White Papers

8 GPU Database Acceleration on PowerEdge R940xa
Block Diagram of Brytlyt stack interfacing between PostgreSQL and GPU Cluster
Data Generation involves acquiring, saving, and preparing datasets to train machine learning models. GPU
databases offer advantages in all three data generation tasks:
• For data acquisition, connectors for data-in-motion and at-rest with high-speed ingest make it easier to
acquire millions of rows of data across disparate systems in seconds.
• For data persistence, the ability to store and manage multi-structured data types in a single GPU database
makes all text, images, spatial and time–series data easily accessible to ML/DL applications
• For data preparation, the ability to achieve millisecond response times using popular languages like SQL,
C++, Java, and Python makes it easier to explore very large datasets.
2.5.1 How does Brytlyt help with Machine Learning?
BrytMind is an exciting cutting-edge product from Brytlyt which combines SQL + Artificial Intelligence +
GPU and bridges the gap between tradition SQL, Business Intelligence and data warehousing, and
Artificial Intelligence. AI is an umbrella of technologies, from machine learning to natural language processing
that allows machines to sense, comprehend, act and learn.
BrytMind uses an enhanced and extended version of PyTorch’ s memory management, so there is zero copy
required when getting data from BrytlytDB’s GPU accelerated database into AI models built using PyTorch
running on GPU. GPUs are extremely well suited to Machine Learning and Neural Networks and BrytMind