KerP

KerP is a software framework designed to orchestrate kernel-level data processing tasks in resource-constrained and real-time environments. The project centers on the idea of treating compute units as kernels that can be connected into pipelines and data streams, enabling high throughput with deterministic latency. KerP emphasizes low overhead, reproducible scheduling, and modular portability across diverse hardware.

The name KerP stands for Kernel-based Processing, reflecting its focus on kernel-oriented computation rather than high-level operators. It is used as the project identifier rather than a formal industry standard, and its development is led by an international community of researchers and practitioners.

Origin and development: KerP originated from academic work in embedded and real-time systems during the 2010s. Early prototypes demonstrated tight integration with heterogeneous hardware such as multicore CPUs and accelerators. A community-driven ecosystem emerged, offering reference implementations, tooling, and documentation, with ongoing contributions to portability and debugging aids.

Architecture and features: The framework exposes kernels with defined input and output ports and supports multiple channel types for inter-kernel communication. The runtime provides scheduling policies including fixed priority, deadline-based, and energy-aware modes, along with resource management, fault isolation, and tracing capabilities to analyze performance.

Relationship to other systems: KerP differs from general-purpose data processing frameworks by its kernel abstraction and focus on determinism and hardware abstraction. It is designed to complement higher-level orchestration layers and can interoperate with standard data formats through adapters.

Applications and limitations: KerP is used in real-time analytics, robotics, industrial automation, and scientific instrumentation where latency guarantees are important. Potential challenges include the learning curve, portability concerns across platforms, and the need for careful memory and resource management in kernel space.