Accelerated Data Science and Data Processing
Why acceleration matters here
Data preparation often dominates end-to-end time. GPU acceleration applies to dataframe operations (cuDF, pandas acceleration), array computing (CuPy), SQL and ETL (RAPIDS Accelerator for Apache Spark), and ingestion (DALI). Candidates should know which tool targets which workload rather than assuming one library covers all.
The transfer bottleneck
Moving data between host memory and GPU memory is expensive. Best practice is to keep data on the GPU across a pipeline and minimize round-trips. A common trap is benchmarking a GPU pipeline that spends most of its time in PCIe transfers; the fix is batching, pinned (page-locked) host memory, and overlapping transfers with compute via streams.
Dataframe semantics
GPU dataframes mimic pandas APIs but differ in edge behavior: some operations fall back to CPU, string handling may be limited, and out-of-memory errors appear at different thresholds. Questions may test when a fallback occurs. Rule: measure, do not assume every operation is GPU-accelerated.
Scaling strategies
For datasets larger than GPU memory, use partitioning, chunked processing, or distributed frameworks. For Spark, the RAPIDS Accelerator replaces CPU operators with GPU equivalents when compatible; unsupported operators fall back, so profiling the plan matters.
Worked example
A pipeline loads a 50 GB CSV, filters rows, then aggregates. Loading the full file to GPU memory fails. Better: read in chunks, filter on GPU per chunk, aggregate incrementally. Rule of thumb: push filters and projections as early as possible so less data ever reaches the GPU.