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.
Users in HPC environments have requirements for using a cloud provider that are different than typical enterprise applications. Learn about the key considerations for ensuring maximum performance for running HPC applications in the cloud.
A recent IDC survey indicated that about 25 percent of sites that ran HPC workloads are using some sort of cloud computing, and that just over 30 percent of the HPC workloads were being performed at cloud sites. There are a number of reasons to consider HPC in the cloud. Here are 5 good ones.
It’s a different kind of computing world out there. The demand for more compute performance for applications used by engineering, risk modeling, or life sciences is relentless. So, how are you keeping up with modern HPC demands? Meet Apollo – creating next-gen HPC and super-computing.
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.
While there is much discussion and products in the market regarding cloud computing and the ability to spin up a virtual machines quickly and efficiently, the fact remains that without planning for cloud based storage, the data will get lost. Simply put, without storage, there is no data.
High Performance Computing (HPC) in the cloud has become a hot topic with new offerings targeted at this market. The demands of technical computing professional to use the cloud for HPC workloads are different than that of a general enterprise software requirement. Performance is key, which requires a different infrastructure at the cloud providers premises.
As an open source tool designed to navigate large amounts of data, Hadoop continues to find new uses in HPC. Managing a Hadoop cluster is different than managing an HPC cluster, however. It requires mastering some new concepts, but the hardware is basically the same and many Hadoop clusters now include GPUs to facilitate deep learning.