tensor cores

**Tensor cores** is the **specialized GPU execution units optimized for matrix-multiply-accumulate operations** - they deliver high throughput for deep learning workloads that are dominated by dense linear algebra. **What Is Tensor cores?** - **Definition**: Hardware units that accelerate matrix math at mixed-precision formats such as fp16 and bf16. - **Workload Fit**: Most beneficial for GEMM-heavy operations in transformers and convolutional networks. - **Precision Modes**: Support multiple input-output precision combinations depending GPU generation. - **Utilization Dependency**: Requires kernel tiling and data layout choices that map efficiently to tensor operations. **Why Tensor cores Matters** - **Throughput**: Tensor cores provide major speedups over scalar-oriented execution paths for matrix workloads. - **Energy Efficiency**: Higher arithmetic density improves compute per watt for training and inference. - **Scale Economics**: Better per-GPU performance reduces total cluster hours needed for model development. - **Algorithm Alignment**: Modern deep learning architectures are designed to exploit tensor-core friendly math. - **Competitive Capability**: Effective tensor-core usage is critical for state-of-the-art model training velocity. **How It Is Used in Practice** - **Kernel Selection**: Use libraries and compiler paths that emit tensor-core optimized kernels. - **Shape Tuning**: Choose batch and hidden dimensions that align with hardware tile preferences. - **Performance Profiling**: Track tensor-core occupancy and fallback rates to detect underutilization. Tensor cores are **the primary acceleration engine for modern deep learning compute** - workloads that map well to tensor math achieve far higher throughput and efficiency.