BaM is a system architecture that makes use of SSDs’ fast speed, big density, low latency, and durability.
BaM acceleration makes use of a unique storage driver created especially to make direct storage device access possible thanks to GPUs’ built-in parallelism
Based on the BaM subsystem, the GIDS dataloader hides storage delay and satisfies memory capacity needs for GPU-accelerated Graph Neural Network (GNN) training
To facilitate fast GPU graph sampling, the graph structure data which is usually considerably smaller than the feature data is pinned into system memory
They used the heterogeneous complete dataset from the Illinois Graph Benchmark (IGB) for GNN training in order to demonstrate the advantages of BaM and GIDS
With a size of 2.28 TB, this dataset is too big to fit in the system memory of most systems
They observe that scaling from 1 to 4 Micron 9400 NVMe SSDs significantly improves (reduces) the feature aggregation processing time since the feature data for this system is stored on the SSDs