Matrix Multiplications on GPUs Run Faster When Given "Predictable" Data
2 days ago
- #Power Throttling
- #GPU Optimization
- #Matrix Multiplication
- Matrix multiplication performance on GPUs varies with input data types due to dynamic power usage, where predictable data like zeros reduces transistor switching and power consumption.
- Under power limits, GPUs may throttle clock speeds when dynamic power from unpredictable data (e.g., random values) exceeds thresholds, impacting achievable FLOPS.
- Benchmarks show that uniform or constant inputs yield higher teraflops than normally distributed data, revealing a gap between theoretical peak FLOPS and real-world performance influenced by power constraints.
- Adjusting GPU power and clock limits demonstrates that predictable inputs mitigate throttling effects, highlighting power as a key bottleneck in high-performance computing, especially with newer GPUs like H100 and B100.