Based on the study of parse wood materials,the fitting empirical equation of tree growth was obtained,a function with tree growth as a variable and time as an independent variable.Through mathematical operations such ...Based on the study of parse wood materials,the fitting empirical equation of tree growth was obtained,a function with tree growth as a variable and time as an independent variable.Through mathematical operations such as function derivation,the mature age of tree growth was obtained,and the obtained expected mature age of Saliz matsudana was 21a.And the application,research directions and precautions of the mature ages were proposed.展开更多
The growing trend of network virtualization results in a widespread adoption of virtual switches in virtualized environments. However, virtual switching is confronted with great performance challenges regarding packet...The growing trend of network virtualization results in a widespread adoption of virtual switches in virtualized environments. However, virtual switching is confronted with great performance challenges regarding packet classification especially in Open Flow-based software defined networks. This paper first takes an insight into packet classification in virtual Open Flow switching, and points out that its performance bottleneck is dominated by flow table traversals of multiple failed mask probing for each arrived packet. Then we are motivated to propose an efficient packet classification algorithm based on counting bloom filters. In particular, counting bloom filters are applied to predict the failures of flow table lookups with great possibilities, and bypass flow table traversals for failed mask probing. Finally, our proposed packet classification algorithm is evaluated with real network traffic traces by experiments. The experimental results indicate that our proposed algorithm outperforms the classical one in Open v Switch in terms of average search length, and contributes to promote virtual Open Flow switching performance.展开更多
The packet generator （pktgen） is a fundamental module of the majority of soft- ware testers used to benchmark network pro- tocols and functions. The high performance of the pktgen is an important feature of Future I...The packet generator （pktgen） is a fundamental module of the majority of soft- ware testers used to benchmark network pro- tocols and functions. The high performance of the pktgen is an important feature of Future Internet Testbeds, and DPDK is a network packet accelerated platform, so we can use DPDK to improve performance. Meanwhile, green computing is advocated for in the fu- ture of the internet. Most existing efforts have contributed to improving either performance or accuracy. We, however, shifted the focus to energy-efficiency. We find that high per- formance comes at the cost of high energy consumption. Therefore, we started from a widely used high performance schema, deeply studying the multi-core platform, especially in terms of parallelism, core allocation, and fre- quency controlling. On this basis, we proposed an AFfinity-oriented Fine-grained CONtrolling （AFFCON） mechanism in order to improve energy efficiency with desirable performance. As clearly demonstrated through a series of evaluative experiments, our proposal can reduce CPU power consumption by up to 11% while maintaining throughput at the line rate.展开更多
Based on the study of parse wood materials,the fitting empirical equation of tree growth was obtained,a function with tree growth as a variable and time as an independent variable.Through mathematical operations such ...Based on the study of parse wood materials,the fitting empirical equation of tree growth was obtained,a function with tree growth as a variable and time as an independent variable.Through mathematical operations such as function derivation,the mature age of tree growth was obtained,and the obtained expected mature age for Larix kaempferi was 46 years.And the application,research directions and precautions of the mature ages were proposed.展开更多
The concept of Cyber-Physical Systems （CPSs）, which combine computation, networking, and physical processes, is considered to be beneficial to smart grid applications. This study presents an integrated simulation en...The concept of Cyber-Physical Systems （CPSs）, which combine computation, networking, and physical processes, is considered to be beneficial to smart grid applications. This study presents an integrated simulation environment to provide a unified platform for the investigation of smart grid applications involving power grid monitoring, communication, and control. In contrast to the existing approaches, this environment allows the network simulator to operate independently, importing its results to the power system simulation. This resolves conflicts between discrete event simulation and continuous simulation. In addition, several data compensation methods are proposed and investigated under different network delay conditions. A case study of wide-area monitoring and control is provided, and the efficiency of the proposed simulation framework has been evaluated based on the experimental results.展开更多
Insulin-like growth factor-1 receptor(IGF-1R) has been made an attractive anticancer target due to its overexpression in cancers.However,targeting it has often produced the disappointing results as the role played by ...Insulin-like growth factor-1 receptor(IGF-1R) has been made an attractive anticancer target due to its overexpression in cancers.However,targeting it has often produced the disappointing results as the role played by cross talk with numerous downstream signalings.Here,we report a disobliging IGF-1R signaling which promotes growth of cancer through triggering the E3 ubiquitin ligase MEX3A-mediated degradation of RIG-I.The active β-arrestin-2 scaffolds this disobliging signaling to talk with MEX3A.In response to ligands,IGF-1Rβ activated the basal βarr2 into its active state by phosphorylating the interdomain domain on Tyr64 and Tyr250,opening the middle loop(Leu130-Cys141) to the RING domain of MEX3A through the conformational changes of βarr2.The models of βarr2/IGF-1Rβ and βarr2/MEX3A could interpret the mechanism of the activated-IGF-1R in triggering degradation of RIG-I.The assay of the mutants βarr2Y64Aand βarr2Y250Afurther confirmed the role of these two Tyr residues of the interlobe in mediating the talk between IGF-1Rβ and the RING domain of MEX3A.The truncated-βarr2 and the peptide ATQAIRIF,which mimicked the RING domain of MEX3A could prevent the formation of βarr2/IGF-1Rβ and βarr2/MEX3A complexes,thus blocking the IGF-1R-triggered RIG-I degradation.Degradation of RIG-I resulted in the suppression of the IFN-I-associated immune cells in the TME due to the blockade of the RIG-I-MAVS-IFN-I pathway.Poly(I:C) could reverse anti-PD-L1 insensitivity by recovery of RIG-I.In summary,we revealed a disobliging IGF-1R signaling by which IGF-1Rβ promoted cancer growth through triggering the MEX3A-mediated degradation of RIG-I.展开更多
A storage system is the core of a computer,and plays an important role in the sustainable development of emerging strategic industries,such as artificial intelligence,big data,cloud computing,and the Internet of Thing...A storage system is the core of a computer,and plays an important role in the sustainable development of emerging strategic industries,such as artificial intelligence,big data,cloud computing,and the Internet of Things.Storage stack access is a major factor restricting the performance of data-intensive systems because of the increasing performance of processors and network devices.展开更多
Detecting genes that affect specific traits(such as human diseases and crop yields)is important for treating complex diseases and improving crop quality.A genome-wide association study(GWAS)provides new insights and d...Detecting genes that affect specific traits(such as human diseases and crop yields)is important for treating complex diseases and improving crop quality.A genome-wide association study(GWAS)provides new insights and directions for understanding complex traits by identifying important single nucleotide polymorphisms.Many GWAS summary statistics data related to various complex traits have been gathered recently.Studies have shown that GWAS risk loci and expression quantitative trait loci(e QTLs)often have a lot of overlaps,which makes gene expression gradually become an important intermediary to reveal the regulatory role of GWAS.In this review,we review three types of gene-trait association detection methods of integrating GWAS summary statistics and e QTLs data,namely colocalization methods,transcriptome-wide association study-oriented approaches,and Mendelian randomization-related methods.At the theoretical level,we discussed the differences,relationships,advantages,and disadvantages of various algorithms in the three kinds of gene-trait association detection methods.To further discuss the performance of various methods,we summarize the significant gene sets that influence highdensity lipoprotein,low-density lipoprotein,total cholesterol,and triglyceride reported in 16 studies.We discuss the performance of various algorithms using the datasets of the four lipid traits.The advantages and limitations of various algorithms are analyzed based on experimental results,and we suggest directions for follow-up studies on detecting gene-trait associations.展开更多
Chromatin interactions create spatial proximity between distal regulatory elements and target genes in the genome,which has an important impact on gene expression,transcriptional regulation,and phenotypic traits.To da...Chromatin interactions create spatial proximity between distal regulatory elements and target genes in the genome,which has an important impact on gene expression,transcriptional regulation,and phenotypic traits.To date,several methods have been developed for predicting gene expression.However,existing methods do not take into consideration the effect of chromatin interactions on target gene expression,thus potentially reducing the accuracy of gene expression prediction and mining of important regulatory elements.In this study,we developed a highly accurate deep learning-based gene expression prediction model(DeepCBA)based on maize chromatin interaction data.Compared with existing models,DeepCBA exhibits higher accuracy in expression classification and expression value prediction.The average Pearson correlation coefficients(PCCs)for predicting gene expression using gene promoter proximal interactions,proximaldistal interactions,and both proximal and distal interactions were 0.818,0.625,and 0.929,respectively,representing an increase of 0.357,0.16,and 0.469 over the PCCs obtained with traditional methods that use only gene proximal sequences.Some important motifs were identified through DeepCBA;they were enriched in open chromatin regions and expression quantitative trait loci and showed clear tissue specificity.Importantly,experimental results for the maize flowering-related gene ZmRap2.7 and the tillering-related gene ZmTb1 demonstrated the feasibility of DeepCBA for exploration of regulatory elements that affect gene expression.Moreover,promoter editing and verification of two reported genes(ZmCLE7 and ZmVTE4)demonstrated the utility of DeepCBA for the precise design of gene expression and even for future intelligent breeding.DeepCBA is available at http://www.deepcba.com/or http://124.220.197.196/.展开更多
1 Introduction A related study called community search,whose target is to find dense subgraphs containing the given node,has drawn a growing amount of attention recently[1].To explore the higher-order structure of com...1 Introduction A related study called community search,whose target is to find dense subgraphs containing the given node,has drawn a growing amount of attention recently[1].To explore the higher-order structure of complex networks,truss-based community search methods[2]have been proposed.Nevertheless,the truss-based hypergraph constructed from the original graph is frequently fragmented and consists of numerous subgraphs and isolated nodes[3],which boils down to the fact that these methods often pay only attention to the truss connections but ignore the lower-order connectivity of the original graph.展开更多
The well-known insulin-like growth factor 1(IGF1)/IGF-1 receptor(IGF-1R)signaling pathway is overexpressed in many tumors,and is thus an attractive target for cancer treatment.However,results have often been disappoin...The well-known insulin-like growth factor 1(IGF1)/IGF-1 receptor(IGF-1R)signaling pathway is overexpressed in many tumors,and is thus an attractive target for cancer treatment.However,results have often been disappointing due to crosstalk with other signals.Here,we report that IGF-1R signaling stimulates the growth of hepatocellular carcinoma(HCC)cells through the translocation of IGF-1R into the ER to enhance sarco-endoplasmic reticulum calcium ATPase 2(SERCA2)activity.In response to ligand binding,IGF-1Rβis translocated into the ER byβ-arrestin2(β-arr2).Mass spectrometry analysis identified SERCA2 as a target of ER IGF-1Rβ.SERCA2 activity is heavily dependent on the increase in ER IGF-1Rβlevels.ER IGF-1Rβphosphorylates SERCA2 on Tyr^(990)to enhance its activity.Mutation of SERCA2-Tyr^(990)disrupted the interaction of ER IGF-1Rβwith SERCA2,and therefore ER IGF-1Rβfailed to promote SERCA2 activity.The enhancement of SERCA2 activity triggered Ca_(ER)^(2+)perturbation,leading to an increase in autophagy.Thapsigargin blocked the interaction between SERCA2and ER IGF-1Rβand therefore SERCA2 activity,resulting in inhibition of HCC growth.In conclusion,the translocation of IGF-1R into the ER triggers Ca_(ER)^(2+)perturbation by enhancing SERCA2 activity through phosphorylating Tyr^(990)in HCC.展开更多
Achieving faster performance without increasing power and energy consumption for computing systems is an outstanding challenge.This paper develops a novel resource allocation scheme for memory-bound applications runni...Achieving faster performance without increasing power and energy consumption for computing systems is an outstanding challenge.This paper develops a novel resource allocation scheme for memory-bound applications running on High-Performance Computing(HPC)clusters,aiming to improve application performance without breaching peak power constraints and total energy consumption.Our scheme estimates how the number of processor cores and CPU frequency setting affects the application performance.It then uses the estimate to provide additional compute nodes to memory-bound applications if it is profitable to do so.We implement and apply our algorithm to 12 representative benchmarks from the NAS parallel benchmark and HPC Challenge(HPCC)benchmark suites and evaluate it on a representative HPC cluster.Experimental results show that our approach can effectively mitigate memory contention to improve application performance,and it achieves this without significantly increasing the peak power and overall energy consumption.Our approach obtains on average 12.69%performance improvement over the default resource allocation strategy,but uses 7.06%less total power,which translates into 17.77%energy savings.展开更多
文摘Based on the study of parse wood materials,the fitting empirical equation of tree growth was obtained,a function with tree growth as a variable and time as an independent variable.Through mathematical operations such as function derivation,the mature age of tree growth was obtained,and the obtained expected mature age of Saliz matsudana was 21a.And the application,research directions and precautions of the mature ages were proposed.
基金supported by the National Key Research and Development Program of China(2021ZD40303)the National Natural Science Foundation of China(62225205 and 92055213)+1 种基金Natural Science Foundation of Hunan Province of China(2021JJ10023)Shenzhen Basic Research Project(Natural Science Foundation)(JCYJ20210324140002006)。
基金supported in part by National Natural Science Foundation of China(61272148,61572525,61502056,and 61602525)Hunan Provincial Natural Science Foundation of China(2015JJ3010)Scientific Research Fund of Hunan Provincial Education Department(15B009,14C0285)
文摘The growing trend of network virtualization results in a widespread adoption of virtual switches in virtualized environments. However, virtual switching is confronted with great performance challenges regarding packet classification especially in Open Flow-based software defined networks. This paper first takes an insight into packet classification in virtual Open Flow switching, and points out that its performance bottleneck is dominated by flow table traversals of multiple failed mask probing for each arrived packet. Then we are motivated to propose an efficient packet classification algorithm based on counting bloom filters. In particular, counting bloom filters are applied to predict the failures of flow table lookups with great possibilities, and bypass flow table traversals for failed mask probing. Finally, our proposed packet classification algorithm is evaluated with real network traffic traces by experiments. The experimental results indicate that our proposed algorithm outperforms the classical one in Open v Switch in terms of average search length, and contributes to promote virtual Open Flow switching performance.
基金supported by the National Science Foundation of China (No. 61472130, Research on Graphic Processing Units-based High-performance Packet Processing)the China Postdoctoral Science Foundation funded project (No. 61702174)
文摘The packet generator （pktgen） is a fundamental module of the majority of soft- ware testers used to benchmark network pro- tocols and functions. The high performance of the pktgen is an important feature of Future Internet Testbeds, and DPDK is a network packet accelerated platform, so we can use DPDK to improve performance. Meanwhile, green computing is advocated for in the fu- ture of the internet. Most existing efforts have contributed to improving either performance or accuracy. We, however, shifted the focus to energy-efficiency. We find that high per- formance comes at the cost of high energy consumption. Therefore, we started from a widely used high performance schema, deeply studying the multi-core platform, especially in terms of parallelism, core allocation, and fre- quency controlling. On this basis, we proposed an AFfinity-oriented Fine-grained CONtrolling （AFFCON） mechanism in order to improve energy efficiency with desirable performance. As clearly demonstrated through a series of evaluative experiments, our proposal can reduce CPU power consumption by up to 11% while maintaining throughput at the line rate.
文摘Based on the study of parse wood materials,the fitting empirical equation of tree growth was obtained,a function with tree growth as a variable and time as an independent variable.Through mathematical operations such as function derivation,the mature age of tree growth was obtained,and the obtained expected mature age for Larix kaempferi was 46 years.And the application,research directions and precautions of the mature ages were proposed.
基金supported in part by the National Key Basic Research and Development (973) Program of China (Nos. 2013CB228206 and 2011CB302505)the National Natural Science Foundation of China (No. 61233016)2012 State Grid S&T project,Advanced Study of Power Quality-Key Technologies and Applications
文摘The concept of Cyber-Physical Systems （CPSs）, which combine computation, networking, and physical processes, is considered to be beneficial to smart grid applications. This study presents an integrated simulation environment to provide a unified platform for the investigation of smart grid applications involving power grid monitoring, communication, and control. In contrast to the existing approaches, this environment allows the network simulator to operate independently, importing its results to the power system simulation. This resolves conflicts between discrete event simulation and continuous simulation. In addition, several data compensation methods are proposed and investigated under different network delay conditions. A case study of wide-area monitoring and control is provided, and the efficiency of the proposed simulation framework has been evaluated based on the experimental results.
基金supported by Beijing Natural Science Foundation(7222253,China)National Natural Science Foundation of China(81973350/82173841)supported by Beijing Natural Science Foundation(7212149,China)。
文摘Insulin-like growth factor-1 receptor(IGF-1R) has been made an attractive anticancer target due to its overexpression in cancers.However,targeting it has often produced the disappointing results as the role played by cross talk with numerous downstream signalings.Here,we report a disobliging IGF-1R signaling which promotes growth of cancer through triggering the E3 ubiquitin ligase MEX3A-mediated degradation of RIG-I.The active β-arrestin-2 scaffolds this disobliging signaling to talk with MEX3A.In response to ligands,IGF-1Rβ activated the basal βarr2 into its active state by phosphorylating the interdomain domain on Tyr64 and Tyr250,opening the middle loop(Leu130-Cys141) to the RING domain of MEX3A through the conformational changes of βarr2.The models of βarr2/IGF-1Rβ and βarr2/MEX3A could interpret the mechanism of the activated-IGF-1R in triggering degradation of RIG-I.The assay of the mutants βarr2Y64Aand βarr2Y250Afurther confirmed the role of these two Tyr residues of the interlobe in mediating the talk between IGF-1Rβ and the RING domain of MEX3A.The truncated-βarr2 and the peptide ATQAIRIF,which mimicked the RING domain of MEX3A could prevent the formation of βarr2/IGF-1Rβ and βarr2/MEX3A complexes,thus blocking the IGF-1R-triggered RIG-I degradation.Degradation of RIG-I resulted in the suppression of the IFN-I-associated immune cells in the TME due to the blockade of the RIG-I-MAVS-IFN-I pathway.Poly(I:C) could reverse anti-PD-L1 insensitivity by recovery of RIG-I.In summary,we revealed a disobliging IGF-1R signaling by which IGF-1Rβ promoted cancer growth through triggering the MEX3A-mediated degradation of RIG-I.
文摘A storage system is the core of a computer,and plays an important role in the sustainable development of emerging strategic industries,such as artificial intelligence,big data,cloud computing,and the Internet of Things.Storage stack access is a major factor restricting the performance of data-intensive systems because of the increasing performance of processors and network devices.
基金supported by the National Key Research and Development Program of China(2022YFD1201504)the Fundamental Research Funds for the Central Universities(2662022YLYJ010,2021ZKPY018,2662021JC008,SZYJY2021003)+2 种基金the Major Science and Technology Project of Hubei Province(2021AFB002)the Major Project of Hubei Hongshan Laboratory(2022HSZD031)the Yingzi Tech&Huazhong Agricultural University Intelligent Research Institute of Food Health(IRIFH202209)。
文摘Detecting genes that affect specific traits(such as human diseases and crop yields)is important for treating complex diseases and improving crop quality.A genome-wide association study(GWAS)provides new insights and directions for understanding complex traits by identifying important single nucleotide polymorphisms.Many GWAS summary statistics data related to various complex traits have been gathered recently.Studies have shown that GWAS risk loci and expression quantitative trait loci(e QTLs)often have a lot of overlaps,which makes gene expression gradually become an important intermediary to reveal the regulatory role of GWAS.In this review,we review three types of gene-trait association detection methods of integrating GWAS summary statistics and e QTLs data,namely colocalization methods,transcriptome-wide association study-oriented approaches,and Mendelian randomization-related methods.At the theoretical level,we discussed the differences,relationships,advantages,and disadvantages of various algorithms in the three kinds of gene-trait association detection methods.To further discuss the performance of various methods,we summarize the significant gene sets that influence highdensity lipoprotein,low-density lipoprotein,total cholesterol,and triglyceride reported in 16 studies.We discuss the performance of various algorithms using the datasets of the four lipid traits.The advantages and limitations of various algorithms are analyzed based on experimental results,and we suggest directions for follow-up studies on detecting gene-trait associations.
基金supported by the Biological Breeding-Major Projects(2023ZD04076)the National Key Research and Development Program of China(2022YFD1201504)+3 种基金the Fundamental Research Funds for the Central Universities(2662022YLYJ010,2021ZKPY018,2662021JC008,and SZYJY2021003)the Major Project of Hubei Hongshan Laboratory(2022HSZD031)the Major Science and Technology Project of Hubei Province(2021AFB002)the Yingzi Tech&Huazhong Agricultural University Intelligent Research Institute of Food Health(IRIFH202209).
文摘Chromatin interactions create spatial proximity between distal regulatory elements and target genes in the genome,which has an important impact on gene expression,transcriptional regulation,and phenotypic traits.To date,several methods have been developed for predicting gene expression.However,existing methods do not take into consideration the effect of chromatin interactions on target gene expression,thus potentially reducing the accuracy of gene expression prediction and mining of important regulatory elements.In this study,we developed a highly accurate deep learning-based gene expression prediction model(DeepCBA)based on maize chromatin interaction data.Compared with existing models,DeepCBA exhibits higher accuracy in expression classification and expression value prediction.The average Pearson correlation coefficients(PCCs)for predicting gene expression using gene promoter proximal interactions,proximaldistal interactions,and both proximal and distal interactions were 0.818,0.625,and 0.929,respectively,representing an increase of 0.357,0.16,and 0.469 over the PCCs obtained with traditional methods that use only gene proximal sequences.Some important motifs were identified through DeepCBA;they were enriched in open chromatin regions and expression quantitative trait loci and showed clear tissue specificity.Importantly,experimental results for the maize flowering-related gene ZmRap2.7 and the tillering-related gene ZmTb1 demonstrated the feasibility of DeepCBA for exploration of regulatory elements that affect gene expression.Moreover,promoter editing and verification of two reported genes(ZmCLE7 and ZmVTE4)demonstrated the utility of DeepCBA for the precise design of gene expression and even for future intelligent breeding.DeepCBA is available at http://www.deepcba.com/or http://124.220.197.196/.
基金supported by the Research Foundation of Education Bureau of Hunan Province of China(Grant Nos.20B625,22B0275)the Changsha Natural Science Foundation(Grant No.kq2202294).
文摘1 Introduction A related study called community search,whose target is to find dense subgraphs containing the given node,has drawn a growing amount of attention recently[1].To explore the higher-order structure of complex networks,truss-based community search methods[2]have been proposed.Nevertheless,the truss-based hypergraph constructed from the original graph is frequently fragmented and consists of numerous subgraphs and isolated nodes[3],which boils down to the fact that these methods often pay only attention to the truss connections but ignore the lower-order connectivity of the original graph.
基金supported by the National Natural Science Foundation of China(81973350,China)supported by the National Natural Science Foundation of China(81872884 and 82173841,China)Beijing Natural Science Foundation(7222253,China)。
文摘The well-known insulin-like growth factor 1(IGF1)/IGF-1 receptor(IGF-1R)signaling pathway is overexpressed in many tumors,and is thus an attractive target for cancer treatment.However,results have often been disappointing due to crosstalk with other signals.Here,we report that IGF-1R signaling stimulates the growth of hepatocellular carcinoma(HCC)cells through the translocation of IGF-1R into the ER to enhance sarco-endoplasmic reticulum calcium ATPase 2(SERCA2)activity.In response to ligand binding,IGF-1Rβis translocated into the ER byβ-arrestin2(β-arr2).Mass spectrometry analysis identified SERCA2 as a target of ER IGF-1Rβ.SERCA2 activity is heavily dependent on the increase in ER IGF-1Rβlevels.ER IGF-1Rβphosphorylates SERCA2 on Tyr^(990)to enhance its activity.Mutation of SERCA2-Tyr^(990)disrupted the interaction of ER IGF-1Rβwith SERCA2,and therefore ER IGF-1Rβfailed to promote SERCA2 activity.The enhancement of SERCA2 activity triggered Ca_(ER)^(2+)perturbation,leading to an increase in autophagy.Thapsigargin blocked the interaction between SERCA2and ER IGF-1Rβand therefore SERCA2 activity,resulting in inhibition of HCC growth.In conclusion,the translocation of IGF-1R into the ER triggers Ca_(ER)^(2+)perturbation by enhancing SERCA2 activity through phosphorylating Tyr^(990)in HCC.
基金supported in part by the Advanced Research Project of China(No.31511010203)the Research Program of NUDT(No.ZK18-03-10)。
文摘Achieving faster performance without increasing power and energy consumption for computing systems is an outstanding challenge.This paper develops a novel resource allocation scheme for memory-bound applications running on High-Performance Computing(HPC)clusters,aiming to improve application performance without breaching peak power constraints and total energy consumption.Our scheme estimates how the number of processor cores and CPU frequency setting affects the application performance.It then uses the estimate to provide additional compute nodes to memory-bound applications if it is profitable to do so.We implement and apply our algorithm to 12 representative benchmarks from the NAS parallel benchmark and HPC Challenge(HPCC)benchmark suites and evaluate it on a representative HPC cluster.Experimental results show that our approach can effectively mitigate memory contention to improve application performance,and it achieves this without significantly increasing the peak power and overall energy consumption.Our approach obtains on average 12.69%performance improvement over the default resource allocation strategy,but uses 7.06%less total power,which translates into 17.77%energy savings.
