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.
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.
HPC developers want to write code and create new applications. The advanced nature of HPC often requires that this process be associated with specific hardware and software environment present on a given HPC resource. Developers want to extract the maximum performance from HPC hardware and at the same time not get mired down in the complexities of software tool chains and dependencies.
NVIDA’s Sanford Russell writes that Microsoft’s recent work on a new programming language extension called C++ AMP will accelerate the adoption of GPU computing. He contends that the move will push programmers get off the fence and go with GPU computing. “The take away from Microsoft’s announcement today is that the GPU computing space has reached maturity, […]