Algorithm-Directed Crash Consistence in Non-Volatile Memory for HPC

Fault tolerance is one of the major design goals for HPC. The emergence of non-volatile memories (NVM) provides a solution to build fault tolerant HPC. Data in NVM-based main memory are not lost when the system crashes because of the non-volatility nature of NVM. However, because of volatile caches, data must be logged and explicitly flushed from caches into NVM to ensure consistence and correctness before crashes, which can cause large runtime overhead. In this paper, we introduce an algorithm-based method to establish crash consistence in NVM for HPC applications. We slightly extend application data structures or sparsely flush cache blocks, which introduce ignorable runtime overhead. Such extension or cache flushing allows us to use algorithm knowledge to \textit{reason} data consistence or correct inconsistent data when the application crashes. We demonstrate the effectiveness of our method for three algorithms, including an iterative solver, dense matrix multiplication, and Monte-Carlo simulation. Based on comprehensive performance evaluation on a variety of test environments, we demonstrate that our approach has very small runtime overhead (at most 8.2\% and less than 3\% in most cases), much smaller than that of traditional checkpoint, while having the same or less recomputation cost.Comment: 12 page

Matching Users' Preference Under Target Revenue Constraints in Optimal Data Recommendation Systems

This paper focuses on the problem of finding a particular data recommendation strategy based on the user preferences and a system expected revenue. To this end, we formulate this problem as an optimization by designing the recommendation mechanism as close to the user behavior as possible with a certain revenue constraint. In fact, the optimal recommendation distribution is the one that is the closest to the utility distribution in the sense of relative entropy and satisfies expected revenue. We show that the optimal recommendation distribution follows the same form as the message importance measure (MIM) if the target revenue is reasonable, i.e., neither too small nor too large. Therefore, the optimal recommendation distribution can be regarded as the normalized MIM, where the parameter, called importance coefficient, presents the concern of the system and switches the attention of the system over data sets with different occurring probability. By adjusting the importance coefficient, our MIM based framework of data recommendation can then be applied to system with various system requirements and data distributions.Therefore,the obtained results illustrate the physical meaning of MIM from the data recommendation perspective and validate the rationality of MIM in one aspect.Comment: 36 pages, 6 figure

Deep Neighbor Layer Aggregation for Lightweight Self-Supervised Monocular Depth Estimation

With the frequent use of self-supervised monocular depth estimation in robotics and autonomous driving, the model's efficiency is becoming increasingly important. Most current approaches apply much larger and more complex networks to improve the precision of depth estimation. Some researchers incorporated Transformer into self-supervised monocular depth estimation to achieve better performance. However, this method leads to high parameters and high computation. We present a fully convolutional depth estimation network using contextual feature fusion. Compared to UNet++ and HRNet, we use high-resolution and low-resolution features to reserve information on small targets and fast-moving objects instead of long-range fusion. We further promote depth estimation results employing lightweight channel attention based on convolution in the decoder stage. Our method reduces the parameters without sacrificing accuracy. Experiments on the KITTI benchmark show that our method can get better results than many large models, such as Monodepth2, with only 30 parameters. The source code is available at https://github.com/boyagesmile/DNA-Depth

Detecting the gravitational wave memory effect with TianQin

The gravitational wave memory effect is a prediction of general relativity. The presence of memory effect in gravitational wave signals not only provides the chance to test an important aspect of general relativity, but also represents a potentially non-negligible contribution to the waveform for certain gravitational wave events. In this paper, we study the prospect of detecting the gravitational wave memory effect directly with the planned space-based gravitational wave detector -- TianQin. We find that during its 5 years operation, for the gravitational wave signals that could be detected by TianQin, about $0.5\sim2.0$ signals may contain displacement memory effect with signal-to-noise ratios (SNRs) greater than 3. This suggests that the chance for TianQin to detect the displacement memory effect directly is low but not fully negligible. In contrast, the chance to detect the spin memory is negligible. We also study that in which parameter space, the memory effect is expected to be significant in waveform modeling.Comment: 14 pages, 9 figure

Model-free adaptive nonlinear control of the absorption refrigeration system

This work is supported by the National Natural Science Foundation of China (Grant Nos. 61773282, 61873181, and 61922062).Peer reviewedPostprin

Theoretical model and characteristics analysis of deflector-jet servo valve’s pilot stage

To analyze the deflector-jet servo valve’s internal flow characteristics, a theoretical model of the complicated flow distribution in the deflector plate is established based on the offset jet attachment theory. When the deflector plate offsets, jet attachment parameters are attained to figure out the jet’s bending and colliding process. On this basis, an analytical method of acquiring the pilot valve’s pressure gain is derived. According to an actual pilot stage’s structure, pressure gain calculations are carried out. Meanwhile, the pilot valve’s mesh model is established for numerical simulation in order to examine the accuracy of the theoretical model. Calculation and numerical simulation show that the final oil jet is not sensitive to the deflector plate’s movement, which directly reveals the pressure stabilizing effect of the V-shaped structure on the deflector plate. Moreover, the experiment on the pressure gain is accomplished and experiment results verify the accuracy of the analytical calculation