Category: Compute

In this post, we’ll review the key optimizations and performance gains for our Whisper audio transcription solution powered by AWS Batch and AWS Inferentia.

AWS Parallel Computing Service (AWS PCS) now supports accounting, a Slurm feature that enables you to monitor resource utilization, enforce resource limits, and manage access-control to specific capacity across users and projects in a cluster. AWS PCS manages the accounting database for the cluster, so that you don’t have to setup and manage a separate accounting database. In this post, we’ll show you how this works, and point you to some actual use cases you can try yourself.

Subsurface infrastructure mapping is crucial for industries ranging from oil and gas to environmental protection. Our groundbreaking approach combines advanced magnetic imaging with physics-informed AI to provide unparalleled visibility into hidden structures, even when traditional methods fall short. Explore how this fusion of cloud computing and AI is opening new possibilities for subsurface exploration and management.

Ansys Gateway powered by AWS now has an integration with AWS ParallelCluster to enable users deploy on-demand HPC clusters for running Ansys simulations on AWS. This allows engineers to run large-scale simulations efficiently while optimizing cloud costs by dynamically adjusting resources based on simulation workload requirements. In this blog post, we describe the architecture, workflow, and Amazon EC2 recommendations for running Ansys applications in Ansys Gateway.

As climate change escalates, power infrastructure faces growing risks. Explore how the ClimateEarthDigitalTwin (CDT™) platform from riskthinking.AI leverages AWS HPC to assess these risks and enable resilience planning for the energy sector. Learn how this cutting-edge solution can safeguard your critical assets.

Discover how Petrobras and Universidade Federal Fluminense (Rio de Janeiro — Brazil) developed an innovative HPC solution on AWS, leveraging Spot Instances to optimize costs. Explore the automations in place to avoid interruptions and use the lowest-cost instances available.

Autonomous robots are increasingly used across industries, from warehouses to space exploration. While developing these robots requires complex simulation and reinforcement learning (RL), setting up training environments can be challenging and time-consuming. AWS Batch multi-node parallel (MNP) infrastructure, combined with NVIDIA Isaac Lab, offers a solution by providing scalable, cost-effective robot training capabilities for sophisticated behaviors and complex tasks.

Discover how to coordinate multiple applications in separate containers within a single AWS Batch job definition. Learn the benefits of this approach and how to share resources between containers for more efficient, scalable deployments.

Developing robust investment strategies requires thorough testing, but relying solely on historical data can introduce biases and limit your insights. Learn how synthetic data from agent-based models can provide an unbiased testbed to systematically evaluate your strategies and prepare for future market scenarios. Part 2 covers implementation details and results.

Struggling with bottlenecks in your batch processing? AWS Batch’s new resource aware scheduling capability could be the solution your business needs. This feature allows you to define and manage consumable resources, helping maximize the use of your compute power. Check out our blog to learn more.