Novel computing architectures offer the possibility to execute float point operations with different precisions. The execution of reduced precision operations, when acceptable for certain applications, is likely to reduce both the execution time and the power consumption. However, the application's error rate and the device's reliability can also be impacted by these precision changes. In this paper, we study the impact of data and operation precision changes on the reliability of modern architectures. We consider Xilinx Field-Programmable Gate-Arrays (FPGA), Intel Xeon Phis, and NVIDIA Graphics Processing Units (GPUs) executing a set of codes implemented in double, single, and half-precision IEEE754-compliant float point data. On FPGAs, the reduced area and performance improvements brought by reduced precision operations increase reliability. On Xeon Phis the compiler biases significantly double and single-precision instructions execution. This raises the drawback of increasing single-precision error rates when compared to double-precision operations. NVIDIA GPUs make use of dedicated mixed-precision cores, which draw nontrivial effects on the device reliability. Generically speaking, on GPUs half-precision allows a higher number of executions to be correctly completed before experimenting a failure. Finally, we also evaluate how transient faults impact the output correctness. Our study shows that for most applications faults in a single or half-precision data or operation are more likely to significantly modify the output value than errors in double-precision data.

Graphics processing units (GPUs) are playing a critical role in convolutional neural networks (CNNs) for image detection. As GPU-enabled CNNs move into safety-critical environments, reliability is becoming a growing concern. In this paper, we evaluate and propose strategies to improve the reliability of object detection algorithms, as run on three NVIDIA GPU architectures. We consider three algorithms: 1) you only look once; 2) a faster region-based CNN (Faster R-CNN); and 3) a residual network, exposing live hardware to neutron beams. We complement our beam experiments with fault injection to better characterize fault propagation in CNNs. We show that a single fault occurring in a GPU tends to propagate to multiple active threads, significantly reducing the reliability of a CNN. Moreover, relying on error correcting codes dramatically reduces the number of silent data corruptions (SDCs), but does not reduce the number of critical errors (i.e., errors that could potentially impact safety-critical applications). Based on observations on how faults propagate on GPU architectures, we propose effective strategies to improve CNN reliability. We also consider the benefits of using an algorithm-based fault-tolerance technique for matrix multiplication, which can correct more than 87% of the critical SDCs in a CNN, while redesigning maxpool layers of the CNN to detect up to 98% of critical SDCs.

The increased need for computing capabilities and higher efficiency have stimulated industries to make available in the market novel architectures with increased complexity. The variety of codes that need to be executed combined with the complexity of novel architectures introduces challenges in the reliability evaluation of computing systems and applications. This paper compares the reliability behaviors of six different architectures (an Intel co-processor, three NVIDIA GPUs, an AMD APU, an embedded ARM) executing eight different codes. To support our evaluation, we present and discuss experimental beam data that covers a total of more than 352,000 years of natural exposure and fault-injection analysis based on a total of more than 120,000 injections. We first quantify both the Silent Data Corruptions and the Detected Unrecoverable Errors rates. Then, we qualify observed errors considering the difference between the corrupted and expected values as well as the portion of the output that has been corrupted. From these analyses, we identify the reliability characteristics which are related to the underlying hardware and the intrinsic behaviors of the executed code. Finally, we discuss the implications of the device- and code-dependent reliability behaviors for approximate computing. We analyze the benefits, in term of reduced error rate, of a relaxed output correctness.

In this paper, we evaluate the error criticality of radiation-induced errors on modern High-Performance Computing (HPC) accelerators (Intel Xeon Phi and NVIDIA K40) through a dedicated set of metrics. We show that, as long as imprecise computing is concerned, the simple mismatch detection is not sufficient to evaluate and compare the radiation sensitivity of HPC devices and algorithms. Our analysis quantifies and qualifies radiation effects on applications' output correlating the number of corrupted elements with their spatial locality. Also, we provide the mean relative error (dataset-wise) to evaluate radiation-induced error magnitude. We apply the selected metrics to experimental results obtained in various radiation test campaigns for a total of more than 400 hours of beam time per device. The amount of data we gathered allows us to evaluate the error criticality of a representative set of algorithms from HPC suites. Additionally, based on the characteristics of the tested algorithms, we draw generic reliability conclusions for broader classes of codes. We show that arithmetic operations are less critical for the K40, while Xeon Phi is more reliable when executing particles interactions solved through Finite Difference Methods. Finally, iterative stencil operations seem the most reliable on both architectures.

In this paper we assess and discuss the radiation sensitivity of a set of HPC applications executed on NVIDIA K20 GPGPUs. The occurrence of both radiation-induced silent data corruption and functional interruption will be experimentally addressed for Hotspot, LavaMD, and Matrix Transponse. Each of the tested codes requires a proper computational power and elaborates a different amount of data. Both these characteristics play a significant role in the application radiations sensitivity. Additionally, an evaluation of the error rate at sea level will be provided for all the tested codes.