Powered by GPUs
Machine Learning Warehouse
Kinetica is a distributed, in-memory, GPU database. Its CUDA-optimized user-defined functions API makes it simpler to prepare, train, and deploy predictive analytics and machine learning.
Accelerate the Machine Learning Pipeline
Work with Operational Data
Deploy your Models
Do More with Less
Democratize Data Science
Business users can be empowered to do more sophisticated analysis without resorting to code. Data science teams can develop and test gold-standard simulations and algorithms while making them directly available on the systems used by end users. Foreseeably, in addition to query, reporting and analytics, users could also call a Monte Carlo simulation, or other custom algorithms, straight from their BI dashboard.
How it Works
User-defined functions (UDFs) enable GPU-accelerated data science logic to power advanced business analytics, on a single database platform. UDFs have direct access to CUDA APIs, and can take full advantage of the distributed architecture of Kinetica. Because Kinetica is designed from the ground up to utilize the GPU, users have an advanced set of tools for distributed computation.
UDFs are able to receive filtered data, do arbitrary computations, and then save output to a separate table. The brute-force parallel compute power of the GPU delivers fast response which makes it highly valuable for interactive analytics and experimentation.
GPUs are also particularly well suited for the types of vector and matrix operations found in machine learning and deep learning systems.