Monday, May 30th 2022
ORNL Frontier Supercomputer Officially Becomes the First Exascale Machine
Supercomputing game has been chasing various barriers over the years. This has included MegaFLOP, GigaFLOP, TeraFLOP, PetaFLOP, and now ExaFLOP computing. Today, we are witnessing for the first time an introduction of an Exascale-level machine contained at Oak Ridge National Laboratory. Called the Frontier, this system is not really new. We have known about its upcoming features for months now. What is new is the fact that it was completed and is successfully running at ORNL's facilities. Based on the HPE Cray EX235a architecture, the system uses 3rd Gen AMD EPYC 64-core processors with a 2 GHz frequency. In total, the system has 8,730,112 cores that work in conjunction with AMD Instinct MI250X GPUs.
As of today's TOP500 supercomputers list, the system is overtaking Fugaku's spot to become the fastest supercomputer on the planet. Delivering a sustained HPL (High-Performance Linpack) score of 1.102 Exaflop/s, it features a 52.23 GigaFLOPs/watt power efficiency rating. In the HPL-AI metric, dedicated to measuring the system's AI capabilities, the Frontier machine can output 6.86 exaFLOPs at reduced precisions. This alone is, of course, not a capable metric for Exascale machines as AI works with INT8/FP16/FP32 formats, while the official results are measured in FP64 double-precision form. Fugaku, the previous number one, scores about 2 ExaFLOPs in HPL-AI while delivering "only" 442 PetaFlop/s in HPL FP64 benchmarks.
As of today's TOP500 supercomputers list, the system is overtaking Fugaku's spot to become the fastest supercomputer on the planet. Delivering a sustained HPL (High-Performance Linpack) score of 1.102 Exaflop/s, it features a 52.23 GigaFLOPs/watt power efficiency rating. In the HPL-AI metric, dedicated to measuring the system's AI capabilities, the Frontier machine can output 6.86 exaFLOPs at reduced precisions. This alone is, of course, not a capable metric for Exascale machines as AI works with INT8/FP16/FP32 formats, while the official results are measured in FP64 double-precision form. Fugaku, the previous number one, scores about 2 ExaFLOPs in HPL-AI while delivering "only" 442 PetaFlop/s in HPL FP64 benchmarks.