Performance is one of the key problems in either high performance computing or GRID application. Performance data must be collected and analyzed for co-allocating resource efficiently, obtaining high performance and f...Performance is one of the key problems in either high performance computing or GRID application. Performance data must be collected and analyzed for co-allocating resource efficiently, obtaining high performance and fault toleration. Furthermore, with the development of Internet and GRID, the exchange of data between virtual organiztions is becoming more and more important, and the type of performance is increasing following the increasing of the resource type, which requires a proper representation of the performance data. This paper does some research on the collection, analysis and representation of the performance data, and presents a Grid-oriented performance tool prototype: THGPT, which can achieve the runtime performance data, describe the data in XML, and implement a browser-based visualization tool of performance data analysis.展开更多
The increasingly frequent exchange of performance data in grid systems across heterogeneous platforms requires a uniform 搑epresentation?of various types of performance data. This paper reviews the current related res...The increasingly frequent exchange of performance data in grid systems across heterogeneous platforms requires a uniform 搑epresentation?of various types of performance data. This paper reviews the current related research, considers the defect of existing methods, and proposes a new portable description method: grid performance data description (GPDD) using an extensible markup language (XML)-based grid performance data representation language (XGPDRL). GPDD describes the abstract structure, which has excellent extensibility (all types of performance data can be described in one format), efficiency, and flexibil-ity. XGPDRL defines the grammar of the GPDD performance data representation, and is both extensible and portable. For benchmarking purposes, performance data can be collected during runtime, represented in XGPDRL, and analyzed visually using a browser across heterogeneous platforms. GPDD and XGPDRL can conveniently ensure data comprehension across various platforms, and are very suitable for grid per-formance data representation.展开更多
文摘Performance is one of the key problems in either high performance computing or GRID application. Performance data must be collected and analyzed for co-allocating resource efficiently, obtaining high performance and fault toleration. Furthermore, with the development of Internet and GRID, the exchange of data between virtual organiztions is becoming more and more important, and the type of performance is increasing following the increasing of the resource type, which requires a proper representation of the performance data. This paper does some research on the collection, analysis and representation of the performance data, and presents a Grid-oriented performance tool prototype: THGPT, which can achieve the runtime performance data, describe the data in XML, and implement a browser-based visualization tool of performance data analysis.
基金the National High-Tech Research and Devel-opment (863) Program of China (No. 2002AA104230)
文摘The increasingly frequent exchange of performance data in grid systems across heterogeneous platforms requires a uniform 搑epresentation?of various types of performance data. This paper reviews the current related research, considers the defect of existing methods, and proposes a new portable description method: grid performance data description (GPDD) using an extensible markup language (XML)-based grid performance data representation language (XGPDRL). GPDD describes the abstract structure, which has excellent extensibility (all types of performance data can be described in one format), efficiency, and flexibil-ity. XGPDRL defines the grammar of the GPDD performance data representation, and is both extensible and portable. For benchmarking purposes, performance data can be collected during runtime, represented in XGPDRL, and analyzed visually using a browser across heterogeneous platforms. GPDD and XGPDRL can conveniently ensure data comprehension across various platforms, and are very suitable for grid per-formance data representation.
