Tensor

The core tensor abstraction in the SKaiNET framework, representing a fundamental architectural decision to compose tensors from data and operations.

Fundamental Architectural Decision: Tensor Composition

This interface embodies a key architectural principle in the SKaiNET framework: tensors are composed of two distinct, complementary components:

1. Data Component (TensorData) - Responsible for:

• Multi-dimensional data storage and indexing

• Memory layout and access patterns

• Shape and dimensional metadata

• Type-safe element access

2. Operations Component (TensorOps) - Responsible for:

• Mathematical operations and transformations

• Computational algorithms

• Operation chaining and composition

• Performance-optimized implementations

Benefits of This Compositional Architecture

Separation of Concerns: Data management is cleanly separated from computational logic, making the codebase more maintainable and easier to understand.

Flexibility: Different data storage strategies (dense, sparse, distributed) can be combined with different operation implementations (CPU, GPU, specialized hardware) without tight coupling.

Performance Optimization: Data layout can be optimized independently from computational algorithms, enabling targeted performance improvements.

Extensibility: New data formats or operation types can be added without modifying existing code, following the open-closed principle.

Testability: Data and operations can be tested independently, improving test coverage and reducing complexity.

Usage Pattern

The tensor acts as a unified interface that delegates data access to the data component and computational operations to the ops component, providing a seamless experience while maintaining the benefits of compositional design.

Parameters

Inheritors