Bitfury, a blockchain development startup has announced that it will now use the computing power it normally utilizes in processing its virtual currency processing to conduct research on Covid-19.
The firm stated that it started allocating some of its powerful GPU-enabled computing nodes to help in running Covid-19 computation since March 20.
— The Bitfury Group (@BitfuryGroup) March 31, 2020
The latest move by Bitfury comes in efforts to contribute to an organized distributed computing endeavor dubbed [email protected] ([email protected]) which was started by a group of scientific labs from North America, Asia as well as Europe.
Following the ravanging Covid-19 pandemic that has led to damaging effects all over the world, [email protected] came up with an initiative to run simulations to understand the molecular structure of the virus as an attempt to assist positively to the progress of working therapeutics to fight the virus.
The [email protected] project requires massive computational power and has been requesting for computer resource donations from individuals, organizations as well as enterprises.
According to Valery Vavilov, Bitfury CEO, the project initiated by [email protected] is important as it brings together doctors and researchers in efforts to understand the virus and how it can be treated.
Bitfury becomes the latest entrant to the [email protected] project from the crypto sector. The initiative has earned support from various crucial players from the crypto industry. Golem, a computing network, as well as blockchain platform Tezos have set aside hundreds of XTZ that will be given to the biggest [email protected] donor by the end of the current month.
According to Cointelegraph, by March 12, there were around 20 teams from Tezos donating different resources to the [email protected] initiative. Another major contributor to the initiative is Ether miner Corewave which has donated computational power of about 6,000 GPUs. Nvidia has also contributed towards the project by urging gamers to donate unutilized GPU computing resources.