Overview

Instructor(s): Jian Tao

Time: March 2026

Location: Online using Zoom

Prerequisite(s): Current ACCESS ID, basic Python skills

CuPy is an open-source array library for GPU-accelerated computing that provides a largely drop-in replacement for NumPy and SciPy, allowing many array programs to run on GPUs by switching imports while keeping idiomatic vectorized code.

CuPy offers NumPy/SciPy-compatible functionality plus GPU-focused features, including FFTs, linear algebra, sparse operations, random number generation, memory pools, streams, and custom kernels, enabling end-to-end acceleration on modern GPU platforms.

This class will provide an introduction to CuPy for accelerating Python array and scientific workflows.

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Course Materials

• Introduction to CuPy: NumPy and SciPy for GPU (Fall 2025): PDF

Participation

During the training, attendees are expected to log in to an HPRC cluster using their own computer and complete the instructor-led examples and exercises.

Learning Objectives and Agenda

Learning Objectives:

• Learn the basic code elements of CuPy and its NumPy-compatible array API.
• Access a GPU-enabled environment and verify CuPy installation and device selection.
• Use CuPy interactively in Jupyter or a Python REPL.
• Use arithmetic, comparison, and logical operators on CuPy arrays.
• Understand and apply core CuPy dtypes and array creation/manipulation routines.
• Work with strings and categorical-like data via vectorized array operations where applicable.
• Use common data structures with CuPy-backed arrays, including dense arrays and sparse matrices.
• Write and use basic functions and ufunc-style operations for vectorized GPU computation.