GPU (Graphics Processing Unit)
A Graphics Processing Unit is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images and handle parallel processing tasks.
Key Features
- Massively Parallel Architecture: Thousands of small cores optimized for parallel workloads
- SIMD Processing: Single Instruction, Multiple Data execution model
- Specialized Units: Tensor cores for matrix operations, RT cores for ray tracing
- High Memory Bandwidth: Optimized for large data throughput
Deep Learning Applications
GPUs have become fundamental to deep learning due to:
- Parallelism: Efficient handling of batched operations
- Matrix Operations: Specialized hardware for key ML primitives
- Memory Hierarchy: Caching systems suited for deep learning workloads
NVIDIA H100 Specifications
- BF16 Performance: ~9.89e14 FLOPs/s (with sparsity)
- Memory Bandwidth: 3.35 TB/s
- Critical Arithmetic Intensity: ~298 FLOPs/byte
- Tensor Cores: Specialized for matrix multiplication operations
Comparison with TPUs
- Programming Model: More flexible but potentially more complex
- Ecosystem: More mature software stack and broader adoption
- Performance Profile: Generally comparable peak performance to TPUs
- Availability: Available for purchase rather than cloud-only access
Optimization Considerations
- CUDA Programming: Optimizing kernels for GPU execution
- Memory Management: Managing data transfers between host and device memory
- Batch Size: Like TPUs, requires sufficient batch size to reach compute-bound regime
- Mixed Precision: Using FP16/BF16 with FP32 accumulation for optimal performance