Research & Engineering Series

GPU Distributed Computing

A 12-part learning path from GPU execution fundamentals to distributed data science and production machine learning—with concepts, runnable examples, performance measurement, and practical trade-offs.

Series Outline

From GPU Fundamentals to Production Systems

The sequence builds from hardware and parallel execution to distributed frameworks, then applies those ideas to machine learning workloads. Published installments will open as standalone, shareable articles.

Part I · Articles 01–04

GPU Foundations

  1. Why GPUs for Data and Machine Learning? Planned

    Compare CPU and GPU workloads, parallelism, throughput, and cases where GPUs are not the right choice.

  2. How GPUs Execute Work Planned

    Understand CUDA grids, blocks, threads, SIMT execution, kernels, and the GPU memory hierarchy.

  3. The Real Cost of GPU Computing Planned

    Explore host-to-device transfers, memory limits, batching, utilization, and profiling fundamentals.

  4. From One GPU to Many Planned

    Distinguish scale-up from scale-out, and examine partitioning, communication, synchronization, and failures.

Part II · Articles 05–08

Distributed GPU Data Processing

  1. RAPIDS: A GPU Data Science Ecosystem Planned

    Meet cuDF, cuML, cuGraph, and RMM, and see how they relate to familiar Python data-science tools.

  2. How Dask Distributes Work Planned

    Learn about tasks, partitions, schedulers, workers, and what changes when workers have GPUs.

  3. Scaling RAPIDS with Dask Planned

    Examine Dask-cuDF, multi-GPU execution, partition sizing, shuffles, and common bottlenecks.

  4. Apache Spark with GPUs Planned

    Understand the RAPIDS Accelerator for Apache Spark, supported operations, GPU execution, fallback, and compatibility.

Part III · Articles 09–12

Machine Learning in Production

  1. XGBoost on a GPU Planned

    Cover tree boosting, GPU training, data formats, memory use, and careful CPU/GPU benchmarking.

  2. Distributed XGBoost Planned

    Explore multi-GPU and multi-node training, Dask integration, data partitioning, and scaling limits.

  3. Choosing the Right Stack Planned

    Compare single-GPU RAPIDS, Dask, Spark with its RAPIDS Accelerator, and distributed XGBoost by workload and operational needs.

  4. A Reproducible End-to-End Case Study Planned

    Follow a dataset from ingestion through processing, training, measurement, and deployment planning.