Graph databases have gained widespread adoption in various industries and have been utilized in a range of applications,including financial risk assessment,commodity recommendation,and data lineage tracking.While the ...Graph databases have gained widespread adoption in various industries and have been utilized in a range of applications,including financial risk assessment,commodity recommendation,and data lineage tracking.While the principles and design of these databases have been the subject of some investigation,there remains a lack of comprehensive examination of aspects such as storage layout,query language,and deployment.The present study focuses on the design and implementation of graph storage layout,with a particular emphasis on tree-structured key-value stores.We also examine different design choices in the graph storage layer and present our findings through the development of TuGraph,a highly efficient single-machine graph database that significantly outperforms well-known Graph DataBase Management System(GDBMS).Additionally,TuGraph demonstrates superior performance in the Linked Data Benchmark Council(LDBC)Social Network Benchmark(SNB)interactive benchmark.展开更多
ttigh-performance computing （HPC） is essential for both traditional and emerging scientific fields, enabling scientific activities to make progress. With the development of high-performance computing, it is foreseea...ttigh-performance computing （HPC） is essential for both traditional and emerging scientific fields, enabling scientific activities to make progress. With the development of high-performance computing, it is foreseeable that exascale computing will be put into practice around 2020. As Moore＇s law approaches its limit, high-perfornlance computing will face severe challenges when moving from exaseale to zettascale, making tile next 10 years after 2020 a vital period to develop key HPC techniques. In this study, we discuss the challenges of enabling zettascale computing with respect to both hardware and software. We then present a perspective of fllture HPC technology evolution and revolution, leading to our main recommendations in support of zettaseale computing in the coming future.展开更多
Reducing the energy consumption of the storage systems disk read/write requests plays an important role in improving the overall energy efficiency of high-performance computing systems.We propose a method to reduce di...Reducing the energy consumption of the storage systems disk read/write requests plays an important role in improving the overall energy efficiency of high-performance computing systems.We propose a method to reduce disk energy consumption by delaying the dispatch of disk requests to the end of a time window,which we call time window-based lazy scheduling.We prove that sorting requests within a single time window can reduce the disk energy consumption,and we discuss the relationship between the size of the time window and the disk energy consumption,proving that the energy consumption is highly likely to decrease with increasing window size.To exploit this opportunity,we propose the Lazy Scheduling based Disk Energy Optimization(LSDEO)algorithm,which adopts a feedback method to periodically adjust the size of the time window,and minimizes the local disk energy consumption by sorting disk requests within each time window.We implement the LSDEO algorithm in an OS kernel and conduct both simulations and actual measurements on the algorithm,confirming that increasing the time window increases disk energy savings.When the average request arrival rate is 300 and the threshold of average request response time is 50 ms,LSDEO can yield disk energy savings of 21.5%.展开更多
文摘Graph databases have gained widespread adoption in various industries and have been utilized in a range of applications,including financial risk assessment,commodity recommendation,and data lineage tracking.While the principles and design of these databases have been the subject of some investigation,there remains a lack of comprehensive examination of aspects such as storage layout,query language,and deployment.The present study focuses on the design and implementation of graph storage layout,with a particular emphasis on tree-structured key-value stores.We also examine different design choices in the graph storage layer and present our findings through the development of TuGraph,a highly efficient single-machine graph database that significantly outperforms well-known Graph DataBase Management System(GDBMS).Additionally,TuGraph demonstrates superior performance in the Linked Data Benchmark Council(LDBC)Social Network Benchmark(SNB)interactive benchmark.
基金Project supported by the National Key Technology R&D Program of China(No.2016YFB0200401)
文摘ttigh-performance computing （HPC） is essential for both traditional and emerging scientific fields, enabling scientific activities to make progress. With the development of high-performance computing, it is foreseeable that exascale computing will be put into practice around 2020. As Moore＇s law approaches its limit, high-perfornlance computing will face severe challenges when moving from exaseale to zettascale, making tile next 10 years after 2020 a vital period to develop key HPC techniques. In this study, we discuss the challenges of enabling zettascale computing with respect to both hardware and software. We then present a perspective of fllture HPC technology evolution and revolution, leading to our main recommendations in support of zettaseale computing in the coming future.
基金supported by the National Key Research and Development Program of China (No. 2017YFB0202201)
文摘Reducing the energy consumption of the storage systems disk read/write requests plays an important role in improving the overall energy efficiency of high-performance computing systems.We propose a method to reduce disk energy consumption by delaying the dispatch of disk requests to the end of a time window,which we call time window-based lazy scheduling.We prove that sorting requests within a single time window can reduce the disk energy consumption,and we discuss the relationship between the size of the time window and the disk energy consumption,proving that the energy consumption is highly likely to decrease with increasing window size.To exploit this opportunity,we propose the Lazy Scheduling based Disk Energy Optimization(LSDEO)algorithm,which adopts a feedback method to periodically adjust the size of the time window,and minimizes the local disk energy consumption by sorting disk requests within each time window.We implement the LSDEO algorithm in an OS kernel and conduct both simulations and actual measurements on the algorithm,confirming that increasing the time window increases disk energy savings.When the average request arrival rate is 300 and the threshold of average request response time is 50 ms,LSDEO can yield disk energy savings of 21.5%.
