NVIDIA just outperformed by nearly 20x the record for running the standard big data analytics benchmark, known as TPCx-BB.
Using the
RAPIDS suite of open-source data science software libraries powered by 16
NVIDIA DGX A100 systems, NVIDIA ran the benchmark in just 14.5 minutes, versus the current leading result of 4.7 hours on a CPU system. The DGX A100 systems had a total of 128 NVIDIA A100 GPUs and used NVIDIA Mellanox networking.
Today, leading organizations use AI to gain insights. The TPCx-BB benchmark features queries that combine SQL with machine learning on structured data, with natural language processing and unstructured data, reflecting the diversity found in modern data analytics workflows.
These unofficial results point to a new standard, and the breakthroughs behind it are available through the NVIDIA software and hardware ecosystem.
TPCx-BB is a big data benchmark for enterprises representing real-world ETL (extract, transform, load) and machine learning workflows. The benchmark’s 30 queries include big data analytics use cases like inventory management, price analysis, sales analysis, recommendation systems, customer segmentation and sentiment analysis.
Despite steady improvements in distributed computing systems, such big data workloads are bottlenecked when running on CPUs.
The RAPIDS results on DGX A100 showcase the breakthrough potential for TPCx-BB benchmarks powered by GPUs, a measurement historically run on CPU-only systems.
The TPCx-BB queries were implemented as a series of Python scripts utilizing the RAPIDS dataframe library,
cuDF; the RAPIDS machine learning library,
cuML; and
CuPy,
BlazingSQL and
Dask as the primary libraries.
Numba was used to implement custom logic in user-defined functions, with
spaCy for Named Entity Recognition.
These results would not be possible without the RAPIDS and broader PyData ecosystem.
