The Arka runtime is Megh’s data streaming framework. Arka enables applications to build custom data pipelines spanning multiple devices and accelerators. Low-level details are abstracted away by Arka’s resource manager, which maps an application’s pipeline request to the pool of available hardware. This technology enables low-latency, low-overhead data streaming over complex functional topologies through Arka’s easy-to-use API.
Let’s examine the video processing use case further. A video processing pipeline includes a data source, a video decoder, one or more functions that operate on the video frames, and a data sink.
On the surface, this looks straightforward, a set of sequential pipeline stages. However, after drilling down into each stage, the complexities become apparent. Each stage consists of one or more functions, and each function may have multiple inputs and outputs.
Adding another dimension to this problem, these functions may be performed on a local CPU or some other networked device or accelerator.
Arka strips away complexity by providing a simple set of APIs that enable applications to build and use data pipelines without having to worry about how or where each function is implemented.
The application presents Arka with a high-level representation of a data pipeline to instantiate. Arka does the heavy lifting by mapping the application’s request to the pool of available resources. Once the resources are reserved, the runtime configures each function’s forwarding table. As data moves through the pipeline, it gets routed directly from function to function. When consecutive functions are implemented on hardware accelerators, a costly trip to the host CPU is avoided.
This year Arka will support:
- Multi-node pipelines: Streaming data through multiple servers will allow you to build even larger and more powerful pipelines
- Edge-to-cloud pipelines: Feeding data into a pipeline from the edge removes the need for additional messaging libraries
Arka’s heterogeneous pipeline management capabilities make the Megh platform flexible, scalable, and easy to use. Check back soon for the next article in this series, which discusses Arka APIs in more detail.
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