Fireworks AI CUDA, Triton & Kernel Engineering interview questions

Count warp sectors for contiguous, strided and masked accesses, then use cache and timing evidence to decide whether changing the mapping helps.

Follow intermediate tensors through separate and fused stages, calculate the traffic and whole-block bounds, and check spills and numerical behavior.

Trace stable row normalization, handle independent strides and fully masked inputs, then separate fusion traffic from measured speed.

Follow an accumulator across K tiles, count reuse at each memory level, and decide whether a tile improves measured GEMM performance.

Follow the running maximum and weighted sum across attention tiles, with executable arithmetic and an honest memory-traffic comparison.

Compare the mechanisms in FlashAttention versions 1, 2 and 3: IO savings, work partitioning, asynchronous dependencies and FP8 accuracy.

Separate cache activity from DRAM traffic, check the access width and active lanes, and use a controlled layout change to test a profiler hypothesis. A few percentages cannot predict the fixed kernel’s runtime.

Use a timeline to establish launch overhead, then trace what graph replay changes. Stable buffers, page-table updates, capture-safe operations and batch buckets determine whether the serving engine can use it safely.

Use cuBLAS or cuBLASLt when its supported operation fits. CUTLASS exposes kernel building blocks for custom layouts, fusion and scheduling, but each choice needs a whole-operation benchmark and an ownership plan.

Derive when operand scales can be applied after a dot product and when they must follow its K blocks. Separate integer zero points, floating-point range, scale granularity and accumulator behavior.

Separate per-expert submission, small-grid scheduling and partially filled tiles. A grouped kernel can share work across experts, but routing, packing and output combination still determine the layer’s cost.

Define what the timer includes, give both implementations the same conditions, and keep the sample distribution. A useful benchmark explains the workload and comparison well enough for someone else to reproduce them.

Longer sequences exercise more state updates, different boundaries and sometimes different kernel paths. Use a two-tile example to check online softmax, then test masking, indexing and precision against a stable reference.

Derive resident-block limits and distinguish resident warps from ready work before trading registers for more occupancy.

Map a non-divisible array onto blocks, guard the final 189 threads, and separate kernel traffic from host transfers without inventing measured timings.

Build a mask-aware classifier that counts complete byte intervals, separates duplicate lane demand from distinct bytes, and streams trace summaries.

Derive the bank mapping before and after padding, trace a fixed-tile kernel, and measure whether the saved conflict outweighs other resource costs.

Build a numerical reference, count the traffic and investigate a row-wise fused kernel. Separate bandwidth lower bounds from measured performance.

Compute MFU with a stated work model, then follow the training step’s critical path across input, host dispatch, computation and communication. Rank experiments by exposed time instead of assigning universal gains to fixes.