STLs for GPUs: Using High-Level Language Approaches

被引：0

作者：

Guerrero-Balaguera, Juan-David ^{[1
]}

Condia, Josie E. Rodriguez ^{[1
]}

Reorda, Matteo Sonza ^{[1
]}

机构：

[1] Politecn Torino, Dept Control & Comp Engn DAUIN, I-10129 Turin, Italy

来源：

IEEE DESIGN & TEST | 2023年 / 40卷 / 04期

关键词：

Graphics processing units; Registers; Programming; Computer architecture; Libraries; Observability; High level languages; Fault detection; Complexity theory; Built-in self test; In-field Testing; Graphics Processing Units (GPUs); High-Level Programming Languages; Self-Test Libraries (STLs); Software-Based Self-Test (SBST);

D O I：

10.1109/MDAT.2023.3267601

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Self-test libraries (STLs) that are widely used for in-field fault detection in processor-based systems can also be used in graphics processing units (GPUs). This work describes a method to develop suitable STLs resorting to high-level languages (HLLs) (e.g., CUDA) for some modules in GPUs, thus reducing the complexity of the STLs development process. The main advantages, limitations and constraints or developing HLL STLs for GPUs are discussed. The libraries are evaluated using FlexGripPlus GPU model. The experimental results show that HLL STLs can be effectively developed for regular modules in the GPU.

引用

页码：51 / 60

页数：10

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