About
Benefits of Intel Xeon Phi Knights Landing (KNL) systems in computing cost are examined with tight-binding simulations of large-scale electronic structures that involve sparse system matrices whose dimensions normally reach several tens of millions. Speed and energy usage of our in-house Schroedinger equation solver are benchmarked in KNL systems for realistic modelling tasks, and are discussed against the cost required by offload computing with P100 GPU devices. Superiority in speed and energy-efficiency observed in KNL systems justify the practicality of bootable manycore processors that are adopted by nearly 30% of largest supercomputers in the world. With a demonstration of the strong scalability up to 2,500 nodes, this work serves as an useful case study that supports the utility of KNL systems for handling memory-bound applications including ours and other numerical problems that involve large-scale sparse matrix-vector multiplications, particularly compared to GPU-based systems..
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The talk
Credits and licence
- Institution
- CERN
- Department
- other events or meetings
- Speaker
- Hoon Ryu
- Year
- 2019
- Language
- English
- Source
- CERN
- Licence
- CERN, as CERN states it. CERN's terms
- Attribution
- © CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
- Who can watch
- CERN gives no rating. Invisible Network shows it to Teens profiles and older, judged from what the source says about it. How courses are placed