Google developed this capability as part of its Machine Learning work and open sourced the tool as a generic analysis platform that can be applied to many different domains and problem sets .
• TPU - Google worked to design and deploy the Tensor Processing Units inside its data centers to accelerate machine learning workloads . The TPU is a custom ASIC , specifically optimized for machine learning workloads .
Google also continues to build its partner ecosystem , with some partners focused on HPC . One partner
with key capability is CycleComputing , which provides the ability to easily schedule HPC workloads to run on a variety of cloud providers , including Google .
While AWS and Azure provide rich sets of IaaS functionality , complemented by HPC specific technologies , Google has taken a path of PaaS ( Platform as a Service ) capabilities with Google Genomics and Google Machine Learning . This allows organizations to analyze large , complex data sets without having to deploy , configure and manage IaaS services . Google ’ s approach is unique and will inevitably continue to be expanded to additional domain .
Azure refers to HPC-centric workloads as Big Compute . This is to distinguish the HPC workloads that are processor and interconnect intensive , from Big Data workloads that have very different communication patterns from HPC . The Azure Big Compute capabilities on Azure cover several specific domains .
• Engineering Design and Simulation Simulations , including finite element analysis , structural analysis and computational fluid dynamics , which commonly support product design and validation .
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