Dell EMC supplies Artemis 3 supercomputer to University of Sydney

Dell EMC said its Artemis 3 supercomputer will power University of Sydney’s research and academic programs.
Dell EMC at VMworld 2017
This high-performance computing (HPC) system uses Dell EMC PowerEdge C4140 server technology.

The University of Sydney’s $2.3 million system has an rPeak performance of 1 petaflops and an rMax of 700 teraflops, allowing faster processing of data to provide answers to scientific questions.

“To stay ahead of the volume and velocity of data being generated by scientific instruments and sensors, researchers need high performance computing (HPC) technology to collect and process data faster, in real-time,” Jeremy Hammond, director, Strategic Ventures, The University of Sydney, Australia, said.

Artemis 3 will support a range of projects at The University of Sydney, including those in established fields such as geophysics and cosmology, as well as rapidly growing areas of genomics and proteomics.

It will also be used in emerging areas that tackle questions answerable by Big Data, such as economics, transport logistics and medical imaging.

It supports accelerated computing on NVIDIA Tesla graphics processing units (GPUs) for high-performance needs, which is vital for the growing areas of AI and machine learning. The University of Sydney  hosts cutting-edge research in this field and can now develop tools and techniques with broader applications in a range of research areas.

Artemis 3 also makes it easier for the university to build a system that is suitable for all levels of academia, from undergraduate students to professors. This is key to ensuring the university delivers not just world-class research, but world-class graduates.

The new PowerEdge C4140 compute platform from Dell EMC contains the latest-generation Intel Xeon Scalable Processors and four NVIDIA NVLink connected Tesla V100 GPUs. The computing design offers 44x more throughput for over 500 key scientific and engineering applications. NVIDIA Tesla V100 is equipped with 640 Tensor Cores, delivering 125 Teraflops of deep learning performance.

Related News

Latest News

Latest News