Nvidia and Cineca, an Italian inter-university consortium and main supercomputing heart, have introduced plans to construct ‘the world’s quickest AI supercomputer.’
The upcoming Leonardo system will use almost 14,000 Nvidia A100 GPUs for a wide range of high-performance computing duties. The peak efficiency of the system is anticipated to hit 10 FP16 ExaFLOPS.
The supercomputer will likely be based mostly on Atos’ BullSequana XH2000 supercomputer nodes, every carrying one unknown Intel Xeon processor, 4 Nvidia A100 GPUs and a Mellanox HDR 200Gb/s InfiniBand card for connectivity. The blades are water cooled and there are 32 of them in every HPC cupboard.
The BullSequana XH2000 structure could be very versatile, so it might home any CPU and GPU and, to that finish, we will solely guess which Intel Xeon processor will likely be used for Leonardo.
Scientists from Italian universities plan to make use of Leonardo for drug discovery, house exploration and analysis, and climate modelling.
Traditionally, such functions depend on high-performance simulation and information analytics workloads that require FP64 precision. But Nvidia says that immediately many HPC duties depend on highly effective synthetic intelligence and machine studying – and for such workloads FP16 precision is sufficient.
Quite naturally, an enormous variety of GPUs may also carry out high-resolution visualizations. Nvidia’s A100 GPU was designed primarily for computing, so it helps all types of precision, together with ‘supercomputing’ FP64 and ‘AI’ FP16.
14,000 Nvidia A100 GPUs can obtain as much as 8.736 FP16 ExaFLOPS (624 TFLOPS per GPU with structural sparsity enabled × 14,000) efficiency. Meanwhile, the identical variety of GPUs can present 135,800 FP64 TFLOPS, which is barely under Summit’s 148,600 FP64 TFLOPS.
Nvidia believes AI and ML are essential for immediately’s supercomputer, so the corporate prefers to cite peak FP16 efficiency with structural sparsity enabled, within the case of the Leonardo supercomputer powered by its A100 GPUs.
“With the advent of AI, we now have a new metric for measuring supercomputers. As a result, the performance of our supercomputers has exploded as the computational power of them has increased exponentially with the introduction of AI,” Ian Buck, VP and GM of Accelerated Computing at Nvidia, told TechRadar Pro.
“Today’s modern supercomputers must be AI supercomputers in order to be an essential tool for science. Nvidia is setting a new trend by combining HPC and AI. Only AI supercomputers can deliver 10 ExaFLOPS of AI performance featuring nearly 14,000 NVIDIA Ampere architecture-based GPUs.”