If your company needs high-performance computing for its big data, an in-house operation might work best. Here’s what you need to know, including how high-performance computing and Hadoop differ.
In the big data world, not every company needs high performance computing (HPC), but nearly all who work with big data have adopted Hadoop-style analytics computing.
The difference between HPC and Hadoop can be hard to distinguish because it is possible to run Hadoop analytics jobs on HPC gear, although not vice versa. Both HPC and Hadoop analytics use parallel processing of data, but in a Hadoop/analytics environment, data is stored on commodity hardware and distributed across multiple nodes of this hardware. In HPC, where the size of data files is much greater, data storage is centralized. HPC, because of the sheer volume of its files, also requires more expensive networking communications, such as Infiniband, because the size of the files it processes require high throughput and low latency.