It is a truly remarkable machine but the prime contractor, Intel, wasn't up to the job, delivered it very late and the machine is still being put together.
High performance computing from HPCWire, May 15:
Some Reasons Why Aurora Didn’t Take First Place in the Top500 List
The makers of the Aurora supercomputer, which is housed at the Argonne National Laboratory, gave some reasons why the system didn’t make the top spot on the Top500 list of the fastest supercomputers in the world.
At slightly over 1-exaflops of performance, Aurora remained in the second spot behind Frontier, which took the top spot at around 1.2 exaflops. Aurora passed the exaflop barrier, making it only the second system to pass the threshold.
The organizers seemed to prioritize the utility of the system over performance. Only time will tell whether the hundreds of millions of tax-payer money spent on the system was worth it.
ANL, HPE, and Intel, the main organizations behind the system, explained why Aurora isn’t complete and answered benchmarking questions.
Argonna runs on a chip Intel calls the “Exascale Compute Blade,” which has the 52-core Xeon CPU Max and Intel Data Center GPU Max.
The Benchmark Isn’t Complete
The Aurora system is still being installed, and more performance can be squeezed out of the system.The high-performance LINPACK run represents about 80% to 90% of the overall system performance benchmarked and may still be able to topple Frontier to the top spot.
Aurora’s theoretical performance was estimated to be 2 exaflops. The real-world performance measure is about 55% of this, which is in line with the numbers achieved on other supercomputers, which are about 50% to 70%.
The System Wasn’t Built for High-performance LINPACK Runs
The hardware choices for Aurora indicate the system wasn’t built expressly to achieve top high-performance LINPACK benchmarks.Instead, the system was built to balance scientific and AI computing. The system achieved 10.6 exaflops on mixed-precision computing on limited system benchmarking.
Scientific computing is shifting toward mixed-precision computing, and ANL, HPE, and Intel are looking ahead with Aurora. At the same time, Aurora meets the needs of conventional scientific applications that require double-precision computing.
The Aurora builders deliberately decided not to include processing units that drive up the main Top500 benchmark.
The System Has More AI Hardware Parts
Aurora’s design decision was to dedicate more silicon space and power budget to AI and mixed-precision parts than Frontier.For example, Intel’s GPU, called Ponte Vecchio, in Aurora does not have dedicated matrix engines for FP64 (double-precision computing). By comparison, AMD’s MI250X in Frontier has dedicated parts for faster and more power-efficient FP64 matrix math calculations....
....MUCH MORE
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