ETL and Scalable GPU Pipelines
Free NVIDIA-Certified Associate: Accelerated Data Science practice — 6 questions on ETL and Scalable GPU Pipelines, with explanations. No sign-up. Full 12-question mixed test →
A data science team runs an ETL pipeline on a single NVIDIA A100 GPU (40GB memory) processing a 200GB dataset with cuDF. Groupby-aggregation operations repeatedly fail with out-of-memory errors. Which approach BEST resolves this while keeping GPU acceleration?
dask-cuda's LocalCUDACluster supports out-of-core processing on a single GPU by partitioning data and spilling device memory to host RAM via device_memory_limit, avoiding OOM while still using GPU acceleration.
A pipeline must read a 2TB dataset stored in Parquet format and apply column selection plus predicate filters to minimize I/O before GPU processing. Which approach BEST achieves this?
Parquet's columnar layout supports column pruning and predicate pushdown at the file level, so specifying columns and filters in read_parquet() minimizes bytes read from disk, maximizing I/O throughput.
When configuring a dask-cuda LocalCUDACluster for a multi-GPU ETL pipeline, which parameter sets the threshold at which device memory begins spilling to host RAM to prevent GPU out-of-memory errors?
device_memory_limit in dask-cuda specifies the GPU memory threshold above which data is spilled from device to host memory, preventing OOM failures during large operations.
What is the primary reason dask-cuda clusters are configured to use UCX (Unified Communication X) instead of default TCP communication in multi-GPU workflows?
UCX allows workers to communicate over high-speed interconnects like NVLink or InfiniBand directly between GPUs, avoiding costly host-memory round trips that TCP-based communication requires, significantly reducing shuffle and transfer overhead.
An 8-GPU dask_cudf pipeline performing a large groupby-merge shows GPU utilization dropping to near zero for extended periods, coinciding with heavy inter-worker network transfer. What is the most likely cause and best remedy?
Groupby-merge operations require data shuffling across workers; when GPU-to-GPU communication is not optimized (no UCX/NVLink) or partitions are too large, shuffle overhead dominates and GPUs sit idle waiting on data transfer. Enabling UCX/NVLink and tuning partition size reduces this bottleneck.
A nightly batch pipeline's dataset grows from 5GB to 90GB over six months, currently processed with single-GPU cuDF on a T4 (16GB memory). At what point should the team plan to migrate to a multi-GPU dask_cudf pipeline rather than simply upgrading GPU memory or falling back to CPU pandas?
cuDF operations (joins, groupbys, sorts) often need 2-4x the input data size in working GPU memory for intermediate buffers; as the dataset approaches the GPU's effective usable capacity, migrating to a multi-GPU dask_cudf pipeline avoids OOM failures and enables continued scaling.
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