Introduction Field Programmable Gate Arrays (FPGAs) as accelerators for data center workloads are beginning to cross the chasm of broader adoption. FPGAs have been around for more than twenty-five years and have successfully accelerated IO-centric applications such as network routers and storage controllers. FPGAs are reconfigurable hardware that offer software-like flexibility while delivering hardware-like performance Read more...
Are you being asked to provide GPUs to your application developers and data scientists for machine learning or high performance computing? Are users asking for more than one GPU to be usable for their application? Are you interested in cost-effective ways to share GPUs across the entire data science team? If any of these types Read more...
Graphics Processing Units (GPUs) represent a key accelerator technology on vSphere for those who are working in the fields of machine learning (ML) and high performance computing (HPC). Traditionally, GPUs have been used by those who want to do high-end graphics, such as product design/CAD in VDI workloads on vSphere. We place our focus here Read more...
While virtualization technologies have proven themselves in the enterprise with cost effective, scalable and reliable IT computing, High Performance Computing (HPC) however has not evolved and is still bound to dedicating physical resources to obtain explicit runtimes and maximum performance. High Performance Computing (HPC) workloads have been traditionally run on bare-metal, non-virtualized clusters. Virtualization can Read more...
This brief article introduces a short 4.5 minute video that explains the reasons why VMware vSphere is a great platform for data scientists/engineers to use as their base operating platform. The video then demonstrates an example of this, showing a data scientist conducting a modeling experiment with an input set of data, while using the Read more...
