Riot Platforms estimates that its forthcoming top-of-the-line bitcoin mining facility near Corsicana, Texas, will cost $833,000 per megawatt of power capacity to construct the first phase of 400 megawatts. Additionally, the power cost for such a facility would be double what miners pay now, perhaps as high as 15 cents per kilowatt/hour. The cost of building a true AI data center can be anywhere from 10 to 20 times the price per megawatt of a new bitcoin mining site (a megawatt can power about 200 Texas homes, according to electricity authority ERCOT). Miners don’t have the proper computing equipment to support this kind of work, nor do they have the right electrical and network infrastructure (AI computing needs a network bandwidth of at least 1 terabyte per second, a tremendous step up from industrial-scale mining farms, which usually have speeds of 1 gigabyte per second). These are not the same types of computations that train AI models or power Chat GPT. HIVE did not respond to a request to specify exactly what type of high-performance computing its GPUs conducted during the Q1 pilot program. The company purchased the 6 megawatt facility that provides these services when it acquired the data-center operations of Toronto-based TeraGo in 2022. Hut 8 has a contract with British Columbia’s Interior Health agency to provide high-performance computing services until 2028. Hut 8 earned $4.5 million from such services in Q1 and $4.3 million in Q2, while a pilot program for HIVE generated $200,000 during Q1, but the miner did not mention any HPC revenue for Q2 in its SEC filings. When miners talk about HPC, however, they are specifically referring to things like cloud computing or graphics rendering. The readers improve their knowledge about the essential data science techniques in order to turn mere transactional information into social, economic, and business insights.High-performance computing is a catchall for any number of data-center tasks. It is also aimed at researchers and analysts who already possess good analytical and data skills, but who do not yet have the specific knowledge to tackle analytic questions about blockchain transactions. Such an approach intends to serve as a good starting point for undergraduate and graduate students to learn data science topics using cryptocurrency blockchain examples. Each chapter introduces basic concepts, presents examples with real cryptocurrency blockchain data and offers exercises and questions for further discussion. The book takes the reader through basic data exploration topics, proceeding systematically, method by method, through supervised and unsupervised learning approaches and information visualization techniques, all the way to understanding the blockchain data from the network science perspective.Ĭhapters introduce the cryptocurrency blockchain data model and methods to explore it using structured query language, association rules, clustering, classification, visualization, and network science. Cryptocurrencies are the first successful use case of highly distributed blockchains, like the world wide web was to the Internet. The concept of blockchain is predicted to enable and spark transformation on par with that associated with the invention of the Internet. Data science and big data technologies can be considered cornerstones of the data-driven digital transformation of organizations and society. It is one of the first books to systematically cover the analytics aspects of blockchains, with the goal of linking traditional data mining research communities with novel data sources. This book brings together two major trends: data science and blockchains.
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