flops utilization

**FLOPs utilization** is the **ratio of achieved floating-point compute throughput to the theoretical hardware peak** - it indicates how effectively accelerator arithmetic capacity is being used during training or inference. **What Is FLOPs utilization?** - **Definition**: Achieved FLOPs divided by device peak FLOPs under the same precision mode. - **Gap Sources**: Memory stalls, kernel launch overhead, communication waits, and non-tensor operations. - **Interpretation**: Moderate utilization can still be excellent depending on model structure and memory intensity. - **Related Metrics**: Often analyzed with occupancy, memory bandwidth, and kernel efficiency counters. **Why FLOPs utilization Matters** - **Hardware Efficiency**: Shows whether expensive accelerators are compute-bound or waiting on other resources. - **Optimization Targeting**: Low utilization guides focus toward bottleneck class rather than generic tuning. - **Comparative Benchmark**: Enables apples-to-apples evaluation across kernels, models, and software stacks. - **Cost Insight**: Better utilization usually lowers training time and infrastructure expense. - **Scaling Confidence**: Utilization trends expose diminishing returns during multi-node expansion. **How It Is Used in Practice** - **Profiler Integration**: Collect achieved FLOPs and supporting counters with consistent benchmark workloads. - **Kernel Tuning**: Improve fusion, tiling, and precision selection to raise effective compute density. - **System Balance**: Address data and communication stalls that suppress arithmetic pipeline usage. FLOPs utilization is **a key efficiency signal for accelerator performance engineering** - understanding utilization gaps is essential for turning peak hardware specs into real workload throughput.