps ensure your training data is readily available, so you can minimize idle compute-resource time and maximize GPU and TPU utilization.</span></p> <h3><strong style="vertical-align:baseline">Programmatically provision Compute Engine resources with the Cluster Toolkit</strong></h3> <p><span style="vertical-align:baseline">It’s easy to deploy Parallelstore instances for Compute Engine with the support of the </span><a href="https://cloud.google.com/cluster-toolkit/docs/overview"><span style="text-decoration:underline;vertical-align:baseline">Cluster Toolkit</span></a><span style="vertical-align:baseline">. Formerly known as Cloud HPC Toolkit, Cluster Toolkit is open-source software for deploying HPC and AI workloads. Cluster Toolkit provisions compute, network, and storage resources for your cluster/workload following best practices. You can get started with Cluster Toolkit today by incorporating the</span><a href="https://github.com/GoogleCloudPlatform/cluster-toolkit/tree/main/modules/file-system/parallelstore" rel="noopener" target="_blank"><span style="vertical-align:baseline"> </span><span style="text-decoration:underline;vertical-align:baseline">Parallelstore</span></a><span style="vertical-align:baseline"> module into your blueprint with only a four-line change in your blueprint; we also provide </span><a href="https://github.com/GoogleCloudPlatform/cluster-toolkit/blob/main/examples/ps-slurm.yaml" rel="noopener" target="_blank"><span style="text-decoration:underline;vertical-align:baseline">starter blueprints</span></a><span style="vertical-align:baseline"> for your convenience. In addition to the Cluster Toolkit, there are also </span><a href="https://registry.terraform.io/providers/hashicorp/google/latest/docs/resources/parallelstore_instance" rel="noopener" target="_blank"><span style="text-decoration:underline;vertical-align:baseline">Terraform templates</span></a><span style="vertical-align:baseline"> for deploying Parallelstore, supporting operations and provisioning processes through code and minimizing manual operational overhead. </span></p></span></section><section class="nRhiJb-DbgRPb-II5mzb-cGMI2b"><section><div class="kg99be" jscontroller="vkZm2d" jsaction="rcuQ6b:npT2md" data-text-to-copy="resource "google_parallelstore_instance" "instance" { instance_id = "instance" location = "us-central1-a" description = "test instance" capacity_gib = 12000 network = google_compute_network.network.name file_stripe_level = "FILE_STRIPE_LEVEL_MIN" directory_stripe_level = "DIRECTORY_STRIPE_LEVEL_MIN" labels = { test = "value" } provider = google-beta depends_on = [google_service_networking_connection.default] } resource "google_compute_network" "network" { name = "network" auto_create_subnetworks = true mtu = 8896 provider = google-beta } # Create an IP address resource "google_compute_global_address" "private_ip_alloc" { name = "address" purpose = "VPC_PEERING" address_type = "INTERNAL" prefix_length = 24 network = google_compute_network.network.id provider = google-beta } # Create a private connection resource "google_service_networking_connection" "default" { network = google_compute_network.network.id service = "servicenetworking.googleapis.com" reserved_peering_ranges = [google_compute_global_address.private_ip_alloc.name] provider = google-beta }"><div jsaction="click:VTVhdd" class="GaRnv"><svg class="" viewBox="0 0 20 20" role="presentation" aria-hidden="true" height=20 widdht=20><path d="M7.979 14.667c-.458 0-.84-.153-1.146-.459a1.53 1.53 0 0 1-.458-1.125V3.938c0-.459.153-.84.458-1.146.306-.306.688-.459 1.146-.459h6.625c.445 0 .823.156 1.136.469.312.313.468.691.468 1.136v9.145c0 .445-.156.82-.468 1.125a1.564 1.564 0 0 1-1.136.459H7.979Zm0-1.334h6.625c.083 0 .15-.024.198-.073a.239.239 0 0 0 .073-.177V3.938a.265.265 0 0 0-.073-.198.268.268 0 0 0-.198-.073H7.979a.268.268 0 0 0-.198.073.266.266 0 0 0-.073.198v9.145c0 .07.024.129.073.177a.267.267 0 0 0 .198.073ZM4.875 17.75a1.53 1.53 0 0 1-1.125-.458 1.532 1.532 0 0 1-.458-1.125V5.729h1.333v10.438c0 .07.024.128.073.177a.24.24 0 0 0 .177.073h7.937v1.333H4.875ZM7.708 3.667v9.666-9.666Z" fill="#1a73e8"/></svg></div><div jscontroller="M2lJBf" jsaction="rcuQ6b:npT2md" data-enable-content-editable-input-style="false" data-enable-line-wrapping data-read-only><div class="hg3Lgc qs41qe BEXrzd" data-loadingmessage="Loading..." jscontroller="GFartf" jsaction="animationend:kWijWc;dyRcpb:dyRcpb" data-active="true" jsname="aZ2wEe"><div class="eBrXtc" aria-live="polite" jsname="vyyg5">Loading...</div><div jsname="Hxlbvc" class="JdM54e"><div class="aopPX ZqnFk co39ub"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX fxjES Cn087"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX ZHXbZe hfsr6b"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX fDBOYb EjXFBf"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div></div></div><div dir="ltr"><textarea jsname="bqeLof" class="Fdco1c">resource "google_parallelstore_instance" "instance" { instance_id = "instance" location = "us-central1-a" description = "test instance" capacity_gib = 12000 network = google_compute_network.network.name file_stripe_level = "FILE_STRIPE_LEVEL_MIN" directory_stripe_level = "DIRECTORY_STRIPE_LEVEL_MIN" labels = { test = "value" } provider = google-beta depends_on = [google_service_networking_connection.default] } resource "google_compute_network" "network" { name = "network" auto_create_subnetworks = true mtu = 8896 provider = google-beta } # Create an IP address resource "google_compute_global_address" "private_ip_alloc" { name = "address" purpose = "VPC_PEERING" address_type = "INTERNAL" prefix_length = 24 network = google_compute_network.network.id provider = google-beta } # Create a private connection resource "google_service_networking_connection" "default" { network = google_compute_network.network.id service = "servicenetworking.googleapis.com" reserved_peering_ranges = [google_compute_global_address.private_ip_alloc.name] provider = google-beta }</textarea></div></div></div><p class="nRhiJb-cHYyed nRhiJb-DbgRPb-R6PoUb-ma6Yeb TxA6Xc"><p>Figure 4: Configure and manage Parallelstore using Terraform</p></p></section></section><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><h3><strong style="vertical-align:baseline">Real-world impact: Respo.vision sees more with Parallelstore</strong></h3> <p><span style="vertical-align:baseline">Respo.Vision, a leader in sports video analytics, is leveraging Parallelstore to accelerate an upgrade from 4K to 8K videos for their real-time system. By using Parallelstore as the transport layer, Respo.vision helps capture and label granular data markers, delivering actionable insights to coaches, scouts, and fans. With Parallelstore, Respo.vision avoided pricey infrastructure investments to manage surges of high-performance video processing, all while maintaining low compute latency. </span></p> <p style="padding-left:40px"><span style="font-style:italic;vertical-align:baseline">“Our goal was to process 8K video streams at 25 frames per second to deliver richer quality sports analytical data to our customers, and Parallelstore exceeded expectations by effortlessly handling the required volume and delivering an impressive read latency of 0.3 ms. The integration into our system was remarkably smooth and thanks to its distributed nature, Parallelstore has significantly enhanced our system's scalability and resilience.”</span><span style="vertical-align:baseline"> - Wojtek Rosinski, CTO, Respo.vision </span></p> <p><span style="vertical-align:baseline">HPC and AI usage is growing rapidly. With its combination of innovative architecture, performance, and integration with Cloud Storage, GKE, and Compute Engine, Parallelstore is the storage solution you need to keep the demanding GPU/TPUs and workloads satisfied. To learn more about Parallelstore, check out the </span><a href="https://cloud.google.com/parallelstore/docs/overview"><span style="text-decoration:underline;vertical-align:baseline">documentation</span></a><span style="vertical-align:baseline">, and reach out to your sales team for more information.</span></p></span></section><section class="kcBhad"><section class="Fabbec"><span class="WrMNjb">Posted in</span><ul class="FzXI4e"><li class="I4B51b"><a href="https://cloud.google.com/blog/products/storage-data-transfer" track-metadata-position="body"track-metadata-eventdetail="cloud.google.com/blog/products/storage-data-transfer"track-metadata-module="tag list"track-metadata-module_headline="posted in">Storage & Data Transfer</a></li><li class="I4B51b"><a href="https://cloud.google.com/blog/products/ai-machine-learning" track-metadata-position="body"track-metadata-eventdetail="cloud.google.com/blog/products/ai-machine-learning"track-metadata-module="tag list"track-metadata-module_headline="posted in">AI & Machine Learning</a></li><li class="I4B51b"><a href="https://cloud.google.com/blog/topics/hpc" track-metadata-position="body"track-metadata-eventdetail="cloud.google.com/blog/topics/hpc"track-metadata-module="tag list"track-metadata-module_headline="posted in">HPC</a></li></ul></section></section></div></div></div></div></div><section class="nRhiJb-DARUcf " track-metadata-module="related articles" track-metadata-module_headline="related articles"><div class="nRhiJb-DbgRPb-c5RTEf-ma6Yeb nRhiJb-DbgRPb-wNfPc-cGMI2b"><h5 class="Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-wNfPc nRhiJb-DbgRPb-II5mzb-cGMI2b">Related articles</h5><section class="m9cUGf HGev3 nJD2Qe nRhiJb-ObfsIf"><div class=" QaGyvd nRhiJb-kR0ZEf-OWXEXe-GV1x9e-c5RTEf nRhiJb-kR0ZEf-OWXEXe-GV1x9e-qWD73c-V2iZpe"><div class="mA0uBe"><a href="https://cloud.google.com/blog/products/containers-kubernetes/improve-data-loading-times-for-ml-inference-apps-on-gke" class="lD2oe" 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Cloud\",\"logo\":{\"@type\":\"ImageObject\",\"url\":\"https://www.gstatic.com/devrel-devsite/prod/v8bb8fa0afe9a8c3a776ebeb25d421bb443344d789b3607754dfabea418b8c4be/cloud/images/cloud-logo.svg\"}},\"url\":\"https://cloud.google.com/blog/products/storage-data-transfer/parallelstore-high-performance-file-service-for-hpc-and-ai-is-ga\",\"keywords\":[\"Storage \\u0026 Data Transfer\",\"AI \\u0026 Machine Learning\",\"HPC\"],\"timeRequired\":\"PT4M\"}\u003c/script\u003e"],["Parallelstore is now GA, fueling the next generation of AI and HPC workloads"],null,null,[[null,null,[null,[null,"\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eOrganizations use artificial intelligence (AI) and high-performance computing (HPC) applications to process massive datasets, run complex simulations, and train generative models with billions of parameters for diverse use cases such as LLMs, genomic analysis, quantitative analysis, or real-time sports analytics. These workloads place big performance demands on their storage systems, requiring high throughput and I/O performance that scales and that maintains sub-millisecond latencies, even when thousands of clients are concurrently reading and writing the same shared files.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eTo power these next-generation AI and HPC workloads, we \u003c/span\u003e\u003ca href\u003d\"https://cloud.google.com/blog/products/storage-data-transfer/storage-announcements-at-next24\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eannounced\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e Parallelstore at Google Cloud Next 2024, and today, we are excited to announce that it is now generally available. Built on the \u003c/span\u003e\u003ca href\u003d\"https://docs.daos.io/v2.6/\" rel\u003d\"noopener\" target\u003d\"_blank\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eDistributed Asynchronous Object Storage (DAOS) architecture\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e, \u003c/span\u003e\u003ca href\u003d\"https://cloud.google.com/parallelstore\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eParallelstore\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e combines a fully distributed metadata and key-value architecture to deliver high-performance throughput and IOPS.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eRead on to learn how Parallelstore serves the needs of complex AI and HPC workloads, allowing you to maximize \u003c/span\u003e\u003ca href\u003d\"https://cloud.google.com/blog/products/ai-machine-learning/goodput-metric-as-measure-of-ml-productivity\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003egoodput\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e and GPU/TPU utilization, programmatically move data in and out of Parallelstore, and provision \u003c/span\u003e\u003ca href\u003d\"https://cloud.google.com/kubernetes-engine\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eGoogle Kubernetes Engine\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e and \u003c/span\u003e\u003ca href\u003d\"https://cloud.google.com/products/compute\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eCompute Engine\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e resources.\u003c/span\u003e\u003c/p\u003e\n\u003ch3\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eMaximize goodput and GPU/TPU utilization\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eTo overcome the performance limitations of traditional parallel file systems, Parallelstore uses a distributed metadata management system and a key-value store architecture. Parallelstore\u2019s high-throughput parallel data access minimizes latency and I/O bottlenecks, and allows it to saturate the network bandwidth of individual compute clients. This efficient data delivery maximizes goodput to GPUs and TPUs, a critical factor for optimizing AI workload costs. Parallelstore can also provide continuous read/write access to thousands of VMs, GPUs and TPUs, satisfying modest-to-massive AI and HPC workload requirements.\u00a0\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eFor a 100 TiB deployment, the maximum Parallelstore deployment, throughput scales to ~115 GiB/s, ~3 million read IOPS, ~1 million write IOPS, and a low-latency of ~0.3 ms. This means that Parallelstore is also a good platform for small files and random, distributed access across a large number of clients. For AI use cases, Parallelstore\u2019s performance with small files and metadata operations enables up to 3.9x faster training times and up to 3.7x higher training throughput compared to native ML framework data loaders, as measured by Google Cloud benchmarking.\u003c/span\u003e\u003c/p\u003e\n\u003ch3\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eProgrammatically move data in and out of Parallelstore\u00a0\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eMany AI and HPC workloads store data in \u003c/span\u003e\u003ca href\u003d\"https://cloud.google.com/storage\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eCloud Storage\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e for data preparation or archiving. You can use Parallelstore\u2019s integrated import/export API to automate movement of the data you\u2019d like to import to Parallelstore for processing. With the API, you can ingest massive datasets from Cloud Storage into Parallelstore at ~20GB/s for files larger than 32MB, and at ~5,000 files per second for files under 32MB.\u003c/span\u003e\u003c/p\u003e"]]],[["gcloud alpha parallelstore instances import-data $INSTANCE_ID\r\n--location\u003d$LOCATION --source-gcs-bucket-uri\u003dgs://$BUCKET_NAME\r\n[--destination-parallelstore-path\u003d\"/\"] --project\u003d $PROJECT_ID",null,[null,"\u003cp\u003eFigure 1: Parallelstore Import gCloud API\u003c/p\u003e"]]],[null,null,[null,[null,"\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eWhen an AI training job or HPC workload is complete, you can export results programmatically to Cloud Storage for further assessment or longer-term storage. You can also automate data transfers via the API, minimizing manual intervention and streamlining data pipelines.\u003c/span\u003e\u003c/p\u003e"]]],[["gcloud alpha parallelstore instances export-data $INSTANCE_ID --location\u003d$LOCATION --destination-gcs-bucket-uri\u003dgs://$BUCKET_NAME\r\n[--source-parallelstore-path\u003d\"/\"]",null,[null,"\u003cp\u003eFigure 2: Parallelstore Export gCloud API\u003c/p\u003e"]]],[null,null,[null,[null,"\u003ch3\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eProgrammatically provision GKE resources through the CSI driver\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eIt\u2019s easy to efficiently manage high-performance storage for containerized workloads through Parallelstores\u2019 GKE CSI driver. You can dynamically provision and manage Parallelstore file systems as persistent volumes or access existing Parallelstore instances in Kubernetes workloads, directly within your GKE clusters using familiar Kubernetes APIs. This reduces the need to learn and manage a separate storage system, so you can focus on optimizing resources and lowering TCO. \u003c/span\u003e\u003c/p\u003e"]]],[["apiVersion: storage.k8s.io/v1\r\nkind: StorageClass\r\nmetadata:\r\n name: parallelstore-class\r\nprovisioner: parallelstore.csi.storage.gke.io\r\nvolumeBindingMode: Immediate\r\nreclaimPolicy: Delete\r\nallowedTopologies:\r\n- matchLabelExpressions:\r\n - key: topology.gke.io/zone\r\n values:\r\n - us-central1-a",null,[null,"\u003cp\u003eFigure 3: Example of the Parallelstore CSI Driver creating a Storage Class\u003c/p\u003e"]]],[null,null,[null,[null,"\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eIn the coming months, you\u2019ll be able to preload data from Cloud Storage via the fully managed GKE Volume Populator, which automates the preloading of data from Cloud Storage directly into Parallelstore during the PersistentVolumeClaim provisioning process. This helps ensure your training data is readily available, so you can minimize idle compute-resource time and maximize GPU and TPU utilization.\u003c/span\u003e\u003c/p\u003e\n\u003ch3\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eProgrammatically provision Compute Engine resources with the Cluster Toolkit\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eIt\u2019s easy to deploy Parallelstore instances for Compute Engine with the support of the \u003c/span\u003e\u003ca href\u003d\"https://cloud.google.com/cluster-toolkit/docs/overview\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eCluster Toolkit\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e. Formerly known as Cloud HPC Toolkit, Cluster Toolkit is open-source software for deploying HPC and AI workloads. Cluster Toolkit provisions compute, network, and storage resources for your cluster/workload following best practices. You can get started with Cluster Toolkit today by incorporating the\u003c/span\u003e\u003ca href\u003d\"https://github.com/GoogleCloudPlatform/cluster-toolkit/tree/main/modules/file-system/parallelstore\" rel\u003d\"noopener\" target\u003d\"_blank\"\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e \u003c/span\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eParallelstore\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e module into your blueprint with only a four-line change in your blueprint; we also provide \u003c/span\u003e\u003ca href\u003d\"https://github.com/GoogleCloudPlatform/cluster-toolkit/blob/main/examples/ps-slurm.yaml\" rel\u003d\"noopener\" target\u003d\"_blank\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003estarter blueprints\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e for your convenience. In addition to the Cluster Toolkit, there are also \u003c/span\u003e\u003ca href\u003d\"https://registry.terraform.io/providers/hashicorp/google/latest/docs/resources/parallelstore_instance\" rel\u003d\"noopener\" target\u003d\"_blank\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eTerraform templates\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e for deploying Parallelstore, supporting operations and provisioning processes through code and minimizing manual operational overhead. \u003c/span\u003e\u003c/p\u003e"]]],[["resource \"google_parallelstore_instance\" \"instance\" { \r\ninstance_id \u003d \"instance\" \r\nlocation \u003d \"us-central1-a\" \r\ndescription \u003d \"test instance\" \r\ncapacity_gib \u003d 12000 \r\nnetwork \u003d google_compute_network.network.name \r\nfile_stripe_level \u003d \"FILE_STRIPE_LEVEL_MIN\" \r\ndirectory_stripe_level \u003d \"DIRECTORY_STRIPE_LEVEL_MIN\" \r\nlabels \u003d { \r\ntest \u003d \"value\" \r\n} \r\nprovider \u003d google-beta \r\ndepends_on \u003d [google_service_networking_connection.default] \r\n} \r\n\r\nresource \"google_compute_network\" \"network\" { \r\nname \u003d \"network\" \r\nauto_create_subnetworks \u003d true \r\nmtu \u003d 8896 \r\nprovider \u003d google-beta \r\n} \r\n\r\n# Create an IP address \r\nresource \"google_compute_global_address\" \"private_ip_alloc\" { \r\nname \u003d \"address\" \r\npurpose \u003d \"VPC_PEERING\" \r\naddress_type \u003d \"INTERNAL\" \r\nprefix_length \u003d 24 \r\nnetwork \u003d google_compute_network.network.id \r\nprovider \u003d google-beta \r\n} \r\n\r\n# Create a private connection \r\nresource \"google_service_networking_connection\" \"default\" { \r\nnetwork \u003d google_compute_network.network.id \r\nservice \u003d \"servicenetworking.googleapis.com\"\r\nreserved_peering_ranges \u003d [google_compute_global_address.private_ip_alloc.name] \r\nprovider \u003d google-beta \r\n}",null,[null,"\u003cp\u003eFigure 4: Configure and manage Parallelstore using Terraform\u003c/p\u003e"]]],[null,null,[null,[null,"\u003ch3\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eReal-world impact: Respo.vision sees more with Parallelstore\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eRespo.Vision, a leader in sports video analytics, is leveraging Parallelstore to accelerate an upgrade from 4K to 8K videos for their real-time system. By using Parallelstore as the transport layer, Respo.vision helps capture and label granular data markers, delivering actionable insights to coaches, scouts, and fans. With Parallelstore, Respo.vision avoided pricey infrastructure investments to manage surges of high-performance video processing, all while maintaining low compute latency.\u00a0\u003c/span\u003e\u003c/p\u003e\n\u003cp style\u003d\"padding-left:40px\"\u003e\u003cspan style\u003d\"font-style:italic;vertical-align:baseline\"\u003e\u201cOur goal was to process 8K video streams at 25 frames per second to deliver richer quality sports analytical data to our customers, and Parallelstore exceeded expectations by effortlessly handling the required volume and delivering an impressive read latency of 0.3 ms. The integration into our system was remarkably smooth and thanks to its distributed nature, Parallelstore has significantly enhanced our system's scalability and resilience.\u201d\u003c/span\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e - Wojtek Rosinski, CTO, Respo.vision\u00a0\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eHPC and AI usage is growing rapidly. With its combination of innovative architecture, performance, and integration with Cloud Storage, GKE, and Compute Engine, Parallelstore is the storage solution you need to keep the demanding GPU/TPUs and workloads satisfied. To learn more about Parallelstore, check out the \u003c/span\u003e\u003ca href\u003d\"https://cloud.google.com/parallelstore/docs/overview\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003edocumentation\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e, and reach out to your sales team for more information.\u003c/span\u003e\u003c/p\u003e"]]]],[["Containers \u0026 Kubernetes","Data loading best practices for AI/ML inference on GKE","When running AI/ML inference workloads on GKE, learn how to accelerate data loading for inference serving containers and downloading models + weights.",["https://storage.googleapis.com/gweb-cloudblog-publish/images/07_-_Containers__Kubernetes_iY4YTLa.max-700x700.jpg","https://storage.googleapis.com/gweb-cloudblog-publish/images/07_-_Containers__Kubernetes_iY4YTLa.max-400x400.jpg 324w, https://storage.googleapis.com/gweb-cloudblog-publish/images/07_-_Containers__Kubernetes_iY4YTLa.max-700x700.jpg 648w"," 324px, 648px","https://storage.googleapis.com/gweb-cloudblog-publish/images/07_-_Containers__Kubernetes_iY4YTLa.max-700x700.jpg"],null,6,null,"https://cloud.google.com/blog/products/containers-kubernetes/improve-data-loading-times-for-ml-inference-apps-on-gke",null,1,[["Brian Kaufman","Senior Product Manager GKE AI/ML"],["Akshay Ram","Group Product Manager GKE AI/ML"]],null,"55570"],["Data Analytics","Dataplex Automatic Discovery makes Cloud Storage data available for Analytics and governance","Dataplex can now automatically discover and catalog Google Cloud Storage data, extracting metadata, and creating BigQuery and BigLake tables.",["https://storage.googleapis.com/gweb-cloudblog-publish/images/09_-_Data_Analytics_tFH57V6.max-700x700.jpg","https://storage.googleapis.com/gweb-cloudblog-publish/images/09_-_Data_Analytics_tFH57V6.max-400x400.jpg 324w, https://storage.googleapis.com/gweb-cloudblog-publish/images/09_-_Data_Analytics_tFH57V6.max-700x700.jpg 648w"," 324px, 648px","https://storage.googleapis.com/gweb-cloudblog-publish/images/09_-_Data_Analytics_tFH57V6.max-700x700.jpg"],null,3,null,"https://cloud.google.com/blog/products/data-analytics/dataplex-discovers-and-catalogs-cloud-storage-data",null,1,[["Parth Desai","Product Manager, Google"]],null,"55546"],["Storage \u0026 Data Transfer","Understand your Cloud Storage footprint with AI-powered queries and insights","AI-powered insights on object metadata can help you control and understand your Cloud Storage footprint, even at billions of objects scale.",["https://storage.googleapis.com/gweb-cloudblog-publish/images/33_-_Storage__Data_Transfer_QsgjqZW.max-700x700.jpg","https://storage.googleapis.com/gweb-cloudblog-publish/images/33_-_Storage__Data_Transfer_QsgjqZW.max-400x400.jpg 324w, https://storage.googleapis.com/gweb-cloudblog-publish/images/33_-_Storage__Data_Transfer_QsgjqZW.max-700x700.jpg 648w"," 324px, 648px","https://storage.googleapis.com/gweb-cloudblog-publish/images/33_-_Storage__Data_Transfer_QsgjqZW.max-700x700.jpg"],null,3,null,"https://cloud.google.com/blog/products/storage-data-transfer/gemini-insights-about-cloud-storage",null,1,[["Manjul Sahay","Group Product Manager, Google Cloud Storage"],["Jinal Dalal","Engineering Director, Google Cloud Storage"]],null,"55329"],["Storage \u0026 Data Transfer","Want to save on GKE block storage costs? Hyperdisk Storage Pools can help","Placing Hyperdisk block storage disks in a storage pool lets you share capacity and performance among the disks and optimize for operations and cost.",["https://storage.googleapis.com/gweb-cloudblog-publish/images/33_-_Storage__Data_Transfer_QsgjqZW.max-700x700.jpg","https://storage.googleapis.com/gweb-cloudblog-publish/images/33_-_Storage__Data_Transfer_QsgjqZW.max-400x400.jpg 324w, https://storage.googleapis.com/gweb-cloudblog-publish/images/33_-_Storage__Data_Transfer_QsgjqZW.max-700x700.jpg 648w"," 324px, 648px","https://storage.googleapis.com/gweb-cloudblog-publish/images/33_-_Storage__Data_Transfer_QsgjqZW.max-700x700.jpg"],null,6,null,"https://cloud.google.com/blog/products/storage-data-transfer/hyperdisk-storage-pools-optimizes-gke-block-storage",null,1,[["Brian Kaufman","Senior Product Manager GKE AI/ML"],["Alexis MacAskill","Software Engineer GKE"]],null,"55229"]],null,"Storage \u0026 Data Transfer",["Google Cloud Summit Series","Discover the latest in AI, Security, Workspace, App Dev, \u0026 more.","Register","https://cloudonair.withgoogle.com/summit_series_24?utm_source\u003dcgc-blog\u0026utm_medium\u003dblog\u0026utm_campaign\u003dsummit-series-2024\u0026utm_content\u003dsummit-series-left-hand-rail-cta\u0026utm_term\u003d-"],[["Storage \u0026 Data Transfer","https://cloud.google.com/blog/products/storage-data-transfer","storage-data-transfer"],["AI \u0026 Machine Learning","https://cloud.google.com/blog/products/ai-machine-learning","ai-machine-learning"],["HPC","https://cloud.google.com/blog/topics/hpc","hpc"]],null,null,4], sideChannel: {}});</script><script id="wiz_jd" nonce="Y09GG6oIglCWVeuA2DlFUA">if (window['_wjdc']) {const wjd = {}; window['_wjdc'](wjd); delete window['_wjdc'];}</script><script aria-hidden="true" id="WIZ-footer" nonce="Y09GG6oIglCWVeuA2DlFUA">window.wiz_progress&&window.wiz_progress(); window.stopScanForCss&&window.stopScanForCss(); ccTick('bl');</script></body></html><footer 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