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dc.creatorTuzi, Daren
dc.date.accessioned2018-08-22T08:53:52Z
dc.date.available2018-08-22T08:53:52Z
dc.identifier.urihttp://dspace.cc.tut.fi/dpub/handle/123456789/26464
dc.description.abstractThe purpose of this thesis is to study the efficiency of using graphical processing units in Cryptonight, the proof-of-work system used to mine Monero. By understanding the dependence of Cryptonight in memory, we theorize that by improving read and write delays we can improve mining results. In this thesis, there is a major focus on the technology behind Bitcoin and Monero since at the time of writing stand to be the most respectable ecosystems. The paper starts by analyzing the history of proof of work and how it has evolved during the past few years. We study the use of CPUs and GPUs to mine during the lifetime of Bitcoin and the eventual development of specialized ASICs. How GPU mining is the current best solution for mining Monero because of its commitment to stay ASIC resistant and why GPU mining is the best way to build a general-purpose miner that has the flexibility to mine different coins and different algorithms. We look at all the hardware components required to build a GPU miner, how to choose between alternatives and how this affects efficiency. During this writing and testing period many components were burned or damaged so some of the common mistakes in handling hardware will be mentioned. We will take a look at all the hardware modifications that can be made like overclocking, undervolting and modifying bios memory timings to increase mining efficiency measured in hash/watt units. Major focus is put in understanding memory timings, how changing specific values impacts hashrate, measuring this data to quantify the efficiency benefits that can be used in profitable mining. This thesis is an attempt to document as much as possible of the knowledge that has been flowing around lately as interest on crypto currencies has increased in the past few years.en
dc.format.extent53en
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.rightsThis publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
dc.titleCryptonight Gpu Mining Efficiencyen
dc.identifier.urnURN:NBN:fi:tty-201808292216
dc.contributor.laitosTietotekniikka – Pervasive Computingen
dc.contributor.tiedekuntaTieto- ja sähkötekniikan tiedekunta – Faculty of Computing and Electrical Engineeringen
dc.contributor.yliopistoTampereen teknillinen yliopisto - Tampere University of Technology
dc.programmeInformation Technologyen
dc.date.published2018-09-05
dc.permissionPermission granteden
dc.contributor.degreesupervisorBrumley, Billy
dc.type.ontasotDiplomityö - Master's thesis


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