AI Accelerator Software Stacks: TPU, Trainium, MTIA & Bring-up

Overview

Why this topic

A custom AI accelerator is only as useful as the software that lets an unchanged PyTorch or JAX program run on it. Google's TPUs, AWS Trainium and Meta's MTIA all solve the same problem: take a model written for a general framework, turn it into a graph, compile that graph for hardware with no CUDA, and run it through a runtime, a driver and firmware.

The infrastructure work is everything around that stack: pinning and shipping matched versions of each layer, caching compilations that take minutes, running compiler CI on scarce silicon, and qualifying each new chip generation before customers see it.

Interview questions

• Walk through what happens between calling a jitted JAX function and the TPU executing it.
• You own the software release for a Trainium training fleet. What exactly do you pin, and how do you roll out a new Neuron SDK version?
• Design compiler CI that runs on real accelerators when silicon is scarce.
• An operator test fails on the new chip but passes on the CPU reference. How do you decide whether it is the compiler, the runtime or the silicon?
• How would you integrate a new accelerator into PyTorch so that existing models run with few or no changes?
• Training throughput on the new generation is 30 percent below the projection. How do you find out where the time goes?

More AI Infrastructure topics

• Bare-metal Provisioning & Fleet Firmware
• Batch GPU Scheduling on Kubernetes: Kueue, Volcano & Gang Scheduling
• CUDA Kernel Profiling, Occupancy & the Roofline
• CUDA Memory Hierarchy & Access Patterns
• Disaggregated Prefill-Decode Architecture
• Distributed Training: NCCL, Collectives & Parallelism
• DPUs in GPU Clusters: BlueField & DOCA
• EDA Licenses & Compute Farms
• Emulation & FPGA Prototyping Pipelines
• Firmware CI with Hardware in the Loop
• GPU Architecture & AI Hardware
• GPU Cluster Fabrics: InfiniBand & RoCE