Graph Embeddings and Neural Networks
GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs
Marius: Learning Massive Graph Embeddings on a Single Machine
P3: Distributed Deep Graph Learning at Scale
Dorylus
Graph / Tensor tasks
Memory intensive
Compute intensive
Motivation: GPUs not a good fit
Compute good
But scalability (limited memory)
Solution: CPUs
Not for compute
Solution: GPU + CPU
Not cost-effective
GPU idle waiting for CPU
Key insight: serverless fits our goals
Large # of parallel threads
Lost-cost, flexible pricing model
Fine grained: only pay for compute resources
Achieve high performance-per-dollar (value)
Challenge
Weak CPU, limited memory
Separate tasks
limited network
Pipeline
Waiting: not fully utilized pipeline
Serverless Optimizations
Task fusion
Tensor re-materialization
Tune number of Lambdas
GNNAdvisor
GNN
High classification accuracy
Better generality
Lower computation complexity
Easier parallelism
GNN: combine graph operations with tensor operations
Acceleration Solutions
Graph Processing Framework
Deep Learning Frameworks
Input Extraction (graph)
Node degree
Embedding Dimensionality
Graph community
Input extraction (GNN model information)
Order of agg and update
Type
2D workload management
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