daisy-exact-search 1.0.8

DaiSy: A Library for Scalable Data Series Similarity Search

DaiSy

A Library for Scalable Data Series Similarity Search

Francesca Del Gaudio, Manos Chatzakis, Gayathiri Ravendirane, Botao Peng, Themis Palpanas

Exact similarity search over large collections of data series is a fundamental operation in modern applications, yet existing solutions are often fragmented, specialized, or tailored to specific execution environments. We present DaiSy (Data series similarity Search library), a unified library for exact data series similarity search that integrates multiple state-of-the-art algorithms within a single, coherent framework. DaiSy is the first library to support exact similarity search across diverse execution environments, including implementations for disk-based, in-memory, GPU-accelerated, and distributed scalable similarity search. The library supports interfaces in both C++ and Python, enabling, researchers and practitioners to easily integrate its functionality in a variety of tasks.

ALPHA VERSION: Currently, DaiSy is experimental. The library is still under active development. We welcome suggestions and bug reports.

Supported State-of-the-Art algorithms

We currently support several algorithms for exact similarity search, each optimized for specific use cases and environments. The following table summarizes the key features of each algorithm:

Algorithm	Description
Bruteforce	Naive parallel similarity search implementation
Lower Bound Bruteforce	Optimized bruteforce with lower bounding for the distance calculations
MESSI	In-memory parallel similarity search
PARIS	Disk-based parallel similarity search
SING	GPU-accelerated in-memory parallel similarity search
Odyssey	Distributed and parallel in-memory similarity search

Quickstart

Dependencies

• Operating System: Linux, macOS, or Windows
• C++ Compiler: C++14 or higher (GCC 6+, Clang 3.4+, MSVC 2015+)
• CMake: Version 3.15 or higher

Optionally,

• Python: 3.10-3.12
• MPI: Required for Odyssey distributed computing algorithm
• CUDA: Required for SING GPU acceleration algorithm

Installation

To download DaiSy, use:

git clone https://github.com/MChatzakis/daisy.git

cd daisy
git submodule update --init --recursive

Based on the available hardware, you can specify the below arguments to enable/disable features.

Flag	Description	Default	Dependencies
BUILD_PYTHON	Enable Python bindings	OFF	Python 3.10+
BUILD_BENCHMARK	Build benchmarking tools	OFF	GoogleBenchmark
BUILD_TESTS	Build test suite	OFF	GoogleTest
BUILD_DEMO	Build demonstration applications	ON	Core library
BUILD_ODYSSEY	Enable MPI for distributed computing	OFF	OpenMPI/MPICH
BUILD_SING	Enable CUDA for GPU acceleration	OFF	CUDA Toolkit
DEBUG_MSG	Enable debug output	OFF	None

To compile:

mkdir build && cd build

cmake ..
make

DaiSy with Python

If you intent to use only the Python interface, you can install the library directly from PyPI using pip:

pip install daisy-exact-search

If you want to use Odyssey, you will need to install mpi:

pip install daisy-exact-search[mpi]

Compatibility issues

Kindly note that we are aware for compatibility issues related to ARM processors (e.g., Apple MX processors). Due to pthread-barriers and SIMD being unavailable on ARM, we currently noticing compilations failling on ARM machines. We are currently working on possible solutions, however we recommend using DaiSy on non-ARM machines for the time being.

Others

We provide several usage examples in both C++ and Python under demos/, demonstrating how to utilize the library for various similarity search tasks. We provide several troubleshooting guides and extra resources in the docs/ directory. In this directory, we also provide useful information about how to contribute to the project, and how to implement new algorithms.

About

Work supported by ΥΠ AI ΘΑ & NextGenerationEU project HARSH (ΥΠ3ΤΑ-0560901).

The logo of DaiSy was designed by Eva Chamilaki.