Tom Augspurger
I'm a software engineer at NVIDIA working on GPU-accelerated ETL tools as part of the RAPIDS team. I've helped maintain several libraries in the scientific python and geospatial stacks.
Session
The zarr-python 3.0 release includes native support for device buffers, enabling Zarr workloads to run on compute accelerators like NVIDIA GPUs. This enables you to get more work done faster.
This talk is primarily intended for people who are at least somewhat familiar with Zarr and are curious about accelerating their n-dimensional array workload with GPUs. That said, we will start with a brief introduction to Zarr and why you might want to consider it as a storage format for the n-dimensional arrays (commonly seen in geospatial, microscopy, or genomics domains, among others). We'll see what factors affect performance and how to maximize throughput for your data analysis or deep learning pipeline. Finally, we'll preview the future improvements to GPU-accelerated Zarr and the packages building on top of it, like xarray and cubed.
After attending this talk, you'll have the knowledge needed to determine if using zarr-python's support for device buffers can help accelerate your workload.