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dc.contributor.advisorMorrison, Donn
dc.contributor.advisorUmuroglu, Yaman
dc.contributor.authorLangland, Torbjørn
dc.contributor.authorSkordal, Kristian Klomsten
dc.date.accessioned2015-10-09T14:01:16Z
dc.date.available2015-10-09T14:01:16Z
dc.date.created2015-06-10
dc.date.issued2015
dc.identifierntnudaim:12754
dc.identifier.urihttp://hdl.handle.net/11250/2353553
dc.description.abstractRecent years have seen the emergence of a new class of currencies, called cryptocurrencies. These currencies use cryptography to provide security and peer-to-peer networking to provide a decentralized system. Bitcoin is the most popular of these currencies. It uses a two-pass SHA-256 hash at its core. Producing new bitcoins is done through a process referred to as "mining", which involves a brute-force search for a hash with a specific value. This process requires large amounts of computing power. Current-generation hardware for bitcoin mining includes highly-optimized ASIC chips which provide huge amounts of performance. However, designers of such chips are having problems with delivering enough power and cooling to the chips. To alleviate this problem, this thesis looks at the possibilities of using heterogeneous computing to reduce power consumption and produce a more energy-efficient mining solution. A SHA-256 accelerator and a DMA module is developed and integrated into a tile for the Single-ISA Heterogeneous MAny-core Computer, SHMAC, and a system with multiple cores is used to exploit the thread-level parallelism provided by the platform. The system is tested using a benchmark to find out what performance and energy efficiency can be expected when using the system for bitcoin mining. The results show a maximum performance of 175,7 kH/s when running the benchmark application on 14 cores using the SHA-256 accelerator and the DMA module. The best energy efficiency was obtained when running on 14 cores without the DMA enabled, at 163,2 kH/J. The results does not compare well to specialized FPGA-based bitcoin miners, but demonstrates the SHMAC platform's large degree of thread-level parallelism which can be better exploited in other applications.
dc.languageeng
dc.publisherNTNU
dc.subjectDatateknologi (2 årig), Komplekse datasystemer
dc.subjectDatateknologi, Komplekse datasystemer
dc.titleMining Bitcoins using a Heterogeneous Computer Architecture
dc.typeMaster thesis
dc.source.pagenumber54


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