Up to this point, the compilation pipeline has operated on abstract representations of tensor operations. While graph-level transformations and loop tiling improve general data locality, they do not inherently account for the physical characteristics of the target device. To maximize throughput, the compiler must map the Intermediate Representation (IR) directly to the instruction set and memory hierarchy of the hardware.

This chapter focuses on code generation strategies for Graphics Processing Units (GPUs), using NVIDIA’s architecture as the primary reference. You will examine how abstract loop nests are translated into the CUDA execution model. This process involves mapping logical iterations to thread blocks and individual threads while ensuring memory coalescing to maximize bandwidth utilization.

Efficient memory access is often the limiting factor in deep learning workloads. We will analyze the GPU memory hierarchy, specifically the movement of data from global memory to shared memory and registers. You will learn strategies to prevent bank conflicts in shared memory, which occur when multiple threads attempt to access different addresses in the same memory bank simultaneously.

The text also covers the utilization of specialized hardware units. Modern accelerators provide matrix-multiply-accumulate units, such as Tensor Cores, which offer significantly higher arithmetic density than standard floating-point units. You will learn how compilers emit the necessary intrinsics to target these units. For instance, a standard matrix multiplication operation

$C_{m,n} = \sum_{k} A_{m,k} \times B_{k,n}$

must be decomposed into fragments that align with the hardware's specific operand sizes and data types.

Finally, we will introduce Triton, a language and compiler designed to automate many of these complex memory management tasks. By the end of this chapter, you will understand the mechanics behind generating high-performance kernels and the specific constraints that dictate their efficiency.