If this was a real-world scenario – and the TPCx-BB is intended to simulate a mixed, real-world workload – then the Nvidia system would have reduced the ML and SQL runtime from 4.7 hours to just under 15 minutes. You can understand why Nvidia would crow loudly about these results. On the larger SF10K test, the Nvidia system ran “only” 19.5 times faster. On the SF1K test, the Nvidia setup bested the previous record time by 37.1X. For the SF10K test, which used a 10TB data set, it used a Mellanox interconnect to hook together 16 DGX A100 systems running a total of 128 A100 GPUs.
Benchmark test gpu nvidia at store series#
For the SF1K test, which simulated a series of queries against a 1TB dataset, the company rolled out a dual DGX A100 systems, comprising a total of 16 A100 GPUs and a Mellanox interconnect. Nvidia today reported unofficial results for two TPCx-BB tests, including the SF1K and the SF10K. So when Nvidia ran the benchmark on its new Ampere class of GPUs system, the results were predictably grim – for CPU systems, that is. Traditionally, vendors have used CPU-based systems for the TPCx-BB benchmark, which simulates a Hadoop-style workload that mixes SQL and machine learning jobs on structured and unstructured data. Editor’s note: TPC announced on January 27, 2021, that the benchmark tests claimed by Nvidia, as described in this story, are a violation of its fair use policy.
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