AI Workload Analysis & Benchmarking

Module Overview

This module teaches the critical skills needed to analyze, profile, and optimize AI workloads - a core responsibility for performance architects at leading technology companies. You'll learn to use professional-grade tools and methodologies to identify bottlenecks and guide architectural decisions.

The AI Performance Challenge

Modern AI workloads present unique challenges:

• Irregular memory access patterns from attention mechanisms
• Dynamic computational graphs in modern frameworks
• Mixed precision operations requiring specialized analysis
• Multi-modal data processing with heterogeneous compute requirements
• Massive scale distributed training with complex communication patterns

Learning Path

1. Workload Characterization Fundamentals

• Computational intensity analysis: FLOPs, memory bandwidth, arithmetic intensity
• Memory access patterns: Temporal and spatial locality in AI workloads
• Execution models: Static vs dynamic graphs, eager vs lazy execution
• Precision requirements: FP32, FP16, INT8, mixed-precision analysis

2. Profiling Tools and Methodologies

• GPU profiling: Hardware vendor profiling tools and performance analyzers
• Framework profiling: TensorBoard, PyTorch Profiler, TensorFlow Profiler
• Custom instrumentation: Building domain-specific profilers
• System-level analysis: CPU, memory, interconnect bottlenecks

3. Benchmark Design and Implementation

• MLPerf benchmarks: Training and inference suites
• Custom benchmark development: Domain-specific workload modeling
• Benchmark validity: Representativeness and reproducibility
• Result interpretation: Statistical analysis and reporting

4. Performance Optimization Strategies

• Kernel optimization: Custom CUDA/HIP kernels for AI operations
• Memory optimization: Data layout, prefetching, caching strategies
• Pipeline optimization: Overlapping computation and communication
• Model optimization: Pruning, quantization, distillation effects on performance

Key Technical Concepts

Roofline Model for AI Workloads

Performance (FLOP/s)
     │
     │    /
     │   /  ← Compute-bound region
     │  /
     │ /
     │/     ← Memory-bound region  
     └─────────────────────
        Arithmetic Intensity (FLOP/Byte)
 
AI Workload Analysis:
- CNN layers: Often compute-bound
- Attention mechanisms: Memory-bound
- Embedding lookups: Memory-bound
- Batch norm: Memory-bound

GPU Utilization Metrics

Key GPU Metrics for AI Workloads:
 
Compute Utilization:
- Tensor Core utilization (for mixed precision)
- CUDA Core utilization (for FP32 operations)  
- SM occupancy and warp efficiency
 
Memory Utilization:
- DRAM bandwidth utilization
- L2 cache hit rates
- Shared memory bank conflicts
 
Communication:
- PCIe bandwidth (multi-GPU)
- NVLink utilization (intra-node)
- InfiniBand/Ethernet (inter-node)

Transformer Profiling Example

# PyTorch Profiler for Transformer Analysis
import torch.profiler as profiler
 
with profiler.profile(
    activities=[profiler.ProfilerActivity.CPU, profiler.ProfilerActivity.CUDA],
    schedule=profiler.schedule(wait=1, warmup=1, active=3),
    on_trace_ready=profiler.tensorboard_trace_handler('./log'),
    record_shapes=True,
    profile_memory=True,
    with_stack=True
) as prof:
    for batch in dataloader:
        output = model(batch)
        loss = criterion(output, targets)
        loss.backward()
        optimizer.step()
        prof.step()
 
# Analysis output shows:
# - Self-attention compute time vs memory time
# - Feed-forward layer utilization
# - Gradient computation bottlenecks
# - Memory allocation patterns

Practical Exercises

Exercise 1: ResNet-50 Bottleneck Analysis

Profile ResNet-50 training on different batch sizes:

• Identify compute vs memory bottlenecks
• Analyze scaling behavior with batch size
• Compare FP32 vs mixed precision performance
• Optimize data loading pipeline

Exercise 2: Large Language Model Inference Profiling

Analyze GPT-style model inference:

• Profile attention computation patterns
• Identify KV-cache performance implications
• Analyze prefill vs decode phase characteristics
• Optimize for different sequence lengths

Exercise 3: Multi-GPU Training Analysis

Profile distributed training setup:

• Analyze communication vs computation overlap
• Identify gradient synchronization bottlenecks
• Measure scaling efficiency across node counts
• Optimize collective communication patterns

Exercise 4: Custom Benchmark Development

Create a benchmark suite for:

• Emerging transformer architectures (MoE, sparse attention)
• Computer vision workloads (object detection, segmentation)
• Multimodal models (CLIP, DALL-E style architectures)

Industry-Standard Methodologies

MLPerf Benchmarking Protocol

MLPerf Training Benchmarks:
┌─────────────────┬─────────────┬───────────────────┐
│ Benchmark       │ Model       │ Key Metrics       │
├─────────────────┼─────────────┼───────────────────┤
│ Image Classif.  │ ResNet-50   │ Time to accuracy  │
│ Object Detection│ SSD         │ mAP convergence   │
│ Translation     │ Transformer │ BLEU score time   │
│ Language Model  │ BERT        │ Masked LM F1      │
│ Recommendation │ DLRM        │ AUC convergence   │
└─────────────────┴─────────────┴───────────────────┘
Results Analysis:

Hardware utilization efficiency
Scaling behavior with system size
Power efficiency (performance/watt)
Cost efficiency (performance/dollar)

Modern GPU Analysis Template

A100 Tensor Core Analysis:
- Theoretical peak: 312 TFLOPS (BF16)
- Memory bandwidth: 1.9 TB/s (HBM2e)
- Actual utilization measurement
- Bottleneck identification
- Optimization recommendations
 
Common A100 Bottlenecks:
1. Memory bandwidth (large models)
2. Tensor Core underutilization (small batch sizes)
3. PCIe bandwidth (multi-GPU communication)
4. CPU preprocessing (data loading)

Advanced Topics

Emerging Workload Analysis

• Mixture of Experts (MoE): Sparse computation patterns
• Neural Architecture Search: Dynamic graph execution
• Federated Learning: Distributed optimization patterns
• Reinforcement Learning: Episode-based computation patterns

Hardware-Specific Optimization

• Advanced GPU Architectures: Transformer Engine and specialized compute unit utilization
• Datacenter GPUs: Matrix Core and tensor processing optimization
• Tensor Processing Units: XLA compiler optimization techniques
• Training-Focused Accelerators: Memory pattern optimization for large-scale training

System-Level Performance

• Storage I/O: Dataset loading and preprocessing
• Network communication: Distributed training patterns
• CPU utilization: Data augmentation and batching
• Memory allocation: Framework overhead analysis

Assessment Framework

Technical Proficiency

• Proficiency with GPU profiling tools (Nsight, etc.)
• Understanding of AI workload characteristics
• Ability to identify and resolve performance bottlenecks

Analytical Skills

• Statistical analysis of benchmark results
• Correlation between workload features and performance
• Prediction of performance on different hardware

Communication

• Clear presentation of profiling results
• Actionable optimization recommendations
• Technical documentation of analysis methodologies

This module prepares you to excel in performance-critical roles at AI hardware companies, where deep understanding of workload behavior drives architectural decisions and optimization strategies.