SparseViz Documentation

Introduction

Welcome to the documentation of SparseViz, a comprehensive library designed for efficient and intuitive handling of sparse data structures. In the realm of data processing and analysis, sparse data structures are pivotal for managing datasets with predominantly empty or zero-valued elements. SparseViz emerges as a versatile tool, bridging the gap between the complexity of sparse data operations and the need for a user-friendly approach.

Core Principle

SparseViz is built upon the principle of simplifying the interaction with sparse data structures. Whether you are a data scientist, a researcher, or a developer working with large, sparse datasets, SparseViz offers a suite of functionalities that cater to your needs. This library not only facilitates the efficient representation and manipulation of sparse data but also extends its capabilities to various operations such as implementing orderings, executing kernels, and visualizing them. Moreover, SparseViz is not just a tool; it's a growing ecosystem. We've designed it with extensibility in mind, allowing for continuous development and integration of new features and methods.

Key Features of SparseViz

As indicated, SparseViz is not just a tool; it is a dynamic and expanding ecosystem thoughtfully crafted to cater to a wide range of applications involving sparse data structures. Its capabilities are diverse and powerful, designed to address the various challenges and needs encountered in working with sparse data. Here are the key features that make SparseViz an indispensable library:

  • Representation of Sparse Data Structures: SparseViz excels in the efficient handling and representation of sparse matrices and tensors, making it easier to work with large, sparse datasets.

  • Comprehensive Operations: The library supports a broad spectrum of operations on sparse data structures. From basic manipulations to more complex transformations, SparseViz ensures that these operations are both efficient and intuitive.

  • Flexible Ordering Systems: SparseViz allows for customizable ordering of sparse data structures. This feature is particularly useful for optimizing data (to increase cache-hit) for specific algorithms or processing techniques.

  • Kernel Execution: With SparseViz, users can execute their customized kernels on sparse data. This functionality is essential for understanding the efficiency of the implemented orderings in practical terms.

  • Efficient Storage Solutions: The library offers optimized storage solutions for sparse data. This ensures that large datasets are not only stored efficiently but are also easily retrievable for future use.

  • Advanced Visualization Tools: One of the standout features of SparseViz is its capability to visualize sparse data structures. This tool is invaluable for understanding complex data patterns and for communicating findings in a clear and impactful way.

Extensibility

At SparseViz, we encourage and support our users to not only use the existing functionalities but to also contribute to the library's growth by implementing their orderings, writing custom kernels, and extending the library in various ways. To facilitate this, we have established a robust infrastructure that allows for seamless customization and extension. There are four primary ways you can customize and extend the library:

  1. Adding Matrix Ordering

  2. Adding Tensor Ordering

  3. Adding Matrix Kernels

    3.1. Adding CPU Matrix Kernels

    3.2. Adding GPU Matrix Kernels

  4. Adding Tensor Kernels

    4.1. Adding CPU Tensor Kernels

    4.2. Adding GPU Tensor Kernels

Before delving into the specifics of each extension method, we will first introduce you to the config file. The config file is a crucial component of SparseViz, allowing for tailored library configuration and setup. Understanding its role and usage is key to effectively extending and customizing SparseViz.