Description

The Awkward Array library provides a NumPy-like interface to the nested and unequal-length datasets that are often required by particle physicists. Since its release in September 2018, it has been used in a variety of particle physics analyses. In late 2019/early 2020, it was rewritten in C++ as Awkward 1.x, and the transition from Awkward 0.x to Awkward 1.x is ongoing.

Since Awkward Array encourages array-at-a-time programming, analysis scripts that use it are already in a good position to take advantage of GPUs. Algorithms designed for GPUs also operate array-at-a-time because this permits threads to run independently and thus achieve better performance. Awkward 1.x’s C++ implementation provides a good starting point for adding GPU kernels in CUDA and/or OpenCL.

The goal of this project is to add a GPU backend to Awkward Array 1.x. With such a backend, the library would have a unique capability of processing complex, JSON-like data that is wholly resident on GPUs, in an efficient way, without specialized compilation or even leaving the Python prompt.

Task ideas

• Track “memory location” through Awkward Array classes: main memory versus GPU (and which GPU). Arrays derived from GPU arrays should live on the same GPU, and operations involving a CPU array and a GPU array should be handled intelligently.
• Characterize the “CPU kernels” in a way that documents them and facilitates bulk translation to “GPU kernels.” Maybe create a tool that performs a naive translation for most existing and any new CPU kernels.
• Identify kernels whose naive translations are already optimal on GPUs and those that can be improved by GPU-specific algorithms. For example, some kernels are prefix sums, implemented on the CPU by a loop-carried dependency, which can be dramatically improved by a Hillis-Steele prefix sum on the GPU. Other examples may be evident.
• Develop a deployment strategy for users with GPUs and users without GPUs (and users with non-Nvidia GPUs). CUDA and OpenCL should be actively compared.
• Measure performance of array operations with and without a GPU, including memory transfer time.

Expected results

By the end of the summer, we would like to see a complete implementation of Awkward Array GPU kernels. It should be reasonably performant, though it need not have the best possible performance.

Evaluation tasks

Contact the mentors for a task that we’ll use to evaluate candidates.

Requirements

• Good software engineering skills; well-organized coding.
• Rigorous testing and documentation.
• Familiarity with CUDA and/or OpenCL and C++.
• Interest in parallel algorithms.

Mentor

• Jim Pivarski
• David Lange

Corresponding Project

• IRIS-HEP

Participating Organizations

• princeton

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