Accelerating System-Level Design Tasks Using Commodity Graphics Hardware: A Case

[aruljothi]

Abstract

Many system-level design tasks (e.g. timing analysis, hardware/software partitioning and design space exploration) involve computational kernels that are intractable (usually NP-hard). As a result, they involve high running times even for mid-sized problems. In this paper we explore the possibility of using commodity graphics processing units (GPUs) to accelerate such tasks that commonly arise in the electronic design automation (EDA) domain. We demonstrate this idea via a detailed case study on a general hardware/software design space exploration problem and propose a GPU-based engine for it. Not only does this problem commonly arise in the embedded systems domain, its computational kernel turns out to be a general combinatorial optimization problem (viz. the knapsack problem) which lies at the heart of several EDA applications. Our experimental results show that our GPU-based implementation offers very attractive speedups for this computational kernel (up to 100