Array Programming on GPUs: Challenges and Opportunities
Xinyi Li, Mark Baranowski, Harvey Dam, Ganesh K. Gopalakrishnan · 2025
Today, the lion's share of machine learning and high-performance computing workloads is executed on GPUs, including high-stakes applications such as self-driving cars and fusion reactor simulations. Unfortunately, GPU computations are carried out on largely undocumented hardware units that cannot trap or report floating-point exceptions. Worsening the situation is an ongoing and accelerating shift toward lower-precision arithmetic, driven by performance demands—yet this shift only exacerbates the frequency and severity of floating-point exceptions. Increasingly, matrix multiplications are offloaded to specialized hardware such as Tensor Cores. However, because these units do not adhere to a unified arithmetic standard, their computed results can deviate to unacceptable levels.