Using GPU Technology to Enable Massive Data Analytics Acceleration | SQream

Bringing the Power of the GPU
to the Era of Massive Data

SQream conquers the largest workloads by combining available CPU, GPU, RAM, and storage resources – enabling reports, interactive dashboards, and ad-hoc queries. This balance of CPU and GPU operations ensures optimal performance. The result is faster response times, even on the most complex interactive dashboards.

Small Footprint
Huge Server Room

Breaking Through the Data Size Barriers

SQream easily ingests and analyzes an organization’s largest datasets. Combining the throughput-oriented GPU with some best-of-breed data techniques, SQream DB scales from terabytes to petabytes with ease. Together with the formidable JOIN, which SQream DB runs entirely and effectively in-GPU from its inception, GPU-acceleration is the backbone of SQream’s massive data analytics capabilities.

With upwards of 5,000 cores per-card, GPUs can bring petaflops and 80,000 cores to a standard rackmount server.
Up to 16 GPUs can be installed in a single server, packing more compute per square inch than any other processor.
Low Cost
Supercomputer capabilities in a small package results in lower running costs, and lower overall TCO.

Freeing Organizations from CPU Limitations

When faced with massive analytic workloads, CPU-based systems often hit the wall. As queries grow in complexity, diversity, and the amount of data being processed – acceleration becomes crucial. Thanks to its ability to rapidly perform repetitive operations, the GPU (Graphic Processing Unit) has gained traction as an innovative way to efficiently and cost-effectively process large volumes of data.

Learn More about GPU Databases

Learn More about GPU Databases

Take a look inside SQream to see how GPU databases can help your business grow, with immediate access to raw data