green graph500.org

Green Graph 500

Energy-Aware Big Data Computing. Date intensive or Big Data computing plays an increasingly important role in HPC workloads and datacenters. The energy consumption will become a limiting factor in the future making energy-aware big data computing a necessity. The Green Graph 500 list collects performance-per-watt metrics and acts as a forum for vendors and data center operators to compare the energy consumption of data intensive computing workloads on their architectures. Third official Green Graph500.
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Graph 500 large-scale benchmarks

To complement the Top 500 with data intensive applications. Top Ten from November 2017 BFS. Various scientific and instudrial fields. National Supercomputing Center in Wuxi. ALCF Mira - 8192 partition.

PICYCLE Recycle Your Raspberry Pi

Do you have an unused Raspberry Pi board locked away in a cupboard? Please donate it for scientific research. We want to scale up this number, to build a distributed UK-wide testbed for system experiments. Put your packaged pi into a Jiffy bag.

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SITE TITLE

Green Graph 500

DESCRIPTION

Energy-Aware Big Data Computing. Date intensive or Big Data computing plays an increasingly important role in HPC workloads and datacenters. The energy consumption will become a limiting factor in the future making energy-aware big data computing a necessity. The Green Graph 500 list collects performance-per-watt metrics and acts as a forum for vendors and data center operators to compare the energy consumption of data intensive computing workloads on their architectures. Third official Green Graph500.

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The web site has the following in the web site, "Date intensive or Big Data computing plays an increasingly important role in HPC workloads and datacenters." I noticed that the web page also stated " The energy consumption will become a limiting factor in the future making energy-aware big data computing a necessity." They also said " The Green Graph 500 list collects performance-per-watt metrics and acts as a forum for vendors and data center operators to compare the energy consumption of data intensive computing workloads on their architectures."

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