"The DAPHNE project aims to define and build an open and extensible system infrastructure for integrated data analysis pipelines, including data management and processing, high-performance computing (HPC), and machine learning (ML) training and scoring." (more information on https://daphne-eu.eu/)
In this repository, we develop the whole DAPHNE system with all its components including, but not limited to DaphneDSL, DaphneLib, DaphneIR, the DAPHNE Compiler, and the DAPHNE Run-time. The system will be built up and extended gradually in the course of the project.
- Find information on getting started in the documentation.
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Read our contribution guidelines.
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Have a look at the online documentation.
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Browse open issues (e.g. "good first issues") or create a new issue.
This section describes the setup and testing for the MNC Sparsity Estimator project. System Specs We ran all tests on the following hardware and software: Processor: 11th Gen Intel(R) Core(TM) i7-1165G7 @ 2.80GHz. Memory: 16.0 GB RAM. System Type: 64-bit OS. Operating System: Windows 11 Home using WSL (Windows Subsystem for Linux). Environment: Run inside a Docker container for consistent results. Testing & Results Dataset: We used real scientific data from the SuiteSparse Matrix Collection instead of random data. Decision Logic: The system uses a 0.25 threshold to decide if a matrix should be stored as SPARSE or DENSE. Reliability: The code passed 2,452 unit tests, proving it works safely with the rest of the DAPHNE system.