Multi-GPU Computing
Learn how to leverage multiple GPUs for distributed computing, collective operations, and advanced communication patterns with DotCompute.
🚧 Documentation In Progress - Multi-GPU examples and patterns are being developed.
Overview
Multi-GPU computing enables:
- Distributed data processing across multiple GPUs
- Collective communications (all-reduce, broadcast, gather)
- Peer-to-peer GPU memory transfers
- Ring-based collective operations with NCCL
Distributed Training
Data Parallelism
TODO: Document data-parallel training:
- Data distribution across GPUs
- Forward/backward pass synchronization
- Gradient aggregation
Model Parallelism
TODO: Explain model-parallel training:
- Layer distribution
- Pipeline parallelism
- Activation checkpointing
Scatter-Gather Operations
Scatter
TODO: Document scatter patterns:
- Broadcasting data to multiple GPUs
- Load distribution
- Synchronization
Gather
TODO: Explain gather operations:
- Collecting results from multiple GPUs
- Result aggregation
- Memory management
All-Reduce
Collective All-Reduce
TODO: Cover all-reduce patterns:
- Broadcasting and reduction
- Hierarchical all-reduce
- Bandwidth optimization
Custom All-Reduce
TODO: Document custom implementations
Ring-Reduce
Ring Collective Operations
TODO: Explain ring-based reductions:
- Ring topology benefits
- Bandwidth-optimal reduction
- Implementation details
Ring Kernels Integration
TODO: Document Ring Kernel system integration
Communication Patterns
P2P Transfers
TODO: Cover peer-to-peer communication:
- Direct GPU-to-GPU transfers
- Bandwidth optimization
- Pinned memory usage
NCCL Integration
TODO: Document NCCL usage:
- NCCL operations
- Device topology awareness
- Error handling
Performance Optimization
TODO: List multi-GPU optimization techniques
Examples
TODO: Provide complete multi-GPU examples