Part 9
Completed
FLOPs, Memory, Bandwidth, Precision
Built a comprehensive profiling framework analyzing transformer operations across FLOPs, memory footprint, bandwidth utilization, and numerical precision. Studied the impact of mixed precision, quantization, and kernel fusion on end-to-end performance.
What I Built
Built a comprehensive profiling framework analyzing transformer operations across FLOPs, memory footprint, bandwidth utilization, and numerical precision. Studied the impact of mixed precision, quantization, and kernel fusion on end-to-end performance.
Key Concepts
FLOP AnalysisMemory FootprintBandwidth UtilizationMixed PrecisionKernel FusionRoofline Model
Architecture
1
FLOP Counter2
Memory Profiler3
Bandwidth Monitor4
Precision Tester5
Roofline AnalyzerResults
Identified attention as bandwidth-bound and FFN as compute-bound. BF16 training achieves 1.6x speedup with no quality loss. INT8 inference 3.2x faster.
Key Learnings
- Not all operations are compute-bound—memory bandwidth is often the bottleneck
- Mixed precision requires careful handling of gradient scaling
- Roofline model accurately predicts optimization potential
Challenges
- Accurate FLOP counting for complex operations
- Understanding interaction between precision and training stability
- Modeling memory bandwidth constraints accurately