Accelerate rendering with Azure high-performance computing (HPC)

Render with confidence

Render on a scalable, MPAA-certified, non-competing platform that’s trusted by 95 percent of the Fortune 500. Customer data is stored in a locked-down environment within a private network.

Collaborate with global teams

Optimize productivity across remote teams on a high-speed, reliable, and highly secure platform that has nearly unlimited HPC capacity and caching that hides storage latency.

Pay per use

Take advantage of pay-per-use licensing—ideal for studios with dynamic project-based workloads, allowing you to tailor compute services to your exact application and project requirements.

Explore Azure products for boosting rendering performance

Azure HPC Cache

  • Get high-performance storage infrastructure for hybrid rendering.
  • Manage spikes in compute demand by instantly spinning up thousands of virtual machines without moving data.
  • Bridge your rendering environment and process data stored in Azure completely in the cloud with low-latency, cost-effective HPC file caching.

Azure Virtual Machines (VMs)

  • Run graphic-intensive, remote visualization applications on GPU-powered Azure NV-series VMs.
  • Run CPU-based rendering on AMD EPYC-powered HB-series VMs featuring up to 260 GB/s of memory bandwidth.
  • Choose from a wide range of pre-configured VMs to meet your business needs, from price-optimized VMs all the way to high-end accelerated NVIDIA GPU- powered VMs for compute- and network-intensive rendering scenarios.

Azure Batch

  • Burst rendering workloads to Azure directly from your application.
  • Manage rendering workloads—including submission and monitoring of rendering jobs— with client plugin support for industry-standard applications.

Azure Render Hub

  • Extend your existing render farm and simplify the creation and management of hybrid or cloud rendering infrastructure.
  • Get native support for deploying PipelineFx Qube! and Thinkbox Deadline 10.

Solution architecture: Monitoring a pipeline manager and orchestrating burst compute node capacity for media rendering

Hybrid risk analysis architectureThis templated risk analysis solution uses Azure HPC compute and GPU virtual machines (VMs) to expand on-premises Tibco GridServer compute to Azure using Azure CycleCloud for auto-scaling integration. The job executes both on-premises and in the cloud by using Avere vFXT fast caching and native NFS access to market data available on-premises.1234566778
  1. Overview
  2. Flow

Hybrid risk analysis architecture

Overview

This templated risk analysis solution uses Azure HPC compute and GPU virtual machines (VMs) to expand on-premises Tibco GridServer compute to Azure using Azure CycleCloud for auto-scaling integration. The job executes both on-premises and in the cloud by using Avere vFXT fast caching and native NFS access to market data available on-premises.

Flow

  1. 1 Operations team uses Azure CycleCloud to configure and launch risk analysis grid in Azure.
  2. 2 Azure CycleCloud orchestrates VM creation and software configuration for Tibco Gridserver brokers and HPCCA, in-memory data cache, and Avere vFXT cache.
  3. 3 Quant (or scheduled batch) submits a risk analysis template workflow to the on-premises Tibco GridServer director. Based on job policies and current on-premises use, the workflow is allowed to burst to Azure to expand on-premises grid capacity.
  4. 4 The Tibco HPCCA detects the change in queue depth for each Tibco broker and requests additional Tibco engine capacity using the Azure CycleCloud Auto-Scaling API. Azure CycleCloud then autostarts engine nodes in Virtual Machine Scale Sets using the Azure H-series, HB-series, and HC-series VMs to optimize cost and performance and NC-series VMs to provide GPU capacity as required.
  5. 5 As soon as engine VMs join the Azure Grid, the brokers begin executing tasks to the new nodes.
  6. 6 Risk jobs pull artifacts from on-premises and Azure Blob storage as needed from NFS mounted Avere vFXT and/or via the fast in-memory cache.
  7. 7 As each task completes, results are returned to the submitter or driver and data is written back to the in-memory cache, or to NFS storage through the Avere vFXT, as required. Cached data is persisted either on-premises or in Azure Blob storage.
  8. 8 As task queues drain, the Tibco HPCCA uses the Azure CycleCloud Auto-Scaling API to shrink the compute grid and reduce cost.

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Create your rendering solution with an Azure HPC partner

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