Search

Graphic processing units (GPUs) have become a basic accelerator both in high-performance nodes and low-power system-on-chip (SoC). They provide massive data parallelism and very high performance per watt. However, their reliability in harsh environments is an important issue to take into account, especially for safety-critical applications. In this article, we evaluate the influence of the parallelization strategy on the reliability of lower–upper (LU) decomposition on a GPU-accelerated SoC under proton irradiation. Specifically, we compare a memory bound and a compute bound implementation of the decomposition on a K20A GPU embedded on a Tegra K1 (TK1) SoC. We leverage the GPU and CPU clock frequencies both to highlight the radiation sensitivity of the GPU where we are running the benchmark and also to apply both algorithms to solve problems with the same size when exposed to the same radiation dose. Results show that more intensive use of the resources of the GPU increases the cross section. We also observed that most of the radiation-induced errors hang the operating system and even the rebooting process. Finally, we present a preliminary study of the error propagation of the LU decomposition algorithms. [ABSTRACT FROM AUTHOR]

This work analyzes the performance of the reduced precision redundancy (RPR) error mitigation technique using the fast Fourier transform (FFT) as a case study. To this purpose, several configurations of an FFT IP design were implemented in field-programmable gate array (FPGA) using reduced precision triple modular redundancy (RP-TMR) and tested under proton irradiation and fault injection. The cross section, the sensitivity to common-mode failures (CMFs), and the signal-to-noise ratio of these configurations were evaluated. The results of the radiation experiments and fault injection campaigns are in agreement and show that the RP-TMR technique may be used as an alternative to triple modular redundancy (TMR) if small errors can be tolerated, as it has a good performance in terms of error correction capabilities, area usage, and sensitivity to critical errors. [ABSTRACT FROM AUTHOR]

Commercial off-the-shelf (COTS) system-on-chip (SoC) are becoming widespread in embedded systems. Many of them include a multicore central processing unit (CPU) and a high-end graphics processing unit (GPU). They combine high computational performance with low power consumption and flexible multilevel parallelism. This kind of device is also being considered for radiation environments where large amounts of data must be processed or compute-intensive applications must be executed. In this article, we compare three different strategies to perform matrix multiplication in the GPU of a Tegra TK1 SoC. Our aim is to analyze how the different use of the resources of the GPU influences not only the computational performance of the algorithm, but also its radiation sensitivity. Radiation experiments with protons were performed to compare the behavior of the three strategies. Experimental results show that most of the errors force a reboot of the platform. The number of errors is directly related with how the algorithms use the internal memories of the GPU and increases with the matrix size. It is also related with the number of transactions with the global memory, which in our experiments is not affected by the radiation. Results show that the smallest cross section is obtained with the fastest algorithm, even if it uses the cores of the GPU more intensively. [ABSTRACT FROM AUTHOR]

Complexity of current digital systems and circuits involves new challenges in the field of hardening and measuring circuit's sensitivity under SEEs. In this work, a new solution for evaluating the SEU sensitivity of space systems based on using programmable logic devices is proposed. This solution is able to perform a deep analysis of fault effects in systems with hardware functionality distribution, taking into account the high complexity of the hardware nodes (complex programmable logic devices) and their collaborative hardening properties. [ABSTRACT FROM AUTHOR]

In this paper, a new fault injection approach to measure SEU sensitivity in COTS microprocessors is presented. It consists in a hardware-implemented module that performs fault injection through the available JTAG-based On-Chip Debugger (OCD). This approach can be applied to most microprocessors, since JTAG standard is a widely supported interface and OCDs are usually available in current microprocessors. Hardware implementation avoids the communication between the target system and the software debugging tool, increasing significantly the fault injection efficiency. The method has been applied to a complex microprocessor (ARM). Experimental results demonstrate the approach is a fast, efficient, and cost-effective solution. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]