GridCoin (GRC)

See Realtime prive of GridCoin (GRC) Here 

What is Gridcoin?

  • Gridcoin (Ticker: GRC) is a decentralized, open source math-based digital asset (cryptocurrency). It performs transactions peer-to-peer cryptographically without the need for a central issuing authority.
  • Gridcoin was the first block chain protocol that delivered a working algorithm that equally rewards and cryptographically proves solving BOINC (Berkeley Open Infrastructure for Network Computing) hosted work, which can be virtually any kind of distributed computing process (GPU/CPU/Sensor/Etc).
  • BOINC is an open-source volunteer oriented computing grid that combines the processing power of all individual users for the purposes of scientific research. It’s free, and harnesses the unused clock cycles from processors and graphics cards to attempt to cure cancer/aids/ebola/malaria, map the milkyway, crack enigma codes, etc..
  • Gridcoin rewards BOINC computation using the Distributed Proof of Research (DPOR) reward mechanism, which is a combination of Proof of BOINC (POB) and Proof of Stake (POSv2).
  • Proof of Work (POW) consensus mechanisms are not utilised by the Gridcoin network, making the Gridcoin cryptocurrency network far more energy efficient than any existing POW cryptocurrencies.

BOINC Introduction

  • First of all, if you just want to be an investor in Gridcoin, you can skip the following steps which explain how to set up a mining environment for Gridcoin. In that case, you will just need to install the Gridcoin Wallet.
  • To get Distributed-Proof-of-Research (DPOR) payments, you will have to participate in one or more projects of the Berkeley Open Infrastructure for Network Computing (BOINC). BOINC is a platform for universities and research groups to upload small packages of their research. These small packages, known as workunits, contain parts of calculations and numerical problems which your home computer can solve. You can find more information here and on Wikipedia.


Science research projects typically publish their results in scientific journals.
Preliminary results are often published in conferences.
In the academic world, the scientific contribution of a project is measured largely by the number of its publications, and the prestige of the journals and conferences in which they appear (high-prestige journals include Nature, Science, and PNAS).

We encourage BOINC volunteers to support projects that make a major scientific contribution.
Keep in mind that doing research and publishing papers may take several years, so newer projects will naturally have few or no publications.

The following scientific papers have resulted from BOINC-based projects (this list does not include papers about volunteer computing or about BOINC itself; those are here).
  • T. Estrada, R. Armen, and M. Taufer: Automatic Selection of Near-Native Protein-Ligand Conformations using a Hierarchical Clustering and Volunteer Computing. In Proceedings of the International Conference On Bioinformatics and Computational Biology (ACM-BCB), August 2010, Niagara Falls, NY, USA.

All publications with pdfs are available at

  • T. Giorgino and G. De Fabritiis, A high-throughput steered molecular dynamics study on the free energy profile of ion permeation through gramicidin A, in press J. Chem. Theory Comput. (2011).
  • I. Buch, S. K. Sadiq and G. De Fabritiis, Optimized potential of mean force calculations of standard binding free energy, in press J. Chem. Theory Comput. (2011)
  • Ignasi Buch, Toni Giorgino, and G. De Fabritiis, Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations, PNAS 2011 ; published ahead of print June 6, 2011, doi:10.1073/pnas.1103547108
  • I. Buch, M. J. Harvey, T. Giorgino, D. P. Anderson, and G. De Fabritiis. High-Throughput All-Atom Molecular Dynamics Simulations Using Distributed Computing. J. Chem. Inf. Model, March 2010.
  • J. Selent, F. Sanz, M. Pastor and G. De Fabritiis, Induced Effects of Sodium Ions on Dopaminergic G-Protein Coupled Receptors, PLOS Computational Biology, 6, e1000884 (2010) pdf.
  • K. Sadiq and G. De Fabritiis,Explicit solvent dynamics and energetics of HIV-1 protease flap-opening and closing, Proteins 78, 2873 (2010) pdf.
  • G. Giupponi, M. Harvey and G. De Fabritiis, The impact of accelerator processors for high-throughput molecular modeling and simulation. Drug Discovery Today 13, 1052-8 (2008).
  • G. De Fabritiis, P. Coveney and J. Villa-Freixa, Energetics of K+ permeability through Gramicidin A by forward-reverse steered molecular dynamics. Proteins 73, 185-94 (2008).
  • M. Harvey, G. Giupponi, J. Villa-Freixa and G. De Fabritiis, PS3GRID.NET: Building a distributed supercomputer using the PlayStation 3. Distributed & Grid Computing – Science Made Transparent for Everyone. Principles, Applications and Supporting Communities (2007).
  • PS3GRID.NET: A distributed computing environment for molecular simulations on the PlayStation 3. Presentation at the international symposium of biomedical informatics, 26-27 June Barcelona (2007).
  • G. De Fabritiis, Performance of the Cell processor for biomolecular simulations. Comp. Phys. Commun. 176, 660 (2007).
  • 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, 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.
  • 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. Asynchronous Global Optimization for Massive-Scale Computing. PhD thesis. Rensselaer Polytechnic Institute. 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.
  • 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, 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Meliciani I, Klenin K, Strunk T, Schmitz K, Wenzel W. (2009) Probing hot spots on protein-protein interfaces with all-atom free-energy simulation. J Chem Phys. 131, 034114.
Quake Catcher Network
  • Elizabeth S. Cochran, Jesse F. Lawrence, Carl Christensen, and Ravi S. Jakka. The Quake-Catcher Network: Citizen Science Expanding Seismic Horizons. Seismological Research Letters, Jan/Feb 2009.
  • Posypkin, Mikhail and Semenov, Alexander and Zaikin, Oleg (2012) Using BOINC desktop grid to solve large scale SAT problems. Computer Science, 13 (1). pp. 25-34. ISSN 1508-2806
  • David P. Anderson, Jeff Cobb, Eric Korpela, Matt Lebofsky, Dan Werthimer. SETI@home: An Experiment in Public-Resource Computing. Communications of the ACM, Vol. 45 No. 11, November 2002, pp. 56-61.
Virtual Prairie
  • C. Monya, M. Garbeyb, M. Smaouib, M.-L. Benota. Large scale parameter study of an individual-based model of clonal plant with volunteer computing. Ecological Modelling 222 (2011) 935–946.
World Community Grid

The Clean Energy Project

Computing for Clean Water

Discovering Dengue Drugs – Together


Help Conquer Cancer

Help Cure Muscular Dystrophy

Help Defeat Cancer

Human Proteome Folding

  • Drew K, Winters P, Butterfoss GL, Berstis V, Uplinger K, Armb J, Riffle M, Schweighofer E, Bovermann B, Goodlett DR, Davis TN, Shasha D, Malmström L, Bonneau R. The Proteome Folding Project: Proteome-scale prediction of structure and function Genome Res. 2011 Nov;21(11):1981-94. Epub 2011 Aug 8.
  • Boxem, Mike; Maliga, Zoltan; Klitgord, Niels; Li, Na; Lemmens, Irma; Mana, Miyeko; de Lichtervelde, Lorenzo; Mul, Joram D.; van de Peut, Diederik; Devos, Maxime; Simonis, Nicolas; Yildirim, Muhammed A.; Cokol, Murat; Kao, Huey-Ling; de Smet, Anne-Sophie; Wang, Haidong; Schlaitz, Anne-Lore; Hao, Tong; Milstein, Stuart; Fan, Changyu; Tipsword, Mike; Drew, Kevin; Galli, Matilde; Rhrissorrakrai, Kahn; Drechsel, David; Koller, Daphne; Roth, Frederick P.; Iakoucheva, Lilia M.; Dunker, A. Keith; Bonneau, Richard; Gunsalus, Kristin C.; Hill, David E.; Piano, Fabio; Tavernier, Jan; van den Heuvel, Sander; Hyman, Anthony A.; Vidal, Marc A Protein Domain-Based Interactome Network for C. elegans Early Embryogenesis Cell doi:10.1016/j.cell.2008.07.009 (volume 134 issue 3 pp.534 – 545)
  • Bonneau, Richard; Facciotti, Marc T.; Reiss, David J.; Schmid, Amy K.; Pan, Min; Kaur, Amardeep; Thorsson, Vesteinn; Shannon, Paul; Johnson, Michael H.; Bare, J. Christopher; Longabaugh, William; Vuthoori, Madhavi; Whitehead, Kenia; Madar, Aviv; Suzuki, Lena; Mori, Tetsuya; Chang, Dong-Eun; DiRuggiero, Jocelyne; Johnson, Carl H.; Hood, Leroy; Baliga, Nitin S. A Predictive Model for Transcriptional Control of Physiology in a Free Living Cell Cell doi:10.1016/j.cell.2007.10.053 (volume 131 issue 7 pp.1354 – 1365)
  • Lars Malmström, Michael Riffle, Charlie E. M. Strauss, Dylan Chivian, Trisha N. Davis, Richard Bonneau, David Baker. Superfamily Assignments for the Yeast Proteome through Integration of Structure Prediction with the Gene Ontology. PLoS Biol 5(4).

Genome Comparison

Nutritious Rice for the World

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