DaskCUDAs

Dask-CUDA is a component of the Dask parallel computing library that enables the use of NVIDIA GPUs for accelerating Dask workflows. It extends Dask's familiar array and DataFrame APIs to operate on data residing on GPUs, leveraging libraries like cuDF and cuPy. This allows users to scale data science and machine learning tasks that are bottlenecked by CPU processing to multiple GPUs and multiple machines.

The core idea behind Dask-CUDA is to distribute computations across available GPUs, treating each GPU as a worker in a Dask cluster. This means that instead of processing data on the CPU, Dask-CUDA offloads operations to the GPU, which can perform many computations much faster than CPUs, especially for tasks involving large datasets and parallelizable operations.

Dask-CUDA integrates seamlessly with the Dask ecosystem, allowing users to write code that looks very similar to standard Dask or Pandas/NumPy code, but with the added benefit of GPU acceleration. This includes operations on Dask-CUDA DataFrames, which are analogous to Dask DataFrames but backed by cuDF Series, and Dask-CUDA Arrays, which are backed by cuPy arrays.

Key features of Dask-CUDA include the ability to manage GPU memory, schedule computations efficiently across multiple GPUs, and handle data transfer between host (CPU) and device (GPU) memory. It supports various deployment modes, from running on a single machine with multiple GPUs to distributed clusters spanning many nodes, each with its own set of GPUs. This makes it a powerful tool for tackling computationally intensive data analytics and deep learning workloads.