Speaker
Description
High-energy physics (HEP) analyses routinely handle massive datasets, often exceeding the available resources. Efficiently interacting with these datasets requires dedicated techniques for data loading and management. Awkward Array is a Python library widely used in high-energy physics (HEP) to efficiently handle complex, irregularly structured ("ragged") data. It transforms flat arrays of data into nested structures, naturally representing physics objects such as particles and their properties. Typically, a physics analysis uses only a subset of these objects or properties, providing an opportunity to significantly reduce memory consumption by loading data from disk lazily, only when explicitly needed.
In this talk, we introduce and demonstrate the new "Virtual Arrays" feature in Awkward Array, enabling lazy loading of data buffers. Instead of immediately loading entire datasets, Virtual Arrays allow delayed data reading from disk until an explicit computation requests them. This approach greatly reduces memory consumption and enhances computational efficiency, allowing analyses to access significantly larger datasets interactively and responsively.
We will describe the design and usage of Virtual Arrays, illustrating how physicists can seamlessly integrate lazy data loading into existing workflows using Coffea—the Columnar Object Framework For Effective Analysis. Coffea enables efficient analysis of event data with columnar operations and transparently scales computations from personal laptops to large distributed infrastructures without modifying analysis code. Concrete high-energy physics examples, including selective data processing and histogramming, will highlight how Virtual Arrays substantially optimize analyses, significantly reducing the time-to-insight for data-intensive collider experiments.