Global Software Career Advancement

Create a correct benchmark harness that separates kernel time from allocation and transfer time.

Transform an uncoalesced global-memory access pattern into a coalesced one.

Use shared-memory tiling to reduce redundant global-memory traffic.

Identify and remove shared-memory bank conflicts in a CUDA kernel.

Use warp-level primitives to replace unnecessary block-wide communication.

Adjust block geometry to balance occupancy, locality, and available parallelism.

Increase instruction-level parallelism without introducing harmful register pressure.

Audit an AI-generated CUDA optimization for correctness, synchronization safety, and hardware plausibility.

Generate PTX and SASS from a CUDA source file using the public CUDA toolchain.

Map a source-level operation to its corresponding PTX and SASS instruction sequences.

Interpret predicates, address calculations, and control flow in GPU assembly.

Detect register spills and unintended local-memory traffic in generated code.

Explain how compiler flags change numerical behavior and generated instructions.

Compare two kernel variants using instruction mix and static resource usage.

Determine whether a source-level optimization survived compilation.

Design a controlled GPU performance experiment with a falsifiable hypothesis.

Measure asynchronous GPU work correctly with CUDA events and host-side synchronization.

Quantify benchmark variability using warm-up, repeated trials, and summary statistics.

Calculate arithmetic intensity for a CUDA kernel.

Use a roofline model to classify a kernel's attainable performance regime.

Derive bandwidth, throughput, and utilization metrics from raw measurements.

Explain the limits and side effects of hardware-counter collection.

Estimate whether profiling overhead could invalidate a performance conclusion.

Classify a workload bottleneck as compute, memory bandwidth, latency, synchronization, or launch overhead.

Interpret a CPU–GPU timeline to locate idle gaps and serialization.

Use warp-stall evidence to select the next performance hypothesis to test.

Distinguish cache, TLB, coalescing, and DRAM-bandwidth problems from measured evidence.

Diagnose lost concurrency caused by stream dependencies and synchronization.

Determine when CUDA Graphs can materially reduce launch overhead.

Attribute an observed speedup to a specific change rather than measurement noise.

Build a minimal reproducer for a GPU performance regression.

Trace the lifecycle of profiling data from hardware event generation to a user-facing metric.

Define the accuracy, overhead, privilege, and compatibility requirements for a profiling feature.

Explain why hardware counters may require multiplexing across measurement passes.

Design a counter-multiplexing plan for an event set that cannot be collected simultaneously.

Correlate CPU, driver, and GPU events across timestamp domains.

Identify buffering and backpressure conditions that can cause profiling data loss.

Validate a profiler metric against a purpose-built microbenchmark.

Write a public-information-only design note for a hypothetical profiling capability.

Map a multi-GPU system's PCIe, NVLink, CPU, and NUMA topology.

Predict when peer-to-peer transfers will outperform host-staged transfers.

Explain the communication steps of ring and tree collective algorithms.

Interpret an NCCL trace to identify a communication bottleneck.

Calculate strong-scaling and weak-scaling efficiency from benchmark results.

Model the benefit available from overlapping communication with computation.

Diagnose topology-related scaling loss in a multi-GPU workload.

Design a reproducible experiment for comparing two collective configurations.

Decompose a transformer workload into GEMM, attention, normalization, communication, and data-movement phases.

Relate matrix dimensions and data types to Tensor Core utilization.

Explain how reduced precision changes throughput, memory traffic, and numerical risk.

Determine when kernel fusion can reduce launch and memory overhead.

Diagnose low GPU utilization in a framework-generated execution trace.

Model the memory footprint of parameters, activations, optimizer state, and KV cache.

Explain how data, tensor, and pipeline parallelism create different communication patterns.

Analyze the latency–throughput trade-off of batching in an inference service.

Diagnose Unified Memory page migration and oversubscription from profiling evidence.

Explain how MPS changes GPU sharing and concurrency behavior.

Explain how GPU partitioning changes resource isolation and observable performance.

Identify profiling limitations introduced by virtualization, permissions, and multi-tenancy.

Construct a microbenchmark that isolates one newly introduced GPU feature.

Compare a new architecture's measured behavior with predictions from its public specifications.

Create a repeatable checklist for updating a profiling methodology across GPU generations.

Translate a developer performance problem into measurable profiling requirements.

Specify a hardware observability requirement without prematurely prescribing its implementation.

Evaluate profiling proposals across accuracy, overhead, area, power, privacy, and compatibility trade-offs.

Define acceptance criteria for a cross-layer GPU profiling feature.

Write a concise architecture decision record for a profiling-system trade-off.

Present an evidence-based recommendation to hardware, driver, and tools stakeholders.

Identify technical and organizational risks in a multi-generation profiling roadmap.

Lead a blameless performance postmortem from evidence to preventive actions.

Define a target-role rubric for GPU performance positions in the United Kingdom, Switzerland, Europe, and Australia.

Rewrite a résumé achievement as a quantified, confidential-safe impact statement.

Build a confidential-safe case study explaining one profiling problem, decision, and result.

Deliver a two-minute career narrative connecting driver work to staff-level GPU performance impact.

Answer a staff-level behavioral question using a structured influence-across-teams example.

Solve a GPU performance interview problem aloud using explicit hypotheses and evidence.

Design a scalable GPU profiling service during a timed systems-design exercise.

Evaluate an international offer using role scope, level, total compensation, taxation, visa risk, and cost of living.