Hardware Acceleration

GPU

1. The database operation accelerated by the CPU must be parallelizable, and in many cases parallelizing an operation is not trivial. Relational operations like filtering, sorting, aggregating, grouping and even joining tables are all possible on GPU.
2. Data usually resides in CPU memory in vectorized columns to optimize parallel processing across all available GPUs. The data is moved as needed to GPU memory for both mathematical and spatial calculations, and the results then returned to CPU.
3. Brytlyt develops a unique approach to accelerate JOINS by GPU. by devising a patent-pending method that recursively separates rows containing a hit from rows that do not. It breaks the data into blocks and then distributes the blocks to the many cores used for searching.
4. Disk-IO bottleneck and PCIe bottleneck are two challenges in the development of the GPU-accelerated database. In order to avoid PCIe bottlenecks and use the full capabilities of CPU and GPU, brytlyt suggests having a ratio of 1:1.

Data Model

Object-Relational