August 2023 Summaries

Rescale's engineering team focuses on managing the complexities of high performance computing (HPC) systems in a hybrid and multi-cloud environment, emphasizing automation in setting up and running simulation jobs. The intricacies of HPC batch job management include scheduling, security, troubleshooting, and understanding cloud-specific requirements. Successful HPC management demands expertise in hardware configuration, software setup, and ongoing system maintenance, both on-premises and in the cloud. Faulty setups can lead to significant time and resource losses, necessitating a skilled team to ensure reliability and efficiency. Security is crucial, especially given the sensitive nature of R&D data, requiring careful management of user access and data protection. Multi-cloud HPC presents additional challenges due to varying configurations and standards across providers, requiring precise infrastructure management to optimize performance and control costs. Mastery of HPC is vital for advancing digital R&D, providing a competitive edge in innovation and product development.

Automotive manufacturers are increasingly adopting digital simulations to enhance innovation and meet rigorous safety and regulatory standards, with cloud-based high-performance computing (HPC) playing a crucial role. Experts from Rescale, AWS, and AMD highlight the advantages of cloud HPC, including massive capacity, elasticity, continuous upgrades, and global scope, which eliminate the limitations of traditional on-premises supercomputing resources. The cloud's pay-per-use model and advanced chip architectures enable organizations to run faster simulations, reduce costs, and focus on product development rather than computing management. This approach accelerates innovation by allowing more simulations and design explorations, ultimately leading to faster product development. Organizations, including Formula One teams, report significant improvements in speed and cost-efficiency with cloud HPC, and platforms like Rescale provide automated, centralized, and secure resources for R&D teams.

Rescale has announced the availability of Amazon EC2 Hpc7g instances, known as "Pectolite," on its high-performance computing platform, allowing users to leverage AWS's Arm-based Graviton3E chip architecture for enhanced R&D innovation. The Hpc7g instances boast significant improvements in cost-performance and energy efficiency, making them suitable for compute-intensive applications such as large simulations and deep learning tasks. These instances are powered by custom-designed AWS Graviton3E processors, offering up to 64 vCPUs, 128 GiBs of memory, and 200 Gbps of network bandwidth, thereby facilitating complex computational tasks. By integrating Hpc7g, Rescale empowers R&D professionals with the ability to efficiently manage demanding computational workloads while focusing on innovation and reducing their carbon footprint. The collaboration between AWS and Rescale opens up new opportunities for industries such as aerospace, automotive, and life sciences to achieve R&D breakthroughs through advanced digital engineering methods.

High-throughput computing (HTC) is gaining traction across various industries for its capability to handle complex scientific and engineering challenges by enabling massively parallel processing of vast data quantities. Companies like Arm leverage Rescale's cloud-based HTC platform to enhance research and development through multi-cloud automation, unified data management, and robust security. This platform allows organizations to efficiently execute HTC projects with improved throughput, cost efficiency, and reliability, benefiting fields such as drug discovery, materials science, and environmental research. Despite the complexity of setting up HTC systems, Rescale facilitates access to optimal hardware infrastructures across leading cloud providers, ensuring seamless operation and scalability. The platform's AI-based Compute Recommendation Engine further optimizes job execution by adopting the best hardware architectures for specific workloads, which significantly speeds up processing and analysis tasks. As HTC technology evolves, it promises to play a vital role in enabling breakthrough scientific discoveries and innovation in the future.

Autonomous driving is poised to revolutionize transportation by enhancing safety, efficiency, and passenger experience, while also aligning with environmental goals through reduced accidents and improved fuel optimization. A key feature is the ability of autonomous fleets to employ drafting techniques, where vehicles drive closely aligned to reduce aerodynamic drag, thus conserving energy. This concept, validated in sports like NASCAR and cycling, can be applied to everyday driving, with simulations showing potential fuel efficiency gains. However, practical challenges remain, such as maintaining safe distances to prevent accidents. The use of early-stage physics simulations and AI-enhanced platforms like Rescale allows engineers to explore the feasibility and optimize the design of autonomous systems, offering a sandbox for innovation and ensuring robust product development. By leveraging machine learning and advanced simulation tools, engineers can evaluate various design scenarios, enhancing the precision and agility of autonomous vehicle development.

Rescale's engineering team is tackling the complexities of managing high-performance computing (HPC) systems in hybrid and multi-cloud environments, focusing on automating the setup of compute jobs for digital simulations and analyses. HPC batch jobs differ significantly from typical IT batch jobs, emphasizing speed, efficiency, and the optimal configuration of hardware and software to ensure performance and reliability. These jobs often require significant computational resources and can involve thousands of CPU cores across numerous nodes, making the correct setup of network fabric and hardware critical. The choice of hardware, such as chip architecture and network fabric like InfiniBand, has profound implications for cost, speed, and energy consumption, as does the use of middleware like MPI. Ensuring optimal inter-node communication is crucial for performance, as HPC jobs are often more network-bound than compute-bound. The first part of this blog series covers essential aspects of HPC batch job configuration, with a promise of deeper exploration into scheduling, costs, security, and multi-cloud management in the upcoming second part.

Rescale is a platform designed to facilitate secure and efficient collaboration in high-performance computing (HPC) environments, tailored to the needs of research and development (R&D) teams across various industries. It addresses the complexities of managing access and sharing HPC resources by offering a centralized, controlled environment that supports different collaboration models: tightly coupled, loosely coupled, and blended. Tightly coupled collaboration is ideal for teams that require high trust and shared access to resources, allowing them to work closely within a single workspace while managing security and budgets. Loosely coupled collaboration provides complete isolation for users, ensuring that each has access only to specific resources without visibility into others' data, similar to a public cloud service. The blended approach combines elements of both, facilitating collaboration among trusted partners while maintaining necessary separations and controls, exemplified by a global oil and gas company that uses Rescale to manage regional workspaces. Rescale's platform automates management tasks, enhances security, and ensures compliance, enabling organizations to streamline their R&D efforts and protect intellectual property.