IBM·Software Engineer·Onsite - System Design / Architecture·Senior

Senior

Jul 2026

Summary

IBM software engineer interview with a meaty systems question about multithreaded convolution. The kind of problem that sounds approachable until you're actually in the room trying to talk through SIMD, false sharing, and load balancing all at once.

Questions Asked (1)

Design and implement a multithreaded CPU-based valid 1D convolution. For three specific scenarios (short kernel, full-length kernel, and a thread-capped environment with up to 100 workers), explain how you'd partition work across threads, handle cache locality, avoid false sharing, manage synchronization, apply SIMD vectorization, and choose tile and chunk sizes. Provide pseudocode or an API-level design for a unified configurable routine.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

This one hit me harder than I expected.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (input size, kernel size, thread count, hardware features) and then present a unified configurable routine that adapts partitioning, tiling, and vectorization based on the scenario. For each scenario, explain the trade-offs in work partitioning, cache locality, false sharing avoidance, synchronization, and SIMD usage, and justify your choices with performance reasoning.

Pro tip: Emphasize that you would measure and tune parameters like tile size and chunk size using profiling, and mention that you'd consider using non-temporal stores for large outputs to avoid cache pollution.

1. Clarify requirements and constraints

Ask about input size, kernel size, number of threads, CPU architecture (SIMD width, cache sizes), and whether the kernel is known at compile time. This determines the design space.

2. Design a unified configurable routine

Propose an API that takes input, kernel, output, and a configuration struct with parameters like tile size, chunk size, and thread count. The routine internally selects a strategy based on the scenario.

3. Explain partitioning and synchronization for each scenario

For short kernel: partition output into contiguous chunks per thread, use SIMD across output elements, and avoid synchronization. For full-length kernel: use a tiled approach where each thread computes a tile of output, loading input tiles into shared memory or registers, and synchronize only if needed. For thread-capped: use a work-stealing or dynamic scheduling with atomic counters to balance load, and consider persistent threads.

4. Address cache locality and false sharing

Use tiling to keep input and kernel in cache, align data to cache lines, and pad per-thread accumulators to avoid false sharing. For thread-capped, ensure that work chunks are large enough to amortize synchronization but small enough for load balance.

5. Apply SIMD vectorization and choose tile/chunk sizes

Vectorize the inner loop over output elements using SIMD intrinsics or compiler auto-vectorization, ensuring alignment and no dependencies. Choose tile sizes to fit in L1/L2 cache and chunk sizes to minimize overhead; tune empirically.

Key Points to Mention

Work partitioning strategies: output-based partitioning for short kernels, tiled input-based for full-length kernels, and dynamic scheduling for thread-capped environments.
Cache locality: blocking/tiling to reuse input and kernel data, and using shared memory or registers to reduce memory traffic.
False sharing avoidance: padding per-thread data structures to cache line size and aligning output buffers.
Synchronization: minimal synchronization for independent output chunks, and using atomics or barriers only when necessary for reductions or shared state.
SIMD vectorization: using intrinsics or auto-vectorization with aligned loads/stores, and handling remainder elements.
Tile and chunk size selection: based on cache size, SIMD width, and thread count, with empirical tuning and consideration of overhead vs. load balance.

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