FAMAD
From HP-SEE Wiki
General Information
- Application's name: Fractal Algorithms for MAss Distribution
- Application's acronym: FAMAD
- Virtual Research Community: Computational Physics
- Scientific contact: Ciprian Mihai Mitu, cmitu@spacescience.ro
- Technical contact: Ciprian Mihai Mitu, cmitu@spacescience.ro
- Developers: High Energy Astrophysics and Advanced Tehnologies, Institute of Space Sciences, Romania
- Web site: http://wiki.hp-see.eu/index.php/FAMAD
Short Description
FAMAD is a collection of algorithms to compute different fractal parameters for mass distribution. This type of algorithms are highly parallizable and can analyse large data customary on clusters. The input data will be high resolution images in the most used format in astrophysics, FITS. The application will use a box counting algorithm running on GPU (with CUDA), for the determination of fractal dimension. Data analysis and ploting will be done in ROOT Framework.
Problems Solved
Fractal algorithms are generally highly parallelizable. This application implements different algorithms on GPU (such as box counting, fractal lacunarity and correlation length) for fractal study. Due to the architecture of the graphical processors, the application will run simulatatiously thousands of threads in parallel. Thus, fractal analysis will be much faster than on CPU.
Scientific and Social Impact
General scientific image on the formation and evolution of the galaxies is that they formed by gravitational collapse of matter. The study of the fractal mass distribution of spiral galaxies will shade new light on the formation and evolution of galaxies. The application will provide new data of spiral arms and their fractalness. We hope that at the end of this study, the general scientific view of the formation and evolution of spiral galaxies will change radically.
Collaborations
- Computational astrophysics
Beneficiaries
Number of users
Development Plan
Resource Requirements
- Number of cores required: 4x480 GPUs, 32 CPUs
- Minimum RAM/core required: 1.5GB/video card, 2GB/core
- Storage space during a single run: 100MB
- Long-term data storage: 1TB
Technical Features and HP-SEE Implementation
- Primary programming language: C++
- Parallel programming paradigm: GPGPU
- Main parallel code: CUDA
- Pre/post processing code: CUDA
- Application tools and libraries: Enumerate (comma separated)
Usage Example
Infrastructure Usage
- Home system: HPCG/BG
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- Accessed production systems:
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Running on Several HP-SEE Centres
- Benchmarking activities and results: .
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