Performance OptimizationHigh-Performance ML at Scale
Comprehensive guide to optimizing performance in ML Bridge applications, including best practices, monitoring, and scaling strategies.
<100ms Latency
1000+ RPS
Auto-Scaling
Key Performance Metrics
Inference Latency
<100ms
Throughput
1000+ RPS
Model Loading
<5s
Resource Util.
>80%
Network Latency
<10ms
TX Finality
<3 blocks
Model Optimization
Techniques to optimize ML models for production environments.
Model Quantization
- • INT8 for 4x speedup
- • Dynamic quantization
- • Post-training quantization
- • Quantization-aware training
Model Pruning
- • Structured pruning
- • Unstructured pruning
- • Magnitude-based pruning
- • Gradual pruning
Knowledge Distillation
- • Teacher-student compression
- • Feature-based distillation
- • Attention transfer
- • Progressive distillation
Model Compilation
- • TensorRT (NVIDIA)
- • ONNX Runtime
- • TensorFlow Lite
- • Apache TVM
Optimization Pipeline Example
# Model optimization pipeline
import torch
from torch.quantization import quantize_dynamic
import tensorrt as trt
# Quantize PyTorch model
quantized_model = quantize_dynamic(
model, {torch.nn.Linear}, dtype=torch.qint8
)
# Convert to TensorRT for GPU optimization
trt_model = torch.jit.trace(quantized_model, example_input)
trt_model = torch_tensorrt.compile(trt_model,
inputs=[torch_tensorrt.Input(input_shape)],
enabled_precisions={torch.half}
)Infrastructure Optimization
GPU Optimization
- • CUDA kernel optimization
- • Memory coalescing patterns
- • Batch processing for throughput
- • Multi-GPU data parallelism
CPU Optimization
- • SIMD vectorization (AVX, SSE)
- • Multi-threading with OpenMP
- • NUMA-aware allocation
- • CPU affinity and thread pinning
Memory Management
- • Memory pool pre-allocation
- • Memory-mapped model weights
- • GC tuning for low-latency
- • Pinned memory for GPU transfer
Caching Strategies
Model Caching
- In-memory storage
- Version management
- Lazy loading
- Worker sharing
Result Caching
- Redis distributed
- Content-based keys
- TTL expiration
- Cache warming
Preprocessing
- Feature extraction
- Tokenization cache
- Image preprocessing
- Embedding vectors
CDN Integration
- Model distribution
- Edge caching
- Geo load balancing
- Cache invalidation
Load Balancing & Scaling
Load Balancing Strategies
Round Robin
Simple distribution across workers
Least Connections
Route to worker with fewest active connections
Weighted Round Robin
Consider worker capacity and performance
Model-Aware Routing
Route based on model availability
Auto-Scaling Configuration
# Kubernetes HPA configuration
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: ml-bridge-hpa
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: ml-bridge-inference
minReplicas: 3
maxReplicas: 50
metrics:
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 70Monitoring & Profiling
Application Metrics
- • Latency P50/P95/P99
- • Throughput (RPS)
- • Error rates
- • Queue depth
System Metrics
- • CPU per core
- • Memory usage
- • GPU utilization
- • Network I/O
Blockchain Metrics
- • TX confirmation
- • Gas costs
- • Contract execution
- • Network congestion
Business Metrics
- • Revenue per inference
- • User satisfaction
- • Model usage
- • Validator earnings
Performance Benchmarks
| Model Type | Hardware | Batch Size | Latency (ms) | Throughput (RPS) |
|---|---|---|---|---|
| BERT-Base | V100 GPU | 1 | 15 | 67 |
| BERT-Base | V100 GPU | 32 | 45 | 711 |
| ResNet-50 | V100 GPU | 1 | 3 | 333 |
| ResNet-50 | V100 GPU | 64 | 12 | 5333 |
| GPT-3.5 | A100 GPU | 1 | 120 | 8 |
Optimization Impact
Quantization (INT8)
+300%
TensorRT
+200%
Batch Processing
+500%
Result Caching
-95% latency
Pruning (50%)
+150%
Recommended Practices
- • Profile before optimizing - measure actual bottlenecks
- • Use appropriate batch sizes for your hardware
- • Implement graceful degradation for high load
- • Monitor performance continuously in production
- • Cache frequently accessed models and results
- • Use asynchronous processing where possible
Common Pitfalls
- • Over-optimizing without measuring impact
- • Ignoring memory bandwidth limitations
- • Using inappropriate batch sizes
- • Not considering cold start penalties
- • Premature optimization without profiling
- • Neglecting network and I/O bottlenecks
Troubleshooting
High Latency
Symptom: Response times > 500ms
Causes: Large batch sizes, memory swapping, network congestion
Solution: Reduce batch size, increase memory, optimize network
Low Throughput
Symptom: RPS below expected capacity
Causes: CPU bottlenecks, insufficient parallelism, I/O blocking
Solution: Scale horizontally, optimize algorithms, use async I/O
Memory Issues
Symptom: OOM errors or high memory usage
Causes: Memory leaks, large models, inefficient caching
Solution: Profile memory, implement model sharing, optimize cache
Low GPU Utilization
Symptom: GPU usage < 80%
Causes: Small batch sizes, CPU bottlenecks, memory transfers
Solution: Increase batch size, optimize data loading, use pinned memory