Rescale upgrades scientific compute containerization

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Rescale, a high-performance computing cloud vendor, has unveiled new features aimed at improving support for containers in cloud services and specialized architectures. New opportunities should facilitate the development of advanced twinning and advanced scientific and engineering applications for digital twins and advanced scientific research.

Containers are one of the fastest growing sectors in the IT sector. Containers help reduce the hassle of moving the application from on-premises to cloud infrastructure or between clouds. Gartner predicts that this year, more than 75% of global organizations will run a containerized application in production, down from 30% in 2020.

However, enterprise high-performance computing (HPC) faces more challenges in moving workloads to containerized infrastructure. Cases of HPC use bring additional requirements on security and parallel processing. Rescal’s special sauce includes bundles of HPC-specific applications and the dependencies needed to implement the best possible architecture.

Simplify the engineer experience

Provides a user-intuitive interface for rescale engineers and scientists to securely deploy and scale containerized workloads on any architecture from the Multi-Cloud Infrastructure catalog. The company is now expanding support for HPC use cases around Artificial Intelligence (AI) and Machine Learning (ML) workloads. With containers, engineers and scientists can pull the published container out of the registry of their choice and proceed with their research without thinking further about the infrastructure.

Containers are used to deploy specialized tools, such as a specialized Nvidia solver for their architecture or a domestic engineering application. With Rescale, users can drag published containers from the registry of their choice and proceed with their research without having to think further about the infrastructure. Rescale automates the entire workflow to run these applications and provides easy collaboration for users to share jobs or data while providing security and financial and architectural controls to IT / HPC organizations.

This helps simplify the architectural decisions needed to build the right infrastructure around each application the user wants to run. The company also has unique intelligence on a complete set of cloud provider architectures. This includes the maturity and available capabilities of these architectures across geographic and cloud provider regions across how each architecture performs against different applications on different scales. These can range from workloads for AI / ML, computational fluid dynamics and computational chemistry. This has helped enterprises such as nuclear fusion reactors (Commonwealth Fusion Systems) and commercial spacecraft (firefly aerospace).

Refactoring for cloud services

Adam McKenzie, CTO and founder of Rescale, said VentureBeat Rescale focuses on two key factors for refactoring existing HPC workloads and tools for running microcircuits in the cloud infrastructure.

First, the lift and shift approach does not work for many HPC applications due to challenges with data gravity, network bandwidth, and complex infrastructure setup. These components are recycled by refactors in software-a-service offerings. Rescale maintains any software that the engineer runs from the catalog. Companies can also bring their software and publish it in catalogs and rescale optimize the architecture for different platforms.

Second, Rescale keeps track of cloud offerings, pricing and performance changes across cloud providers. This helps ensure that each application uses the best architecture, runs in the best regions and pays for the right service level.

McKenzie said, “This built-in cloud approach is reminiscent of how Snowflake redesigned the user’s data warehouse experience in the cloud. What Snowflake did with the data warehouse, Rescale is doing with an accelerated computing stack.

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