RT Conference Proceedings
T1 Evaluating the soft error sensitivity of a GPU-based SoC for matrix multiplication
A1 León, Germán
A1 Badía, Jose M.
A1 Belloch Rodríguez, José Antonio
A1 Lindoso Muñoz, Almudena
A1 Entrena Arrontes, Luis Alfonso
AB System-on-Chip (SoC) devices can be composed of low-power multicore processors combined with a small graphics accelerator (or GPU) which offers a trade-off between computational capacity and low-power consumption. In this work we use the LLFI-GPU fault injection tool on one of these devices to compare the sensitivity to soft errors of two different CUDA versions of matrix multiplication benchmark. Specifically, we perform fault injection campaigns on a Jetson TK1 development kit, a board equipped with a SoC including an NVIDIA 'Kepler” Graphics Processing Unit (GPU). We evaluate the effect of modifying the size of the problem and also the thread-block size on the behaviour of the algorithms. Our results show that the block version of the matrix multiplication benchmark that leverages the shared memory of the GPU is not only faster than the element-wise version, but it is also much more resilient to soft errors. We also use the cuda-gdb debugger to analyze the main causes of the crashes in the code due to soft errors. Our experiments show that most of the errors are due to accesses to invalid positions of the different memories of the GPU, which causes that the block version suffers a higher percentage of this kind of errors.
PB Elsevier Ltd.
SN 0026-2714
YR 2020
FD 2020-11
LK https://hdl.handle.net/10016/35621
UL https://hdl.handle.net/10016/35621
LA eng
NO Proceeding of: 31th European Symposium on Reliability of Electron Devices, Failure Physics and Analysis (ESREF 2020), Athens, Greece, 4th to 8 October 2020 (Virtual conference)
NO This work has been supported by the Spanish Government through TIN2017-82972-R and ESP2015-68245-C4-1-P, and by the Valencian Regional Government through PROMETEO/2019/109.
DS e-Archivo
RD 18 jul. 2024