LSM-based key-value stores suffer from sub-optimal performance due to their slow and heavy background compactions. The compaction brings severe CPU and network overhead on high-speed disaggregated storage. This article further reveals that data-intensive compression in compaction consumes a significant portion of CPU power. Moreover, the multi-threaded compactions cause substantial CPU contention and network traffic during high-load periods. Based on the above observations, we propose fine-grained dynamical compaction offloading by leveraging the modern Data Processing Unit (DPU) to alleviate the CPU and network overhead. To achieve this, we first customized a file system to enable efficient data access for DPU. We then leverage the Arm cores on the DPU to meet the burst CPU and network requirements to reduce resource contention and data movement. We further employ dedicated hardware-based accelerators on the DPU to speed up the compression in compactions. We integrate our DPU-offloaded compaction with RocksDB and evaluate it with NVIDIA’s latest Bluefield-2 DPU on a real system. The evaluation shows that the DPU is an effective solution to solve the CPU bottleneck and reduce data traffic of compaction. The results show that compaction performance is accelerated by 2.86 to 4.03 times, system write and read throughput is improved by up to 3.2 times and 1.4 times respectively, and host CPU contention and network traffic are effectively reduced compared to the fine-tuned CPU-only baseline.
20 healthy free-range Guanzhong dairy goats were slaughtered at the age of 0-day,1-month-old,2-month-old,3-month old and 12month-old,to research on the development of digestive parameters and body mass index,anatomical comparison methods was used.The results showed that:①The highest relative daily growth rate was observed in the age of 0 day to 1-month-old,closed to 5%,then decreased gradually,average daily gain peaked at 160g/d from 1-month-old to 2-month-old.②The absolute growth rate of digestive organs increased gradually,while the relative growth rate peaked at different time,the allometric growth rate of digestive organs were different and more rapidly than the whole body.③The color of bile was tawny at birth,then became to blackish green,the pH value was 6.25～6.70.④The pH value of rumen changed from 5.70～6.70,the pH value of abomasum was neutral acidosid at birth,then decreased gradually,with the minimum value of 2.75.The pH value of duodenum ranged from 5.10～5.90,the pH value of jejunum and ileum increased with age,and maintained at neutral after 2-month-old.Compared with the pH value of different parts of intestine at the same age,ileum′s was the highest,jejunum′s was higher than duodenum′s.There was no significant difference in the pH value of different parts of intestine at 0 day(P0.05),but they differ significantly at 1-month-old(P0.05),especially the pH value of duodenum differ significantly with jejunum and ileum at the age of 2-month-old,3-month-old and 12-month-old.
The broad landscape of new technologies currently being explored makes the current times very exciting for computer systems research. The community is actively researching an extensive set of topics, ranging from the small (e.g., energy-independent embedded devices) to the large (e.g., brain-scale deep learning), simultaneously addressing technology discontinuities (End of Moore's Law and EnergyWall), new challenges in security and privacy, and the rise of artificial intelligence (AI).
While industry is applying some of these technologies, its efforts are necessarily focused on only a few areas, and on relatively short-term horizons. This offers academic researchers the opportunity to attack the problems with a broader and longer-term view. Further, in recent times, the computer systems community has started to pay increasing attention to non-performance measures, such as security, complexity, and power. To make progress in this multi-objective world, the composition of research teams needs to change. Teams have to become inter-disciplinary, enabling the flow of ideas across computing fields.
While many research directions are interesting, this report outlines a few high-priority areas where inter-disciplinary research is likely to have a high payoff:
a) Developing the components for a usable planet-scale Internet of Things (IoT), with provably energy-efficient devices. This report envisions a highly-available, geographically distributed, heterogeneous large-scale IoT system with the same efficiency, maintainability, and usability as today's data centers. This planet-scale IoT will be populated by many computationally-sophisticated IoT devices that are ultra-low power and operate energy-independently.
b) Rethinking the hardware-software security contract in the age of AI. In light of the recent security vulnerabilities, this report argues for building hardware abstractions that communicate security guarantees, and for allowing software to communicate its security and privacy requirements to the hardware. Further, security and privacy mechanisms should be integrated into the disruptive emerging technologies that support AI.
c) Making AI a truly dependable technology that is usable by all the citizens in all settings. As AI frameworks automate an increasing number of critical operations, this report argues for end-to-end dependable AI, where both the hardware and the software are understood and verified. Further, AI needs to turn from a centralized tool into a capability easily usable by all the citizens in all settings to meet an ever expanding range of needs.
d) Developing solutions to tackle extreme complexity, possibly based on formal methods. This report argues for the need to tame the explosion of system complexity and heterogeneity by creating new abstractions and complexity-management solutions. Such solutions need to be accessible to domain experts. An important step towards this goal is to scale out and extend formal methods for the real world.
This report also describes other, related research challenges.
Strong self-interference due to the co-located transmitter is the bottleneck for implementing an in-band full-duplex (IBFD) system. If not adequately mitigated, the strong interference can saturate the receiver's analog-digital converters (ADCs) and hence void the digital processing. This paper considers utilizing a reconfigurable intelligent surface (RIS), together with a receiving (Rx) phase shifter network (PSN), to mitigate the strong self-interference through jointly optimizing their phases. This method, named self-interference mitigation using RIS and PSN (SIMRP), can suppress self-interference to avoid ADC saturation effectively and therefore improve the sum rate performance of communication systems, as verified by the simulation studies.
The dual-factor model of mental health has garnered substantial support, positing the necessity of encompassing both negative (e.g., psychological problems) and positive (e.g., well-being) indicators in comprehensive evaluations of people’s mental health. Nonetheless, the nature of the profiles and predictors (such as academic emotions) during four years of university life lack clarity, hampering a profound understanding of mental well-being among university students. This research included 135 items designed to assess an array of depression symptoms, negative emotional experiences, life satisfaction, positive emotional experiences, and academic emotions. First, this research affirmed the applicability of the dual-factor model in the context of Chinese university students (N = 2277) with the utilization of confirmatory factor analysis (CFA). Furthermore, latent profile analysis (LPA) was employed to delineate prevalent constellations of psychological symptoms and subjective well-being within participants. The outcomes unveiled the existence of three distinct clusters: (1) Complete Mental Health, (2) Vulnerable, and (3) Troubled. Third, by employing R3stept on academic emotions and mental health classifications, this study revealed that there were associations between this variable and specific amalgams of psychological symptoms and well-being levels. These findings bear influence on the practice of mental health screening and the identification of individuals necessitating targeted interventions.
During the construction of Jinsha River Wudongde Hydropower Station, we encountered problems affecting the safety and stability of the mountain, such as the development of laminated geological formations, poor integrity of the slope surface rock, and even slippage and large deformation. In order to strengthen the deformation and stability control of the layered rock body of the side slope and ensure the safety of the dam project, we tracked and analyzed the actual measured displacement data. Taking the flood relief tunnel rock body and slope as the main research object, we analyzed the valley deformation and development trend in key areas, and summarized the spatial and temporal distribution law of valley deformation in different areas. At the same time, a three-dimensional geological model is established by numerical simulation method, and the finite difference combined with the reduction method is used to calculate the stability of the slope of the flushing pool. The deformation monitoring and numerical simulation results reveal the deformation characteristics of the valley and the stability of steep slopes in the early stage of water storage, and explain the evolution mechanism of slope deformation based on geological structure characteristics. It is found that: the laminated slopes in the reservoir area of the dam are prone to deformation under the joint action of long-term construction disturbance and fracture water seepage; construction disturbance has a high degree of influence on the artificial excavation area below 1070m in elevation, with the maximum rock deformation and surface displacement reaching 92.2mm and 312.5mm, respectively; however, the mountain above 1070m in elevation is limitedly affected, and the valley deformation of the mountain on the left bank of the reservoir area is higher than that on the right bank, the accumulated deformation on the left and right still does not exceed 20mm; seepage has a more obvious effect on the displacement of the top of the slope, and excavation and other disturbances have a more obvious effect on the displacement of the artificial slope; for the deformation of the valley of the water pad pond behind the dam increases more slowly, the deformation data at the site is basically consistent with the change trend of the numerically calculated data, and generally shows a contraction trend, the maximum contraction of the simulated calculation is close to 20mm, located at an elevation of 990m. On-site slope displacement monitoring, with high precision and real and reliable data, is of great significance for hydropower plant construction and long-term safe operation after completion.
Abstract With an increasing number of antennas and users, the complexity increases dramatically, in order to simplify the process. In this paper, We propose existed algorithms discussing their principles and their advantages as well as the comparison between each method and then we provide the possible improvement in theory changing steps in iteration process to avoid the matrix inversion, by introducing a pre-conditioned gradient (PCG) method and further improved Jacobi method and using Neumann-series terms to estimate an approximate matrix inversion and the Gauss-Seidel (GS) method with a diagonal-approximate initial solution to the method and biconjugate gradient stabilized (BICGSTAB) method and above all of the methods are based on the minimum mean square error (MMSE) detector including the application in multi-user MIMO system. Another approach is from a signal detector based on symmetric successive over relaxation (SSOR) method without matrix inversion and Neumann series based on the zero forcing signal detector. The third algorithm uses a modified version of the conjugate gradient least square (CGLS). These methods are mainly replacing the inversion process into iteration process to reduce the complexity of the algorithm not only replacing the stages but optimizing the speed from setting the initial solution as well as the improvement using the Neumann-series to replace the iteration part. Finally, we proposed a symmetric successive overrelaxation (SSOR) based Gauss-Seidel for massive MIMO detection.
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