Azure Batch
Author: Ronald Fung
Creation Date: 31 May 2023
Next Modified Date: 31 May 2024
A. Introduction
Use Azure Batch to run large-scale parallel and high-performance computing (HPC) batch jobs efficiently in Azure. Azure Batch creates and manages a pool of compute nodes (virtual machines), installs the applications you want to run, and schedules jobs to run on the nodes. There’s no cluster or job scheduler software to install, manage, or scale. Instead, you use Batch APIs and tools, command-line scripts, or the Azure portal to configure, manage, and monitor your jobs.
Developers can use Batch as a platform service to build SaaS applications or client apps where large-scale execution is required. For example, you can build a service with Batch to run a Monte Carlo risk simulation for a financial services company, or a service to process many images.
There is no additional charge for using Batch. You only pay for the underlying resources consumed, such as the virtual machines, storage, and networking.
For a comparison between Batch and other HPC solution options in Azure, see High Performance Computing (HPC) on Azure.
Run parallel workloads Batch works well with intrinsically parallel (also known as “embarrassingly parallel”) workloads. These workloads have applications which can run independently, with each instance completing part of the work. When the applications are executing, they might access some common data, but they don’t communicate with other instances of the application. Intrinsically parallel workloads can therefore run at a large scale, determined by the amount of compute resources available to run applications simultaneously.
Some examples of intrinsically parallel workloads you can bring to Batch:
Financial risk modeling using Monte Carlo simulations
VFX and 3D image rendering
Image analysis and processing
Media transcoding
Genetic sequence analysis
Optical character recognition (OCR)
Data ingestion, processing, and ETL operations
Software test execution
You can also use Batch to run tightly coupled workloads, where the applications you run need to communicate with each other, rather than running independently. Tightly coupled applications normally use the Message Passing Interface (MPI) API. You can run your tightly coupled workloads with Batch using Microsoft MPI or Intel MPI. Improve application performance with specialized HPC and GPU-optimized VM sizes.
Some examples of tightly coupled workloads:
Finite element analysis
Fluid dynamics
Multi-node AI training
Many tightly coupled jobs can be run in parallel using Batch. For example, you can perform multiple simulations of a liquid flowing through a pipe with varying pipe widths.
B. How is it used at Seagen
As a biopharma research company that uses Microsoft Azure, Azure Batch can be used to run large-scale parallel and high-performance computing (HPC) workloads in the cloud. Here are some ways that Seagen can use Azure Batch:
Parallel processing: Azure Batch can be used to run parallel processing tasks, such as genetic sequencing, drug discovery, and data analysis, which can be performed on large datasets.
HPC workloads: Azure Batch can be used to run HPC workloads, such as molecular dynamics simulations, protein folding simulations, and Monte Carlo simulations, which require large amounts of compute power.
Scalability: Azure Batch is designed to scale to handle large-scale parallel and HPC workloads, making it a powerful tool for businesses that need to process large amounts of data in the cloud.
Easy integration: Azure Batch can be easily integrated with other Azure services, such as Azure Storage, Azure Virtual Machines, and Azure Active Directory, allowing you to create powerful and scalable HPC solutions.
Cross-platform support: Azure Batch supports multiple operating systems and programming languages, including Windows, Linux, and .NET, making it easy to use with your existing software stack.
Security: Azure Batch provides built-in security features, including authentication and access control, to help ensure that your HPC workloads are secure.
Cost-effective: Azure Batch is a cost-effective solution for running HPC workloads in the cloud, as it allows you to pay only for the compute resources you use.
Overall, Azure Batch is a powerful tool for running large-scale parallel and HPC workloads in the cloud. Its ability to provide parallel processing, scalability, easy integration, cross-platform support, security, and cost-effectiveness make it an ideal solution for businesses looking to process large amounts of data in the cloud.
C. Features
Azure Batch is a cloud-based service that provides managed batch processing and high-performance computing (HPC) workloads. Here are some of the key features of Azure Batch:
Batch processing: Azure Batch allows you to process large amounts of data in parallel, which can be useful for batch processing tasks such as rendering, image processing, and data analysis.
HPC workloads: Azure Batch is designed to handle high-performance computing (HPC) workloads, such as scientific simulations, machine learning training, and financial modeling.
Scalability: Azure Batch is designed to scale to handle large workloads, allowing you to process large amounts of data in parallel.
Easy integration: Azure Batch can be easily integrated with other Azure services, such as Azure Storage, Azure Virtual Machines, and Azure Active Directory, allowing you to create powerful and scalable HPC solutions.
Cross-platform support: Azure Batch supports multiple operating systems and programming languages, including Windows, Linux, and .NET, making it easy to use with your existing software stack.
Security: Azure Batch provides built-in security features, including authentication and access control, to help ensure that your HPC workloads are secure.
Cost-effective: Azure Batch is a cost-effective solution for running HPC workloads in the cloud, as it allows you to pay only for the compute resources you use.
Flexible scheduling: Azure Batch allows you to schedule jobs and tasks based on your specific needs, which can help optimize resource usage and reduce costs.
Customization: Azure Batch provides customization options, allowing you to tailor the service to meet your specific needs.
Overall, Azure Batch is a powerful tool for running batch processing and HPC workloads in the cloud. Its ability to provide batch processing, HPC workloads, scalability, easy integration, cross-platform support, security, cost-effectiveness, flexible scheduling, and customization options make it an ideal solution for businesses looking to process large amounts of data in the cloud.
D. Where Implemented
E. How it is tested
Testing Azure Batch involves verifying that batch processing and HPC workloads are properly configured and functioning as expected. Here are some steps you can take to test Azure Batch:
Verify configuration: Verify that Azure Batch is properly configured and integrated with your batch processing and HPC workloads.
Test batch processing: Test Azure Batch by running batch processing tasks, such as rendering, image processing, and data analysis, and verifying that they are processed in parallel.
Test HPC workloads: Test Azure Batch by running HPC workloads, such as scientific simulations, machine learning training, and financial modeling, and verifying that they are processed with the required level of performance and accuracy.
Test scalability: Test the scalability of Azure Batch by running batch processing and HPC workloads at high volumes and verifying that they are processed efficiently and without any delays.
Test integration: Test the integration of Azure Batch with other Azure services, such as Azure Storage, Azure Virtual Machines, and Azure Active Directory, by verifying that jobs and tasks are properly received and processed by these services.
Test cross-platform support: Test the cross-platform support of Azure Batch by using multiple operating systems and programming languages, such as Windows, Linux, and .NET, to verify that jobs and tasks are properly received and processed.
Test security: Test the security of Azure Batch by verifying that jobs and tasks are properly authenticated and that access control policies are enforced.
Test cost-effectiveness: Test the cost-effectiveness of Azure Batch by monitoring usage and verifying that the service is cost-effective for your business.
Overall, testing Azure Batch involves verifying that batch processing and HPC workloads are properly configured and functioning as expected, testing scalability, integration, cross-platform support, security, cost-effectiveness, and monitoring usage to ensure that the service is meeting the needs of your business. By testing Azure Batch, you can ensure that your batch processing and HPC workloads are effectively using the service and that your business is benefiting from the scalability, efficiency, and cost-effectiveness it provides.
F. 2023 Roadmap
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G. 2024 Roadmap
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H. Known Issues
As with any software, there may be known issues or limitations that users should be aware of when using Azure Batch. Here are some of the known issues for Azure Batch:
Limitations on VM selection: Azure Batch may have limitations on the selection of virtual machines (VMs) that can be used for batch processing and HPC workloads, which can impact the performance and scalability of your workloads.
Cost: Azure Batch can be more expensive than other batch processing solutions, which can be a factor for businesses with limited budgets.
Complexity: Azure Batch can be complex to configure and manage, which may require additional training and support for users.
Network latency: Azure Batch may be affected by network latency, which can impact the speed and reliability of batch processing and HPC workloads.
Compatibility issues: Azure Batch may have compatibility issues with certain batch processing and HPC workloads, which can limit the ability of users to use the service with their existing software stack.
Job scheduling limitations: Azure Batch may have limitations on the scheduling of jobs, which can impact the ability of users to optimize resource usage and reduce costs.
Overall, while Azure Batch is a powerful tool for running batch processing and HPC workloads in the cloud, users must be aware of these known issues and take steps to mitigate their impact. This may include carefully selecting VMs that are compatible with Azure Batch, carefully configuring the service to meet the specific needs of their business, providing training and support to users to effectively use and manage Azure Batch, carefully evaluating the cost and complexity of the solution to ensure that it is a good fit for their business, and carefully scheduling jobs to optimize resource usage and reduce costs. By taking these steps, users can ensure that their batch processing and HPC workloads are effectively using Azure Batch and that their business is benefiting from the scalability, efficiency, and cost-effectiveness it provides.
[x] Reviewed by Enterprise Architecture
[x] Reviewed by Application Development
[x] Reviewed by Data Architecture