Various industries have adopted or are in the planning and evaluation phase for using the cloud for HPC applications. Within the realm of technical computing, certain workloads are suited to a cloud-based HPC environment. Workloads could be considered either loosely coupled or tightly coupled. In each of the industries discussed in this article, multiple jobs submitted with different input parameters would be loosely coupled and not require a low-latency, high-speed interconnect, while a job that requires the use of multiple systems working in concert would be tightly coupled and need InfiniBand (IB).
In the pantheon of HPC grand challenges, weather forecasting and long term climate simulation rank right up there with the most complex and computationally demanding problems in astrophysics, aeronautics, fusion power, exotic materials, and earthquake prediction, to name just a few. This special reports looks at how HPC takes on the challenge of global weather forecasting and climate research.
In the past few years, accelerated computing has become strategically important for a wide range of applications. To gain performance on a variety of codes, hardware developers and software developers have concentrated their efforts to create systems that can accelerate certain applications by significant amount compared to what was previously possible.
This article describes the challenges that users face and the solutions available to make running cloud based HPC applications a reality. You’ll learn about different cloud computing models, potential economic savings and factors to consider when comparing an on-site data center with a cloud-based provider.
Cloud computing is changing the way that IT organizations operate and innovate. While moving enterprise type applications to a cloud service provider is progressing, technical computing has been lagging in this transformation. This is changing, as the technology that once sat in a protected data center is now available in the cloud.
For some applications, cloud based clusters may be limited due to communication and/or storage latency and speeds. With GPUs, however, these issue are not present because application running on cloud GPUs perform exactly the same as those in your local cluster — unless the application span multiple nodes and are sensitive to MPI speeds. For those GPU applications that can work well in the cloud environment, a remote cloud may be an attractive option for both production and feasibility studies.