Teaching Accelerated Computing with Hands-On Experience

Abstract

Heterogeneous computing systems maintain high-performance executions with parallel hardware resources. Graphics Processing Units (GPUs) with many parallel efficient cores and high-bandwidth memory structures enable accelerated computing for high-performance, deep learning, and embedded programs from diverse domains. The expertise in GPU programming requires a significant effort to utilize parallel computational units efficiently. Teaching programming for heterogeneous systems also becomes difficult due to dedicated hardware requirements and up-to-date course materials. In this paper, we present our teaching experience in an undergraduate parallel programming course, where we adopt NVIDIA Deep Learning Institute workshop and teaching kit contents and GPU devices at different scales to expose students to a set of hardware platforms with hands-on coding experience.