oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation

With the rapid development of deep learning models and hardware support for dense computing, the deep learning workload characteristics changed significantly from a few hot spots on compute-intensive operations to a broad range of operations scattered across the models. Accelerating a few compute-intensive operations using the expert-tuned implementation of primitives does not fully exploit the performance potential of AI hardware. Various efforts have been made to compile a full deep neural network (DNN) graph. One of the biggest challenges is to achieve high-performance tensor compilation by generating expert level performance code for the dense compute-intensive operations and applying compilation optimization at the scope of DNN computation graph across multiple compute-intensive operations. We present oneDNN Graph Compiler, a tensor compiler that employs a hybrid approach of using techniques from both compiler optimization and expert-tuned kernels for high performance code generation of the deep neural network graph. oneDNN Graph Compiler addresses unique optimization challenges in the deep learning domain, such as low-precision computation, aggressive fusion of graph operations, optimization for static tensor shapes and memory layout, constant weight optimization, and memory buffer reuse. Experimental results demonstrate significant performance gains over existing tensor compiler and primitives library for performance-critical DNN computation graphs and end-to-end models on Intel Xeon Scalable Processors.Comment: 10 pages excluding reference, 9 figures, 1 tabl

Investigations on the pyrolysis of microalgal-bacterial granular sludge: products, kinetics, and potential mechanisms

This study investigated the pyrolysis of microalgal-bacterial granular sludge for producing bio-oil and biochar. Results showed that the bio-oil productivity of pyrolyzed MBGS reached 39.5-45.4 wt%, while 23.8-41.2% for the nitrogen-containing bio-oil at the temperature of 673-1073 K. Meanwhile the biochar with a nitrogen content of 3.7-7.0 wt% could also be produced. Moreover, the Van-Krevelen diagram revealed that produced bio-oil had a H/C ratio higher than that from agroforestry biomass, but its O/C ratio was found to be similar to those of coal and biochar. It further appeared from a mass conservation analysis that the highest bio-oil production yield was achieved at a pyrolysis temperature of 773 K, while the pyrolytic kinetics of MBGS in the temperature range studied was governed by the 3-D diffusion mechanism with the activation energy of 224.96 kJ·mol-1

Cadmium-effect on performance and symbiotic relationship of microalgal-bacterial granules

So far, the microalgal-bacterial granular sludge process has attracted growing interest as an emerging wastewater treatment technology. Cadmium ion (Cd2+) commonly found in wastewater is toxic to microorganisms, thus its effect on microalgal-bacterial granules was investigated in this study. Results showed that Cd2+ at the concentration above 1 mg/L could compromise the performances of microalgal-bacterial granules. The removal efficiency of chemical oxygen demand decreased from about 70% in the control to 42.2% and 25.0% after 30-day operation at the respective Cd2+ concentrations of 5 and 10 mg/L, while the ammonia-nitrogen removal also declined from 70.4% to 30.5% with the increase of the Cd2+ concentration from 1 to 10 mg/L, indicating that nitrifying bacteria were susceptive to the presence of Cd2+. It was further revealed that Cd2+ could stimulate the production of extracellular polymeric substances, e.g. 190.19 ± 7.04 mg/g VSS in the presence of 10 mg/L of Cd2+ versus 100.26 ± 3.82 mg/g VSS in the control after 10-day operation. More importantly, about 84.1%–94.8% of Cd2+ was found to bind to the extracellular proteins in microalgal-bacterial granules at the Cd2+ concentrations studied. In addition, Chlorococcum and Cyanobacteria in microalgal-bacteria granules were withered in the presence of 10 mg/L of Cd2+, suggesting uncoupled symbiosis between microalgae and bacteria induced by Cd2+. Consequently, this study showed that Cd2+ could negatively impact on the microbial structures and metabolisms of microalgal-bacterial granular sludge, leading to a compromised process performance in terms of organic and nitrogen removal.Shulian Wang is grateful to the financial support from National Natural Science Foundation of China (51909082)

RNA sequencing and anthocyanin synthesis-related genes expression analyses in white-fruited Vaccinium uliginosum

Abstract Background Vaccinium uliginosum (Ericaceae) is an important wild berry having high economic value. The white-fruited V. uliginosum variety found in the wild lacks anthocyanin and bears silvery white fruits. Hence, it is a good resource for investigating the mechanism of fruit color development. This study aimed to verify the differences in the expression levels of some structural genes and transcription factors affecting the anthocyanin biosynthesis pathway by conducting high-throughput transcriptome sequencing and real-time PCR analysis by using the ripening fruits of V. uliginosum and the white-fruited variety. Results We annotated 42,837 unigenes. Of the 325 differentially expressed genes, 41 were up-regulated and 284 were down-regulated. Further, 11 structural genes of the flavonoid pathway were up-regulated, whereas two were down-regulated. Of the seven genes encoding transcription factors, five were up-regulated and two were down-regulated. The structural genes VuCHS, VuF3’H, VuFHT, VuDFR, VuANS, VuANR, and VuUFGT and the transcription factors VubHLH92, VuMYB6, VuMYBPA1, VuMYB11, and VuMYB12 were significantly down-regulated. However, the expression of only VuMYB6 and VuMYBPA1 rapidly increased during the last two stages of V. uliginosum when the fruit was ripening, consistent with anthocyanin accumulation. Conclusions VuMYB6 was annotated as MYB1 by the BLAST tool. Thus, the white fruit color in the V. uliginosum variant can be attributed to the down-regulation of transcription factors VuMYB1 and VuMYBPA1, which leads to the down-regulation of structural genes associated with the anthocyanin synthesis pathway

Pipeline Leakage Detection and Localization Using a Reliable Pipeline-Mechanism Model Incorporating a Bayesian Model Updating Approach

Pipeline transportation is widely used in industrial production and daily life. In order to reduce the waste of resources and economic losses caused by pipeline leakage, effective pipeline leakage detection and localization technology is particularly important. Among the many leakage detection methods, the model-based method for pipeline leakage detection and localization is widely used. However, the key to the method is how to obtain an accurate and reliable pipeline model to ensure and improve the detection accuracy. This paper proposes a novel method to obtain a reliable pipeline-mechanism model that fused data and mechanism models based on Bayesian theory. Moreover, in the process of Bayesian fusion, the complexity and calculations in the mechanism models were greatly reduced by establishing a surrogate model. After that, the multidimensional posterior distribution was sampled by the Markov chain Monte Carlo-differential evolution adaptive metropolis (ZS) (MCMC-DREAM (ZS)) algorithm, and the uncertainty in the model was updated to obtain a reliable pipeline-mechanism model. Subsequently, the pipeline resistance coefficient, which could be calculated based on the reliable pipeline-mechanism model, was proposed as an indicator for detecting whether the pipeline leaked or not. Finally, the pipeline leak model was used to determine the location of the leak. The reliable pipeline-mechanism model was applied in an experimental device to validate its performance. The results showed that the proposed method improved the accuracy and reliability of the mechanism model, and, in addition, the leakage could be accurately located

Uptake, accumulation, and degradation of dibutyl phthalate by three wetland plants

The uptake and degradation mechanisms of dibutyl phthalate (DBP) by three wetland plants, namely Lythrum salicaria, Thalia dealbata, and Canna indica, were studied using hydroponics. The results revealed that exposure to DBP at 0.5 mg/L had no significant effect on the growth of L. salicaria and C. indica but inhibited the growth of T. dealbata. After 28 days, DBP concentrations in the roots of L. salicaria, T. dealbata, and C. indica were 8.74, 5.67, and 5.46 mg/kg, respectively, compared to 2.03–3.95 mg/kg in stems and leaves. Mono-n-butyl phthalate concentrations in L. salicaria tissues were significantly higher than those in the other two plants at 23.1, 15.0, and 13.6 mg/kg in roots, stems, and leaves, respectively. The roots of L. salicaria also had the highest concentration of phthalic acid, reaching 2.45 mg/kg. Carboxylesterase, polyphenol oxidase, and superoxide dismutase may be the primary enzymes involved in DBP degradation in wetland plants. The activities of these three enzymes exhibited significant changes in plant tissues. The findings suggest L. salicaria as a potent plant for phytoremediation and use in constructed wetlands for the treatment of DBP-contaminated wastewater. HIGHLIGHTS The uptake and degradation mechanisms of DBP by three common wetland plants were investigated by the hydroponic experiment.; The uptake and degradation capacities of DBP were higher in L. salicaria, which could well resist the oxidative damage caused by DBP and degrade it under the effect of enzymes.; L. salicaria can be used as a potential plant for DBP removal in phytoremediation and the constructed wetland.

Pipeline Leakage Detection and Localization Using a Reliable Pipeline-Mechanism Model Incorporating a Bayesian Model Updating Approach

Pipeline transportation is widely used in industrial production and daily life. In order to reduce the waste of resources and economic losses caused by pipeline leakage, effective pipeline leakage detection and localization technology is particularly important. Among the many leakage detection methods, the model-based method for pipeline leakage detection and localization is widely used. However, the key to the method is how to obtain an accurate and reliable pipeline model to ensure and improve the detection accuracy. This paper proposes a novel method to obtain a reliable pipeline-mechanism model that fused data and mechanism models based on Bayesian theory. Moreover, in the process of Bayesian fusion, the complexity and calculations in the mechanism models were greatly reduced by establishing a surrogate model. After that, the multidimensional posterior distribution was sampled by the Markov chain Monte Carlo-differential evolution adaptive metropolis (ZS) (MCMC-DREAM (ZS)) algorithm, and the uncertainty in the model was updated to obtain a reliable pipeline-mechanism model. Subsequently, the pipeline resistance coefficient, which could be calculated based on the reliable pipeline-mechanism model, was proposed as an indicator for detecting whether the pipeline leaked or not. Finally, the pipeline leak model was used to determine the location of the leak. The reliable pipeline-mechanism model was applied in an experimental device to validate its performance. The results showed that the proposed method improved the accuracy and reliability of the mechanism model, and, in addition, the leakage could be accurately located