Data Processing is Becoming a GPU Workload

Data processing is increasingly transitioning from traditional CPU-based systems to GPU-based systems as organizations seek to extract value from various forms of unstructured, multimodal data such as videos, audio, and sensor outputs. This shift is driven by the need for model inference to process and structure these complex data types, which are not suitable for SQL-based manipulation. As a result, there is a growing reliance on GPUs to handle inference-heavy workloads, enabling new insights and efficiencies from data that were previously difficult to utilize. This evolution introduces challenges in system architecture, as traditional homogeneous clusters can lead to underutilization and inefficiencies, prompting the development of solutions like Ray and Anyscale to optimize resource allocation and execution. The need for streaming execution, handling API-bound stages, and managing large-scale batch inference further highlights the complexities of modern data pipelines, with companies like Netflix, Alibaba, and Nvidia pioneering frameworks to support these advanced workflows.

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