High-performance computing (HPC) technologies typically use multiple machines to enable computationally intensive tasks, such as AI, and render them more manageable.
HPC began solely in the domain of supercomputers, but over time it has come to serve as the basis for computer cluster architectures that have arisen because of low-cost microprocessors, high-speed networks, and software that supports distributed computing. Alleviating limitations surrounding memory, compute, and storage allows for faster analytics and greater computational demands.
HPC With Katana
Jelani Harper, an editorial consultant for Inside Big Data, recently sat down with Katana Graph Principal Engineer Bo Wu to discuss some architectural foundations of the HPC methods featured in the company’s platform. Wu identified three primary components, “a partitioner, a runtime scheduler, and a communication engine,” that form a scalable, high-performance knowledge graph engine. The knowledge graph engine is responsible for storing data in a graph structure — an organizational method in which data is loosely structured — and performing computations. Katana Graph’s technology enables graph processing in an HPC environment that allows for massively parallel computation.
Wu explained that a partitioner structures the graph into overlapping subsets on different machines to distribute computational processes. This method allows for massive scalability, with workloads distributed across multiple machines to boost performance. The runtime scheduler ensures that machines are processing the agreed-upon tasks at the right times, optimizing computation resources to maximize performance. Lastly, the communication engine enables the coordination of computation demands between machines for faster results.
Benefits of HPC
The Katana Graph Intelligence Platform's partitioner, runtime capabilities, and communication engine comprise an HPC architecture for rapid computation on the scalable graph engine. This HPC then provides the fastest analytics available to organizations, whether that analytics includes AI or not. This architecture is necessary for truly fast computations on huge data sets.
Rapid computations enabled by an architecture with HPC capabilities allow for better and more timely analytics. Forward-thinking enterprises leverage technology to their best advantage, and HPC via a more scalable knowledge graph engine delivers bottom-line results at enterprise scale.
Whether structured or unstructured data, Katana Graph can greatly improve the insights and opportunities uncovered from your organization’s data. Click to speak with a Graph Expert.
