hardGoogleGroqCerebras

Explain how a systolic array computes a matrix multiply. Why is it efficient, and why does it want big square tiles?

Trace a two-by-two output-stationary array cycle by cycle. Derive its fill and drain cost without turning a toy schedule into a TPU benchmark.

Updated Oct 2026 · Learn how AI infrastructure works through explanations, worked examples and diagrams, then practise applying it to interview questions.

Trace a two-by-two output-stationary array cycle by cycle. Derive its fill and drain cost without turning a toy schedule into a TPU benchmark.

This answer is included with a free account. Sign in to read it and save your progress.

free account · no card needed

Work through the numbers

Change the assumptions in a free calculator, then explain what the result establishes and what still needs measurement.

The concepts behind this question

Use these concept explanations to review the ideas behind this question.

Advanced

🧩 GPU & Accelerator Architecture🔒 Premium

TPU Architecture and Systolic ArraysTPUs use matrix units and compiler-managed execution to run machine-learning workloads. Memory, shapes and inter-chip communication still determine how efficiently a model runs.

Advanced

⚡ Kernels & Compilers🔒 Premium

Tiled Matrix MultiplicationTiled matrix multiplication reuses input values across many output calculations. Shared-memory and register tiles reduce repeated data movement while consuming limited on-chip resources.

Advanced

🧩 GPU & Accelerator Architecture🔒 Premium

Tensors, Shapes and DtypesA tensor is an array of numbers with a shape, a representation and a storage location. Learn to read batch and sequence dimensions, calculate bytes, and distinguish a reshape from a transpose before sizing GPU work.

Core

🧩 GPU & Accelerator ArchitectureSign in

Tensor Cores and Matrix UnitsTensor cores accelerate supported matrix multiply-accumulate operations. Their performance depends on precision, matrix shape and how efficiently the kernel supplies data.

Explore the full curriculum →

UP NEXT ON YOUR JOURNEY

Next in this trackWould you move a cost-sensitive training and serving fleet from H100 to Trainium2? What do you gain, and what do you have to plan for?Next in this trackThe MI300X has 192 GB per card. When does AMD actually win against an H100 for LLM serving, and what would you check before betting on it?Next in this trackWhy are Groq and Cerebras so fast at batch 1, and what does that speed cost at scale?

FEDITOR'S NOTE

Track matching operands into each cell and keep the partial sum there. Count idle cell-cycles as well as useful multiply-adds before interpreting array utilization.

DISCUSSION · 0

No comments yet — be the first to share your approach.