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<div style="background-color: #D4E2FC; border-top: 1px solid #5F92F2; font-size: bigger; padding-left: 15px; margin: 12px -5px -5px -5px;">'''BOINC project page template'''</div>
{{Infobox software
| name                = MilkyWay@home
| logo                = Mw.png
| logo caption        = MilkyWay@home logo
| screenshot          = Milkyway.gif
| caption              = A dwarf galaxy being disrupted by the Milky Way's gravity


[[File:{{#setmainimage:Mw.png}}|alt=MilkyWay@home image|center|frameless]]
| status              = Active
| category            = Astrophysics
| compute              = CPU
| dependencies        =


[https://milkyway.cs.rpi.edu/milkyway/ '''''MilkyWay@home'''''] is a '''''[[wikipedia:Volunteer computing|volunteer distributed computing]]''''' project based at [https://www.rpi.edu/ '''''Rensselaer Polytechnics Institute'''''] that needs your help to optimize N-body simulations of dwarf galaxies around the Milky Way.[[File:Milkyway.gif|alt=Milky Way image|thumb|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)]]
| developer            = Heidi Jo Newberg, Travis Desell, Carlos Varela
| author              =
| sponsor              = Rensselaer Polytechnic Institute
| maintainer          = MilkyWay@home team
| released            = {{Start date and age|2007|04|01}}
| repository          = {{URL|https://github.com/Milkyway-at-home}}
 
| programming language = C, C++, OpenCL
| operating system    = Windows, Linux, macOS
| size                = ~50 MB
 
| stats as of          = {{Start date and age|2026|05|21}}
| average performance  = 200817.71 GigaFLOPS
| active users        = 19120
| total users          = 257894
| active hosts        = 61248
| total hosts          = 1543021
 
| rac                  = 12400000
| credit per day      = 730000
| gpu performance      =
| cpu performance      =
 
| website              = {{URL|https://milkyway.cs.rpi.edu/milkyway/}}
| license              = GNU GPL
}}
 
[https://milkyway.cs.rpi.edu/milkyway/ '''''MilkyWay@home'''''] is a '''''[[wikipedia:Volunteer computing|volunteer distributed computing]]''''' and '''''[[wikipedia:Distributed computing|distributed computing]]''''' project operated by the [[wikipedia:Rensselaer Polytechnic Institute|Rensselaer Polytechnic Institute]] (RPI). The project uses the [[wikipedia:Berkeley Open Infrastructure for Network Computing|BOINC]] platform to harness unused processing power from volunteer computers around the world in order to study the structure and evolution of the [[wikipedia:Milky Way|Milky Way galaxy]], particularly the galactic halo and the distribution of [[wikipedia:Dark matter|dark matter]].<ref>{{cite web |url=https://milkyway.cs.rpi.edu/milkyway/ |title=MilkyWay@home |publisher=Rensselaer Polytechnic Institute |access-date=2026-05-21}}</ref>
 
The project is known for extensive use of [[wikipedia:Graphics processing unit|GPU computing]], becoming one of the earliest BOINC projects to heavily support AMD and NVIDIA GPUs for scientific applications.<ref>{{cite conference |last=Desell |first=Travis |title=Accelerating the MilkyWay@Home volunteer computing project with GPUs |book-title=Parallel Processing and Applied Mathematics |year=2009}}</ref>
 
== History ==
 
MilkyWay@home was launched in 2007 by researchers at RPI's Department of Computer Science and Department of Physics, Applied Physics, and Astronomy.<ref>{{cite web |url=https://boinc.berkeley.edu/pubs.php |title=BOINC Publications and Papers |publisher=University of California, Berkeley |access-date=2026-05-21}}</ref> 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 [[wikipedia:Sagittarius Dwarf Spheroidal Galaxy|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.<ref>{{cite journal |last=Cole |first=Nathan |title=Maximum Likelihood Fitting of Tidal Streams with Application to the Sagittarius Dwarf Tidal Tails |journal=Astrophysical Journal |volume=683 |pages=750–766 |year=2008}}</ref>
 
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.<ref>{{cite conference |last=Desell |first=Travis |title=Accelerating the MilkyWay@Home volunteer computing project with GPUs |book-title=PPAM 2009 |year=2009}}</ref>
 
== 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.<ref>{{cite journal |last=Newberg |first=Heidi Jo |title=MilkyWay@home: Harnessing Volunteer Computers to Constrain Dark Matter in the Milky Way |journal=Proceedings of the International Astronomical Union |year=2014}}</ref>
 
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? ==
== Why MilkyWay@home? ==
The N-body project on MilkyWay@home simulates dwarf galaxies colliding with (or being disrupted by) the Milky Way. The result from this project will give insight on how dwarf galaxies interact with the Milky Way given certain parameters and how those results look when compared to the data observed today.  
 
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 ==
== Goal ==
The goal of the N-body project is to match simulated dwarf galaxies to real dwarf galaxy data, and thereby constrain the properties of the Milky Way galaxy's gravitational potential (as well as the properties of the dwarf galaxies).
 
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.<ref>{{cite journal |last=Mendelsohn |first=Eric J. |title=Estimate of the Mass and Radial Profile of the Orphan-Chenab Stream's Dwarf-galaxy Progenitor Using MilkyWay@home |journal=The Astrophysical Journal |year=2022}}</ref>


== Methods ==
== Methods ==
MilkyWay@home studies the history of our galaxy by analyzing the stars in the Milky Way galaxy's '''''[[wikipedia:Galactic_halo|Galactic Halo]]'''''. This includes searching for elusive '''''[[wikipedia:Dark_matter|dark matter]]'''''. This research is done by mapping structures of stars orbiting the Milky Way - many these structures are actually "tidal debris streams," or dwarf galaxies that are being pulled apart by our Galaxy's superior gravitational field. The orbits, shapes, and compositions of these dwarf galaxies provide vital clues to the history of our Galaxy, as well as to the distribution of dark matter. Additionally, MilkyWay@home has recently started developing the "N-body" sub-project, which creates simulated dwarf galaxies and "shoots" them into the Milky Way's gravitational field. We allow the simulated dwarf galaxy's initial conditions to vary until the final simulated dwarf matches what we see in actual halo structures. In other words, we are trying to match dwarf galaxy models to real data, in order to learn more about what is (and what isn't) possible for our Galaxy.  For both projects, we use data from the '''''[[wikipedia:Sloan_Digital_Sky_Survey|Sloan Digital Sky Survey]].'''''


Using stellar data from the Sloan Digital Sky Survey (SDSS) and the Dark Energy Camera (DEC), we were able to use MilkyWay@home's N-body project to generate a mass estimate of the Orphan-Cehnab's original progenitor dwarf galaxy, the first time such an estimate has ever been made from tidal debris alone. The Orphan-Chenab Stream (OCS) is a tidal stream that was discovered in 2006 while examining the Sagittarius Stream. Because no progenitor core could be detected within the stream, it was originally named the 'Orphan Stream'. However, in 2018, the southern half of the stream was detected and named Chenab. Thus, the stream was renamed 'Orphan-Chenab'.
MilkyWay@home studies the history of the Milky Way galaxy by analyzing stars in the [[wikipedia:Galactic halo|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.<ref>{{cite journal |last=Weiss |first=Jake |title=A Tangle of Stellar Streams in the North Galactic Cap |journal=The Astrophysical Journal |year=2018}}</ref>
 
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.<ref>{{cite journal |last=Shelton |first=Siddhartha |title=An Algorithm for Reconstructing the Orphan Stream Progenitor with MilkyWay@home Volunteer Computing |journal=The Astrophysical Journal |year=2021}}</ref>
[[File:Our best map of the Milky Way so far (the-milky-way-galaxy).jpg|thumb|Our best map of the Milky Way so far]]


We found the total mass of the OCS's progenitor to be roughly 2 x 107 solar masses, with a mass-to-light ratio of 73.5 (about 98.6% dark matter). This is interesting because other mass estiamtes of the OCS progenitor placed this value somewhere between 108 and 109 solar masses. This is likely because these mass estimates used velocity dispersions for their calculation and assumed the system to be in equilibrium. However, we have also shown that the OCS has an unbound and heavily disrupted progenitor, shattering any assumption of dynamic equilibrium. We also find that the majority of the OCS's mass (especially its dark matter) resides within the tails of the stream, making them ideal candidates for indirect dark matter detection experiments.
== Applications ==


It should still be noted, however, that this measurement is incomplete as our optimizations using MilkyWay@home require an in-depth analysis of the sources of systematic error, such as the accuracy of our Milky Way gravitational potential and the validity of our progenitor models. We also need to include the effects of the Large Magellanic Cloud as it was shown in 2019 to have a measurable effect on the Southern tail of the OCS (Erkal et. al. 2019). In future work, we plan on quantifying this systematic error as the effects of different galactic models on our final fitted mass.
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.<ref>{{cite web |url=https://milkyway.cs.rpi.edu/milkyway/apps.php |title=MilkyWay@home Applications |publisher=RPI |access-date=2026-05-21}}</ref>


== Project team / Sponsors ==
== Project team / Sponsors ==
Kevin Roux. Tom Donlon. Eric Mendelsohn. Matthew. Matt Arsenault. Jake Weiss. Rensselaer Polytechnics Institute.


Supported by the National Science Foundation under Grant Numbers 0612213, 0607618, 0448407, 1009670, 1615688, and 1908653.
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 [[wikipedia:Rensselaer Polytechnic Institute|Rensselaer Polytechnic Institute]].
 
Supported by the [[wikipedia:National Science Foundation|National Science Foundation]] under Grant Numbers 0612213, 0607618, 0448407, 1009670, 1615688, and 1908653.


# [https://www.nsf.gov/awardsearch/showAward?AWD_ID=1615688 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
# [https://www.nsf.gov/awardsearch/showAward?AWD_ID=1615688 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
# [http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1009670 Stars and Dark Matter in the Halo of the Milky Way], NSF AST Grant # 1009670, started 09/15/2010. Principal Investigator: Heidi Newberg.
# [http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1009670 Stars and Dark Matter in the Halo of the Milky Way], NSF AST Grant #1009670, started 09/15/2010
# [http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0612213 Data-Driven Discovery of the Milky Way Origin and Evolution from the Sloan Digital Sky Survey], NSF IIS Grant #0612213, started 07/26/2006. Principal Investigators: Malik Magdon-Ismail, Heidi Newberg, Boleslaw Szymanski and Carlos Varela.
# [http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0612213 Data-Driven Discovery of the Milky Way Origin and Evolution from the Sloan Digital Sky Survey], NSF IIS Grant #0612213
# [https://milkyway.cs.rpi.edu/milkyway/publications/AAS_2014_posters/Weiss_AAS.pdf Revealing the Structure of the Galactic Halo through Statistical Analysis - Middle School Teacher Training], NSF AST Grant #0607618, started 07/15/2006. Principal Investigators: Heidi Newberg, Malik Magdon-Ismail.
# [https://milkyway.cs.rpi.edu/milkyway/publications/AAS_2014_posters/Weiss_AAS.pdf Revealing the Structure of the Galactic Halo through Statistical Analysis - Middle School Teacher Training]
# [http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0448407 Middleware and Programming Technology for Grid Computing], NSF CAREER Grant #0448407, started 01/21/2005. Principal Investigator: Carlos Varela.
# [http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0448407 Middleware and Programming Technology for Grid Computing], NSF CAREER Grant #0448407
 
[[File:Milkyway.gif|alt=Milky Way image|thumb|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.<ref>{{cite web |url=https://milkyway.cs.rpi.edu/milkyway/server_status.php |title=Server Status |publisher=MilkyWay@home |access-date=2026-05-21}}</ref>


== Scientific publications ==
== Scientific publications ==


# Eric J. Mendelsohn. Using MilkyWay@home to Measure the Mass of the Orphan-Chenab Stream Progenitor Dowarf Galaxy. PhD thesis. Rensselaer Polytechnic Institute, 2022.
=== Major journal papers ===
# 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.
 
# Siddhartha Shelton, Heidi Jo Newberg, Jake Weiss, Jacob S. Bauer, Matthew Arsenault, Larry Widrow, Clayton Rayment, Travis Desell, Roland Judd, Malik Magdon-Ismail, Eric Mendelsohn, Matthew Newby, Colin Rice, Boleslaw K. Szymanski, Jeffery M. Thompson, Carlos Varela, Benjamin Willett, Steve Ulin, Lee Newberg. An Algorithm for Reconstructing the Orphan Stream Progenitor with MilkyWay@home Volunteer Computing. The Astrophysical Journal, 2021.
# Eric J. Mendelsohn. [https://milkyway.cs.rpi.edu/milkyway/publications/Eric.M_thesis.pdf Using MilkyWay@home to Measure the Mass of the Orphan-Chenab Stream Progenitor Dwarf Galaxy]. PhD thesis. Rensselaer Polytechnic Institute, 2022.
# Heidi Jo Newberg, Siddhartha Shelton, Eric Mendelsohn, Jake Weiss, Matthew Arsenault, Jacob S. Bauer, Travis Desell, Roland Judd, Malik Magdon-Ismail, Lee A. Newberg, Matthew Newby, Clayton Rayment, Colin Rice, Boleslaw K. Szymanski, Jeffery M. Thompson, Steve Ulin, Carlos Varela, Lawrence M. Widrow, Benjamin A. Willett. Streams and the Milky Way Dark Matter Halo. International Astronomical Union, 2020.
# Eric J. Mendelsohn, Heidi Jo Newberg, Siddhartha Shelton, Lawrence M. Widrow, Jeffery M. Thompson, Carl J. Grillmair. [https://milkyway.cs.rpi.edu/milkyway/publications/Mendelsohn_2022.pdf Estimate of the Mass and Radial Profile of the Orphan-Chenab Stream's Dwarf-galaxy Progenitor Using MilkyWay@home]. ''The Astrophysical Journal'', 2022.
# Siddhartha Shelton. Constraining Dwarf Galaxy Properties Using Tidal Streams. PhD thesis. Rensselaer Polytechnic Institute, 2018.
# Siddhartha Shelton et al. [https://milkyway.cs.rpi.edu/milkyway/publications/Shelton_2021.pdf An Algorithm for Reconstructing the Orphan Stream Progenitor with MilkyWay@home Volunteer Computing]. ''The Astrophysical Journal'', 2021.
# Jake Weiss. The Stellar Density of the Major Substructure in the Milky Way Halo. PhD thesis. Rensselaer Polytechnic Institute, 2018.
# Heidi Jo Newberg et al. [https://milkyway.cs.rpi.edu/milkyway/publications/Newberg_2020.pdf Streams and the Milky Way Dark Matter Halo]. International Astronomical Union, 2020.
# Jake Weiss, Heidi Jo Newberg, Travis Desell. A Tangle of Stellar Streams in the North Galactic Cap. The Astrophysical Journal, 2018.
# Jake Weiss, Heidi Jo Newberg, Travis Desell. [https://milkyway.cs.rpi.edu/milkyway/publications/Weiss_2018.pdf A Tangle of Stellar Streams in the North Galactic Cap]. ''The Astrophysical Journal'', 2018.
# Jake Weiss, Heidi Jo Newberg, Matthew Newby, Travis Desell. Fitting the Density Substructure of the Stellar Halo with MilkyWay@home. The Astrophysical Journal, 2018.
# Jake Weiss, Heidi Jo Newberg, Matthew Newby, Travis Desell. [https://milkyway.cs.rpi.edu/milkyway/publications/Weiss_2018_ApJS_238_17.pdf Fitting the Density Substructure of the Stellar Halo with MilkyWay@home]. ''The Astrophysical Journal'', 2018.
# Julie Dumas, Heidi Jo Newberg, Bethany Niedzielski, Adam Susser, Jeffery M. Thompson, Jake Weiss, Kim M. Lewis. Testing the Dark Matter Caustic Theory Against Observations in the Milky Way. The Astrophysical Journal, 2015.
# Heidi Jo Newberg, Matthew Newby, Travis Desell, Malik Magdon-Ismail, Boleslaw Szymanski, Carlos Varela. [http://arxiv.org/pdf/1411.6003v1.pdf MilkyWay@home: Harnessing Volunteer Computers to Constrain Dark Matter in the Milky Way]. Proceedings of the International Astronomical Union, 2014.
# Samantha Scibelli, Heidi Jo Newberg,  Jeffrey  L. Carlin, Brian Yanny. Census of Blue Stars in SDSS DR8. The Astrophysical Journal Supplement, Volume 215, Issue 2, pages 25 pp, 2014.
# Matthew Newby et al. [http://iopscience.iop.org/1538-3881/145/6/163/pdf/1538-3881_145_6_163.pdf Spatial Characterization of the Sagittarius Dwarf Galaxy Tidal Tails]. ''Astronomical Journal'', 2013.
# Yan Xu, Heidi Jo Newberg. Exploration of Galactic Structures Beyond the Sun Toward the Anti-Center of the Milky Way. Proceedings of the International Astronomical Union, IAU Symposium, Volume 298, pp. 450-450, 2014
# Nathan Cole et al. [http://wcl.cs.rpi.edu/papers/cole-apj-2008.pdf Maximum Likelihood Fitting of Tidal Streams with Application to the Sagittarius Dwarf Tidal Tails]. ''Astrophysical Journal'', 2008.
# 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. In the Proceedings of the International Astronomical Union, pages 98-104, 2014
 
# Matthew T. Newby. The Sagittarius Tidal Stream and the Shape Of The Galactic Stellar Halo. PhD thesis. Rensselaer Polytechnical Institute. 2013
=== Computer science and volunteer computing papers ===
# Matthew Newby, Nathan Cole, Heidi Newberg, Travis Desell, Malik Magdon-Ismail, Boleslaw Szymanski, Carlos Varela, Benjamin Willett, and Brian Yanny. Spatial Characterization of the Sagittarius Dwarf Galaxy Tidal Tails. Astronomical Journal, 145(163), May 2013.
 
# Benjamin Arthur Willett Simultaneous Orbit Fitting of Stellar Streams: Constraining the Galactic Dark Matter Halo. PhD thesis . Rensselaer Polytechnic Institute, 2010
# Travis Desell et al. [http://wcl.cs.rpi.edu/papers/escience2009.pdf Robust Asynchronous Optimization for Volunteer Computing Grids]. IEEE e-Science 2009.
# Travis Desell, David P. Anderson, Malik Magdon-Ismail, Heidi Newberg, Boleslaw Szymanski and Carlos A. Varela. An Analysis of Massively Distributed Evolutionary Algorithms. In the ''Proceedings of the 2010 IEEE Congress on Evolutionary Computation (IEEE CEC 2010)'', Barcelona, Spain, July 2010. To Appear.
# Travis Desell et al. [http://wcl.cs.rpi.edu/papers/ppam2009.pdf Accelerating the MilkyWay@Home volunteer computing project with GPUs]. PPAM 2009.
# Travis Desell, Malik Magdon-Ismail, Boleslaw Szymanski, Carlos A. Varela, Heidi Newberg and David P. Anderson. Validating Evolutionary Algorithms on Volunteer Computing Grids. In the Proceedings of the 10th IFIP International Conference on Distributed Applications and Interoperable Systems (DAIS 2010), Amsterdam, Netherlands, June 2010. To Appear.
# Travis Desell et al. [https://milkyway.cs.rpi.edu/milkyway/publications/desell_dais_2010.pdf Validating Evolutionary Algorithms on Volunteer Computing Grids]. DAIS 2010.
# Nathan Cole, Travis Desell, Daniel Lombranaa Gonzalez, Francisco Fernandez de Vega, Malik Magdon-Ismail, Heidi Newberg, Boleslaw Szymanski and Carlos Varela. ''Evolutionary Algorithms on Volunteer Computing Platforms: The MilkyWay@Home Project''. In F. Fernandez de Vega, E. Cantu-Paz (Eds.): Parallel and Distributed Computational Intelligence, SCI 269, pp 63-90. Springer-Verlag Berlin Heidelberg. 2010
# Travis Desell et al. [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5586073&tag=1 An Analysis of Massively Distributed Evolutionary Algorithms]. IEEE CEC 2010.
# Travis Desell. Asynchronous Global Optimization for Massive-Scale Computing. PhD thesis. Rensselaer Polytechnic Institute. 2009
# Nathan Cole et al. [https://milkyway.cs.rpi.edu/milkyway/publications/cole2009.pdf A Study of the Sagittarius Tidal Stream Using Maximum Likelihood]. ADASS XVIII, 2009.
# Travis Desell, Malik Magdon-Ismail, Boleslaw Szymanski, Carlos Varela, Heidi Newberg and Nathan Cole. Robust Asynchronous Optimization for Volunteer Computing Grids. In the 5th IEEE International Conference on e-Science (eScience2009), Oxford, UK, pages 263-270, December 2009.
 
# Travis Desell, Anthony Waters, Malik Magdon-Ismail, Boleslaw Szymanski, Carlos Varela, Matthew Newby, Heidi Newberg, Andreas Przystawik and Dave Anderson. Accelerating the MilkyWay@Home volunteer computing project with GPUs. In 8th International Conference on Parallel Processing and Applied Mathematics (PPAM 2009), Wroclaw, Poland, September 2009. To appear.
== See also ==
# Nathan Cole. Maximum Likelihood Fitting of Tidal Streams with Application to the Sagittarius Dwarf Tidal Tails. PhD thesis. Rensselaer Polytechnic Institute. 2009.
 
# Nathan Cole, Heidi Newberg, Malik Magdon-Ismail, Travis Desell, Carlos Varela, and Boleslaw Szymanski. A Study of the Sagittarius Tidal Stream Using Maximum Likelihood. Astronomical Data Analysis Software and Systems XVIII, 411: 221, 2009.
* [[wikipedia:BOINC|BOINC]]
# Nathan Cole, Heidi Newberg, Malik Magdon-Ismail, Travis Desell, Kristopher Dawsey, Warren Hayashi, Jonathan Purnell, Boleslaw Szymanski, Carlos A. Varela, Benjamin Willett, and James Wisniewski. Maximum Likelihood Fitting of Tidal Streams with Application to the Sagittarius Dwarf Tidal Tails. Astrophysical Journal, 683:750-766, 2008.
* [[wikipedia:Volunteer computing|Volunteer computing]]
# Travis Desell, Boleslaw Szymanski, and Carlos A. Varela. An Asynchronous Hybrid Genetic-Simplex Search for Modeling the Milky Way Galaxy using Volunteer Computing. In Genetic and Evolutionary Computation Conference (GECCO 2008), Atlanta, Georgia, pages 921-928, July 2008.
* [[wikipedia:SETI@home|SETI@home]]
# Travis Desell, Boleslaw Szymanski, and Carlos A. Varela. Asynchronous Genetic Search for Scientific Modeling on Large-Scale Heterogeneous Environments. In Proceedings of the 17th International Heterogeneity in Computing Workshop (HCW/IPDPS'08), Miami, FL, pages 12pp, April 2008. IEEE.
* [[wikipedia:Einstein@Home|Einstein@Home]]
# Boleslaw Szymanski, Travis Desell, and Carlos A. Varela. The Effect of Heterogeneity on Asynchronous Panmictic Genetic Search. In Proc. of the Seventh International Conference on Parallel Processing and Applied Mathematics (PPAM'2007), LNCS, Gdansk, Poland, September 2007.
* [[wikipedia:GPU computing|GPU computing]]
# Travis Desell, Nathan Cole, Malik Magdon-Ismail, Heidi Newberg, Boleslaw Szymanski, and Carlos A. Varela. Distributed and Generic Maximum Likelihood Evaluation. In 3rd IEEE International Conference on e-Science and Grid Computing (eScience2007), Bangalore, India, pages 337-344, December 2007. Best paper finalist.
* [[wikipedia:Dark matter|Dark matter]]
 
== External links ==
 
* [https://milkyway.cs.rpi.edu/milkyway/ Official website]
* [https://milkyway.cs.rpi.edu/milkyway/forum_index.php Project forums]
* [https://milkyway.cs.rpi.edu/milkyway/server_status.php Server status]
* [https://github.com/Milkyway-at-home GitHub repository]
* [https://boinc.berkeley.edu/ BOINC]
 
== References ==
 
{{Reflist}}
 
[[Category:BOINC projects]]
[[Category:Volunteer computing]]
[[Category:Distributed computing projects]]
[[Category:Astronomy projects]]
[[Category:Astrophysics]]
[[Category:Dark matter]]
[[Category:Rensselaer Polytechnic Institute]]
[[Category:2007 software]]

Latest revision as of 22:36, 28 May 2026


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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