While at the NVIDIA GPU Technology Conference (GTC) this week in Silicon Valley, I noted how well GPU databases are resonating with the marketplace. The vendors in this space were highly regarded judging by the crowds in the technical sessions presented by these companies, and also the general buzz heard on the conference floor. Interest in GPU databases is certainly on the rise.
Take SQream for example, developer of SQream DB, the GPU-accelerated data warehouse designed for rapidly analyzing massive data stores at a fraction of the cost. SQream announced support for NVIDIA DGX-2.
SQream DB enables enterprises to quickly and easily load massive volumes of data in the range of terabytes to petabytes for analysis, while generating higher quality business intelligence faster than any other data store at these volumes. SQream DB already supports NVIDIA DGX-1, and the added support for NVIDIA DGX-2 will allow customers to load and analyze very large data stores even faster.
SQream’s support for NVIDIA DGX-2 will provide our customers with the ability to realize even more rapid business intelligence from very large data stores,” said SQream co-founder and CEO Ami Gal. “This will build on the success we already have with enterprises in telecom, finance, retail and healthcare who are achieving significant results from insights that are providing new revenue growth paths as well as cost savings and efficiencies.”
SQream DB customers will be able to build on NVIDIA DGX-2 high-throughput analytical pipelines from ingest to query for historical data analysis, trend identification, fraud detection, outlier detection and more. Combining SQream DB with other GPU-accelerated machine learning and artificial intelligence frameworks such as TensorFlow and H2O, allows users to create extensive proactive analytics pipelines on very large amounts of data.
NVIDIA DGX-2 is the first 2 petaFLOPS system that combines 16 fully interconnected GPUs for 10X the performance of an 8-GPU system. It is powered by NVIDIA DGX software that enables accelerated deployment and simplified operations at scale, as well as a scalable architecture built on NVIDIA NVSwitch.
Contributed by Daniel D. Gutierrez, Managing Editor and Resident Data Scientist for insideAI News. In addition to being a tech journalist, Daniel also is a consultant in data scientist, author, educator and sits on a number of advisory boards for various start-up companies.
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