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Google·Machine Learning Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say
May 2026

Summary

Google ML Engineer system design round, and they went straight for the jugular with a distributed matrix multiply question. No warmup, no easy stuff first. Left the room genuinely unsure if I'd covered enough ground.

Questions Asked (1)

Q1

Design and implement a distributed matrix multiplication C = A × B across two GPUs, where both A and B must reside on each device. Walk through your data partitioning strategy, which communication primitives you'd use, how you'd schedule compute to overlap with communication, memory layout and buffer reuse, numerical precision choices, synchronization, how you'd aggregate and return C, and how the design scales or handles failures.

System DesignTechnical Trade-offsAlgorithms & Data Structures
Author's notes

This is a beast of a question and I did not treat it like one at first.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (matrix sizes, precision, hardware) and then present a high-level design that partitions both A and B across the two GPUs, uses NCCL for communication, and overlaps compute with communication via CUDA streams. Dive into specifics like 2D block partitioning, double buffering, and mixed precision, and conclude with scaling and fault tolerance considerations.

Pro tip: Emphasize the trade-offs between communication overhead and compute efficiency, and mention real-world libraries like cuBLAS and NCCL that Google uses, showing practical awareness.

1. Clarify Requirements and Constraints

Ask about matrix dimensions, precision requirements, GPU models, interconnect (NVLink/PCIe), and whether the matrices are already distributed or need to be loaded. This informs partitioning and communication strategy.

2. Data Partitioning and Memory Layout

Propose a 2D block partitioning of both A and B across GPUs, ensuring each GPU holds a portion of A and B. Discuss memory layout (row-major vs. column-major) and buffer reuse to minimize allocations.

3. Communication and Compute Overlap

Use NCCL all-gather or all-to-all to exchange necessary blocks. Overlap communication with compute using CUDA streams and double buffering: while computing on one block, prefetch the next.

4. Precision and Synchronization

Choose precision (FP32, TF32, FP16 with FP32 accumulation) based on accuracy needs. Use CUDA events or NCCL barriers for synchronization between GPUs.

5. Aggregation, Scaling, and Fault Tolerance

Aggregate partial results from each GPU (e.g., via all-reduce or gather) to form C. Discuss scaling to more GPUs (e.g., 3D partitioning) and fault tolerance (checkpointing, redundant computation).

Key Points to Mention

  • 2D block partitioning of A and B to minimize communication volume
  • Use of NCCL for collective communication (all-gather, all-to-all, all-reduce)
  • Overlapping communication and compute with CUDA streams and double buffering
  • Memory layout considerations (row-major vs. column-major) and buffer reuse to reduce overhead
  • Numerical precision choices (FP32, TF32, FP16) and their impact on performance and accuracy
  • Synchronization primitives (CUDA events, NCCL barriers) and fault tolerance strategies (checkpointing, redundant computation)

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.