MilkyWay@home

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MilkyWay@home
A dwarf galaxy being disrupted by the Milky Way's gravity
Project
StatusActive
CategoryAstrophysics
ComputeCPU
Development
DeveloperHeidi Jo Newberg, Travis Desell, Carlos Varela
SponsorRensselaer Polytechnic Institute
MaintainerMilkyWay@home team
Initial releaseApril 1, 2007  (19 years ago)
Repositoryhttps://github.com/Milkyway-at-home
Software
Written inC, C++, OpenCL
Operating systemWindows, Linux, macOS
Size~50 MB
BOINC statistics
Stats as ofMay 21, 2026  (0 years ago)
Performance200817.71 GigaFLOPS
Active users19,120
Total users257,894
Active hosts61,248
Total hosts1,543,021
Analytics
RAC12,400,000
Credit/day730,000
Metadata
Websitehttps://milkyway.cs.rpi.edu/milkyway/
LicenseGNU GPL

MilkyWay@home is a volunteer distributed computing and distributed computing project operated by the Rensselaer Polytechnic Institute (RPI). The project uses the BOINC platform to harness unused processing power from volunteer computers around the world in order to study the structure and evolution of the Milky Way galaxy, particularly the galactic halo and the distribution of dark matter.[1]

The project is known for extensive use of GPU computing, becoming one of the earliest BOINC projects to heavily support AMD and NVIDIA GPUs for scientific applications.[2]

History

MilkyWay@home was launched in 2007 by researchers at RPI's Department of Computer Science and Department of Physics, Applied Physics, and Astronomy.[3] The project was created to combine astronomical data analysis with volunteer computing technologies developed through the BOINC middleware platform.

The project originally focused on fitting models to the Sagittarius tidal stream, a stellar stream created by the interaction between the Milky Way and a dwarf galaxy. Later research expanded to additional tidal streams, dwarf galaxy simulations, and reconstruction of galactic structure using N-body simulations.[4]

MilkyWay@home gained attention within the BOINC community because of its extremely high GPU utilization and optimized OpenCL applications, which allowed volunteers to achieve very high computational throughput compared to CPU-only projects.[5]

Scientific objectives

The main scientific objective of MilkyWay@home is to better understand the structure and formation history of the Milky Way galaxy through analysis of stellar streams and dwarf galaxies. By modeling the motion and disruption of dwarf galaxies orbiting the Milky Way, researchers can estimate the shape and distribution of dark matter in the galactic halo.[6]

The project primarily studies:

  • The structure of the Milky Way stellar halo
  • Tidal debris streams
  • Dwarf galaxy interactions
  • Galactic gravitational potential
  • Dark matter distribution
  • Stellar density substructures

Why MilkyWay@home?

The N-body project on MilkyWay@home simulates dwarf galaxies colliding with or being disrupted by the Milky Way. The results help researchers understand how dwarf galaxies interact with the Milky Way under varying physical conditions and how these simulated interactions compare with observational astronomical data.

Goal

The goal of the N-body project is to match simulated dwarf galaxies to real dwarf galaxy observations and thereby constrain the properties of the Milky Way galaxy's gravitational potential. Comparing observed baryonic matter distributions with calculated galactic potentials helps scientists estimate the distribution and density of dark matter within the Milky Way.[7]

Methods

MilkyWay@home studies the history of the Milky Way galaxy by analyzing stars in the galactic halo. Many observed stellar structures are believed to be remnants of dwarf galaxies torn apart by the Milky Way's gravitational field. These remnants form tidal debris streams that can be mapped and modeled computationally.[8]

The project uses volunteer computing to process large numbers of simulations in parallel. The N-body subproject creates simulated dwarf galaxies and evolves them within a model of the Milky Way gravitational field. Parameters are adjusted iteratively until the simulations closely resemble observed stellar structures.[9]

Our best map of the Milky Way so far

Applications

MilkyWay@home applications are distributed through the BOINC client and support multiple operating systems and hardware architectures. The project has historically supported:

  • CPU applications
  • NVIDIA GPU applications using CUDA and OpenCL
  • AMD GPU applications using OpenCL
  • Multi-threaded N-body simulations

The GPU applications became particularly popular among volunteer computing enthusiasts due to their exceptionally high credit generation rates and strong floating point performance.[10]

Project team / Sponsors

The project team has included:

  • Heidi Jo Newberg
  • Kevin Roux
  • Hiroka Warren
  • Travis Desell
  • Carlos Varela
  • Malik Magdon-Ismail
  • Boleslaw K. Szymanski

The project is operated by Rensselaer Polytechnic Institute.

Supported by the National Science Foundation under Grant Numbers 0612213, 0607618, 0448407, 1009670, 1615688, and 1908653.

  1. Charting the Structure of the Milky Way Stellar Halo and Disk, NSF AST Grant #1615688, 09/15/2016 - 08/31/2019, Principal Investigator: Heidi Jo Newberg
  2. Stars and Dark Matter in the Halo of the Milky Way, NSF AST Grant #1009670, started 09/15/2010
  3. Data-Driven Discovery of the Milky Way Origin and Evolution from the Sloan Digital Sky Survey, NSF IIS Grant #0612213
  4. Revealing the Structure of the Galactic Halo through Statistical Analysis - Middle School Teacher Training
  5. Middleware and Programming Technology for Grid Computing, NSF CAREER Grant #0448407
Milky Way image
A dwarf galaxy being disrupted by the Milky Way's gravity (the Milky Way is not shown, and would be at the center of the picture)

BOINC statistics

As of May 2026, MilkyWay@home remains one of the largest astronomy-focused BOINC projects. The project has historically maintained a strong GPU user base due to efficient OpenCL applications and high throughput workloads.[11]

Scientific publications

Major journal papers

  1. Eric J. Mendelsohn. Using MilkyWay@home to Measure the Mass of the Orphan-Chenab Stream Progenitor Dwarf Galaxy. PhD thesis. Rensselaer Polytechnic Institute, 2022.
  2. Eric J. Mendelsohn, Heidi Jo Newberg, Siddhartha Shelton, Lawrence M. Widrow, Jeffery M. Thompson, Carl J. Grillmair. Estimate of the Mass and Radial Profile of the Orphan-Chenab Stream's Dwarf-galaxy Progenitor Using MilkyWay@home. The Astrophysical Journal, 2022.
  3. Siddhartha Shelton et al. An Algorithm for Reconstructing the Orphan Stream Progenitor with MilkyWay@home Volunteer Computing. The Astrophysical Journal, 2021.
  4. Heidi Jo Newberg et al. Streams and the Milky Way Dark Matter Halo. International Astronomical Union, 2020.
  5. Jake Weiss, Heidi Jo Newberg, Travis Desell. A Tangle of Stellar Streams in the North Galactic Cap. The Astrophysical Journal, 2018.
  6. Jake Weiss, Heidi Jo Newberg, Matthew Newby, Travis Desell. Fitting the Density Substructure of the Stellar Halo with MilkyWay@home. The Astrophysical Journal, 2018.
  7. Heidi Jo Newberg, Matthew Newby, Travis Desell, Malik Magdon-Ismail, Boleslaw Szymanski, Carlos Varela. MilkyWay@home: Harnessing Volunteer Computers to Constrain Dark Matter in the Milky Way. Proceedings of the International Astronomical Union, 2014.
  8. Matthew Newby et al. Spatial Characterization of the Sagittarius Dwarf Galaxy Tidal Tails. Astronomical Journal, 2013.
  9. Nathan Cole et al. Maximum Likelihood Fitting of Tidal Streams with Application to the Sagittarius Dwarf Tidal Tails. Astrophysical Journal, 2008.

Computer science and volunteer computing papers

  1. Travis Desell et al. Robust Asynchronous Optimization for Volunteer Computing Grids. IEEE e-Science 2009.
  2. Travis Desell et al. Accelerating the MilkyWay@Home volunteer computing project with GPUs. PPAM 2009.
  3. Travis Desell et al. Validating Evolutionary Algorithms on Volunteer Computing Grids. DAIS 2010.
  4. Travis Desell et al. An Analysis of Massively Distributed Evolutionary Algorithms. IEEE CEC 2010.
  5. Nathan Cole et al. A Study of the Sagittarius Tidal Stream Using Maximum Likelihood. ADASS XVIII, 2009.

See also

External links

References

  1. ↑ MilkyWay@home. Rensselaer Polytechnic Institute. Retrieved 2026-05-21}.
  2. ↑ (2009})."Accelerating the MilkyWay@Home volunteer computing project with GPUs".
  3. ↑ BOINC Publications and Papers. University of California, Berkeley. Retrieved 2026-05-21}.
  4. ↑ Cole, Nathan.(2008}).Maximum Likelihood Fitting of Tidal Streams with Application to the Sagittarius Dwarf Tidal Tails. Astrophysical Journal. pp. 750–766.
  5. ↑ (2009})."Accelerating the MilkyWay@Home volunteer computing project with GPUs".
  6. ↑ Newberg, Heidi Jo.(2014}).MilkyWay@home: Harnessing Volunteer Computers to Constrain Dark Matter in the Milky Way. Proceedings of the International Astronomical Union.
  7. ↑ Mendelsohn, Eric J..(2022}).Estimate of the Mass and Radial Profile of the Orphan-Chenab Stream's Dwarf-galaxy Progenitor Using MilkyWay@home. The Astrophysical Journal.
  8. ↑ Weiss, Jake.(2018}).A Tangle of Stellar Streams in the North Galactic Cap. The Astrophysical Journal.
  9. ↑ Shelton, Siddhartha.(2021}).An Algorithm for Reconstructing the Orphan Stream Progenitor with MilkyWay@home Volunteer Computing. The Astrophysical Journal.
  10. ↑ MilkyWay@home Applications. RPI. Retrieved 2026-05-21}.
  11. ↑ Server Status. MilkyWay@home. Retrieved 2026-05-21}.
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