May 2022 Summaries

Traditional high-performance computing (HPC) approaches often result in isolated, siloed systems due to their reliance on bespoke technology stacks and a focus on maximizing the utilization of expensive infrastructure without prioritizing data sharing or collaboration. This can hinder digital transformation efforts within organizations. In contrast, cloud-based HPC leverages the inherently connected nature of the cloud to enhance standardization, collaboration, and performance optimization across multiple business units. By utilizing cloud capabilities, organizations can apply best practices and policy changes instantly, significantly improving engineering velocity. Cloud-based HPC allows for seamless access to resources, applications, and data, fostering a unified environment that accelerates innovation and engineering processes. Rescale exemplifies this approach by integrating computing, storage, applications, and workflows, thus unifying analysis efforts and boosting efficiency. The blog post, written by Garrett VanLee and Edward Hsu, highlights the transformative potential of cloud-based HPC in advancing scientific and industrial discoveries through the convergence of supercomputing, HPC, and AI simulation models.

The blog post introduces a series discussing the evolution of High-Performance Computing (HPC) from traditional, inflexible models to more adaptable cloud-based approaches. Traditional HPC systems are limited by their tightly coupled software and hardware configurations, making them costly and challenging to adapt to different infrastructures, software versions, or cloud providers. In contrast, cloud-based HPC offers a vast array of options, including diverse architectures like x86, GPU, and FPGA, and supports various deployment models such as virtual, containerized, or bare metal. This flexibility allows organizations to optimize performance and cost for engineering workloads by choosing the most suitable architecture, application, or cloud provider. Rescale's intelligent cloud HPC platform exemplifies this adaptability by enabling users to run any workload type on their preferred cloud infrastructure and supports all major cloud providers. The author, Garrett VanLee, emphasizes the convergence of supercomputing, HPC, and AI simulation models as critical trends driving scientific and industrial advancements.

The blog post introduces a six-part series that examines the shift from traditional hardware-centric High-Performance Computing (HPC) to a user-centric model, emphasizing the importance of user productivity over hardware utilization. Traditional HPC models prioritize maximizing hardware usage to justify costs, often leading to competition among engineers for resources. In contrast, a user-centric approach focuses on the strategic impact of engineering productivity on business outcomes, providing engineers with the necessary tools, data, and policies to enhance decision-making and efficiency without the need for resource escalation. The post highlights the importance of aligning HPC environments with business priorities, security policies, and evolving technology trends to optimize both performance and economic considerations. It discusses Rescale’s approach to automating HPC environments, allowing engineers to leverage cloud-based infrastructure seamlessly, thereby enabling them to concentrate on critical engineering tasks. The author, Garrett VanLee, is involved in product marketing at Rescale and focuses on the intersection of supercomputing, HPC, and AI simulation models to drive innovation across industries.

Empowering new product development through faster innovation is a strategic priority for enterprises, yet commercializing scientific and engineering breakthroughs remains challenging due to the increasing complexity of products and the reliance on high-performance computing (HPC). The digital transformation facilitated by cloud technology has only just begun to impact engineering and R&D, with many organizations struggling to achieve desired productivity gains through traditional HPC methods. A reconceptualization towards cloud-based HPC is necessary, optimizing for R&D throughput rather than merely hardware efficiency. This transformation involves embracing user-centric, connected, and automated systems that leverage the cloud's capabilities, such as ease of access to the latest technologies and enhanced collaboration opportunities. The acceleration of cloud HPC adoption, driven by the rise of AI and deep learning, is expected to significantly grow the global HPC market. However, successful transformation requires moving beyond a lift-and-shift approach to cloud, necessitating a focus on new levels of automation and user-centric strategies to fully harness the potential of cloud operating models for engineering and R&D.