The Experts below are selected from a list of 138 Experts worldwide ranked by ideXlab platform
Zifa Wang - One of the best experts on this subject based on the ideXlab platform.
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gnaqpms v1 1 accelerating the global nested air quality prediction modeling system gnaqpms on intel xeon phi processors
Geoscientific Model Development, 2017Co-Authors: Hui Wang, Huansheng Chen, Xueshun Chen, Xinwei Xie, Rongrong Wang, Xiao Tang, Junmin Lin, Zifa WangAbstract:Abstract. The Global Nested Air Quality Prediction Modeling System (GNAQPMS) is the global Version of the Nested Air Quality Prediction Modeling System (NAQPMS), which is a multi-scale chemical transport model used for air quality forecast and atmospheric environmental research. In this study, we present the porting and optimisation of GNAQPMS on a second-generation Intel Xeon Phi processor, codenamed Knights Landing (KNL). Compared with the first-generation Xeon Phi coprocessor (codenamed Knights Corner, KNC), KNL has many new hardware features such as a bootable processor, high-performance in-package memory and ISA compatibility with Intel Xeon processors. In particular, we describe the five optimisations we applied to the key modules of GNAQPMS, including the CBM-Z gas-phase chemistry, advection, convection and wet deposition modules. These optimisations work well on both the KNL 7250 processor and the Intel Xeon E5-2697 V4 processor. They include (1) updating the pure Message Passing Interface (MPI) parallel mode to the hybrid parallel mode with MPI and OpenMP in the emission, advection, convection and gas-phase chemistry modules; (2) fully employing the 512 bit wide vector processing units (VPUs) on the KNL platform; (3) reducing unnecessary memory access to improve cache efficiency; (4) reducing the thread local storage (TLS) in the CBM-Z gas-phase chemistry module to improve its OpenMP performance; and (5) changing the global communication from writing/reading interface files to MPI functions to improve the performance and the parallel scalability. These optimisations greatly improved the GNAQPMS performance. The same optimisations also work well for the Intel Xeon Broadwell processor, specifically E5-2697 v4. Compared with the Baseline Version of GNAQPMS, the optimised Version was 3.51 × faster on KNL and 2.77 × faster on the CPU. Moreover, the optimised Version ran at 26 % lower average power on KNL than on the CPU. With the combined performance and energy improvement, the KNL platform was 37.5 % more efficient on power consumption compared with the CPU platform. The optimisations also enabled much further parallel scalability on both the CPU cluster and the KNL cluster scaled to 40 CPU nodes and 30 KNL nodes, with a parallel efficiency of 70.4 and 42.2 %, respectively.
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accelerating the global nested air quality prediction modeling system gnaqpms model on intel xeon phi processors
Geoscientific Model Development Discussions, 2017Co-Authors: Hui Wang, Huansheng Chen, Junming Lin, Xueshun Chen, Xinwei Xie, Rongrong Wang, Xiao Tang, Zifa WangAbstract:The GNAQPMS model is the global Version of the Nested Air Quality Prediction Modelling System (NAQPMS), which is a multi-scale chemical transport model used for air quality forecast and atmospheric environmental research. In this study, we present our work of porting and optimizing the GNAQPMS model on the second generation Intel Xeon Phi processor codename “Knights Landing” (KNL). Compared with the first generation Xeon Phi coprocessor, KNL introduced many new hardware features such as a bootable processor, high performance in-package memory and ISA compatibility with Intel Xeon processor. In particular, we described the five optimizations we applied to the key modules of GNAQPMS – CBM-Z gas chemistry, advection, convection and wet deposition. These optimizations work well on both the KNL 7250 processor as well as the Intel Xeon processor E5-2697 V4. They include: 1) updating the pure MPI parallel mode to hybrid parallel mode with MPI and OpenMP in emission, advection, convection and chemistry modules; 2) fully employ the 512-bit wide vector processing units (VPU) on the KNL platform; 3) reducing unnecessary memory access to improve caches efficiency; 4) reducing thread local storage (TLS) in CBM-Z gas phase chemistry module to improve its OpenMP performance; 5) changing global communication from interface-files writing/reading to using Message Passing Interface (MPI) functions to improve the performance and the parallel scalability. These optimizations improved GNAQPMS performance great. The same optimizations also work well for the Intel Xeon Broadwell processor, specifically, E5-2697v4. Compared with the Baseline Version of GNAQPMS, the optimized Version is 3.34x faster on KNL and 2.39x faster on CPU. Furthermore, the optimized Version on KNL runs at 26 % lower average power compare to CPU. Combining the performance and energy improvement, the KNL platform is 47% more efficient compare to the CPU platform. The optimizations also enables much further parallel scalability on both the CPU cluster and KNL cluster – scale to 40 CPU nodes and 30 KNL nodes, with a parallel efficiency of 70.4 % and 42.2 %, respectively.
Hui Wang - One of the best experts on this subject based on the ideXlab platform.
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gnaqpms v1 1 accelerating the global nested air quality prediction modeling system gnaqpms on intel xeon phi processors
Geoscientific Model Development, 2017Co-Authors: Hui Wang, Huansheng Chen, Xueshun Chen, Xinwei Xie, Rongrong Wang, Xiao Tang, Junmin Lin, Zifa WangAbstract:Abstract. The Global Nested Air Quality Prediction Modeling System (GNAQPMS) is the global Version of the Nested Air Quality Prediction Modeling System (NAQPMS), which is a multi-scale chemical transport model used for air quality forecast and atmospheric environmental research. In this study, we present the porting and optimisation of GNAQPMS on a second-generation Intel Xeon Phi processor, codenamed Knights Landing (KNL). Compared with the first-generation Xeon Phi coprocessor (codenamed Knights Corner, KNC), KNL has many new hardware features such as a bootable processor, high-performance in-package memory and ISA compatibility with Intel Xeon processors. In particular, we describe the five optimisations we applied to the key modules of GNAQPMS, including the CBM-Z gas-phase chemistry, advection, convection and wet deposition modules. These optimisations work well on both the KNL 7250 processor and the Intel Xeon E5-2697 V4 processor. They include (1) updating the pure Message Passing Interface (MPI) parallel mode to the hybrid parallel mode with MPI and OpenMP in the emission, advection, convection and gas-phase chemistry modules; (2) fully employing the 512 bit wide vector processing units (VPUs) on the KNL platform; (3) reducing unnecessary memory access to improve cache efficiency; (4) reducing the thread local storage (TLS) in the CBM-Z gas-phase chemistry module to improve its OpenMP performance; and (5) changing the global communication from writing/reading interface files to MPI functions to improve the performance and the parallel scalability. These optimisations greatly improved the GNAQPMS performance. The same optimisations also work well for the Intel Xeon Broadwell processor, specifically E5-2697 v4. Compared with the Baseline Version of GNAQPMS, the optimised Version was 3.51 × faster on KNL and 2.77 × faster on the CPU. Moreover, the optimised Version ran at 26 % lower average power on KNL than on the CPU. With the combined performance and energy improvement, the KNL platform was 37.5 % more efficient on power consumption compared with the CPU platform. The optimisations also enabled much further parallel scalability on both the CPU cluster and the KNL cluster scaled to 40 CPU nodes and 30 KNL nodes, with a parallel efficiency of 70.4 and 42.2 %, respectively.
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accelerating the global nested air quality prediction modeling system gnaqpms model on intel xeon phi processors
Geoscientific Model Development Discussions, 2017Co-Authors: Hui Wang, Huansheng Chen, Junming Lin, Xueshun Chen, Xinwei Xie, Rongrong Wang, Xiao Tang, Zifa WangAbstract:The GNAQPMS model is the global Version of the Nested Air Quality Prediction Modelling System (NAQPMS), which is a multi-scale chemical transport model used for air quality forecast and atmospheric environmental research. In this study, we present our work of porting and optimizing the GNAQPMS model on the second generation Intel Xeon Phi processor codename “Knights Landing” (KNL). Compared with the first generation Xeon Phi coprocessor, KNL introduced many new hardware features such as a bootable processor, high performance in-package memory and ISA compatibility with Intel Xeon processor. In particular, we described the five optimizations we applied to the key modules of GNAQPMS – CBM-Z gas chemistry, advection, convection and wet deposition. These optimizations work well on both the KNL 7250 processor as well as the Intel Xeon processor E5-2697 V4. They include: 1) updating the pure MPI parallel mode to hybrid parallel mode with MPI and OpenMP in emission, advection, convection and chemistry modules; 2) fully employ the 512-bit wide vector processing units (VPU) on the KNL platform; 3) reducing unnecessary memory access to improve caches efficiency; 4) reducing thread local storage (TLS) in CBM-Z gas phase chemistry module to improve its OpenMP performance; 5) changing global communication from interface-files writing/reading to using Message Passing Interface (MPI) functions to improve the performance and the parallel scalability. These optimizations improved GNAQPMS performance great. The same optimizations also work well for the Intel Xeon Broadwell processor, specifically, E5-2697v4. Compared with the Baseline Version of GNAQPMS, the optimized Version is 3.34x faster on KNL and 2.39x faster on CPU. Furthermore, the optimized Version on KNL runs at 26 % lower average power compare to CPU. Combining the performance and energy improvement, the KNL platform is 47% more efficient compare to the CPU platform. The optimizations also enables much further parallel scalability on both the CPU cluster and KNL cluster – scale to 40 CPU nodes and 30 KNL nodes, with a parallel efficiency of 70.4 % and 42.2 %, respectively.
F. Prindle - One of the best experts on this subject based on the ideXlab platform.
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POSIX Delta Document for the Next-Generation Computer Resources (NGCR) Operating Systems Interface Standard Baseline (Version 5).
1995Co-Authors: F. PrindleAbstract:Abstract : The objective of the Next-Generation Computer Resources (NGCR) Program is to standardize computer and computer component interfaces for the Navy's next generation of mission critical computing systems. The NGCR Operating Systems Standards Working Group (OSSWG) will either select a set of interface standards from commercial sources or jointly develop such standards with industry. Previously, the OSSWG established operating systems interface requirements for these standards. In this document, the OSSWG evaluates how effectively its established requirements are met by the Portable Operating System Interface (POSIX) standards, which have been selected as the Baseline for the NGCR Program.
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POSIX Delta Document for the Next-Generation Computer Resources (NGCR) Operating Systems Interface Standard Baseline (Version 4)
1994Co-Authors: F. PrindleAbstract:Abstract : The objective of the Next-Generation Computer Resources (NGCR) program is to standardize computer and computer component interfaces for the Navy's next generation of mission-critical computing systems. The NGCR Operating Systems Standards Working Group (OSSWG) will either select a set of interface standards from commercial sources or jointly develop such standards with industry. Previously, the OSSWG established operating systems interface requirements for these standards. In this document, the OSSWG evaluates how effectively its established requirements are met by the Portable Operating System Interface (POSIX) standards, which have been selected as the Baseline for the NGCR Program. Computer operating systems, Operating system interface standards.
Huansheng Chen - One of the best experts on this subject based on the ideXlab platform.
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gnaqpms v1 1 accelerating the global nested air quality prediction modeling system gnaqpms on intel xeon phi processors
Geoscientific Model Development, 2017Co-Authors: Hui Wang, Huansheng Chen, Xueshun Chen, Xinwei Xie, Rongrong Wang, Xiao Tang, Junmin Lin, Zifa WangAbstract:Abstract. The Global Nested Air Quality Prediction Modeling System (GNAQPMS) is the global Version of the Nested Air Quality Prediction Modeling System (NAQPMS), which is a multi-scale chemical transport model used for air quality forecast and atmospheric environmental research. In this study, we present the porting and optimisation of GNAQPMS on a second-generation Intel Xeon Phi processor, codenamed Knights Landing (KNL). Compared with the first-generation Xeon Phi coprocessor (codenamed Knights Corner, KNC), KNL has many new hardware features such as a bootable processor, high-performance in-package memory and ISA compatibility with Intel Xeon processors. In particular, we describe the five optimisations we applied to the key modules of GNAQPMS, including the CBM-Z gas-phase chemistry, advection, convection and wet deposition modules. These optimisations work well on both the KNL 7250 processor and the Intel Xeon E5-2697 V4 processor. They include (1) updating the pure Message Passing Interface (MPI) parallel mode to the hybrid parallel mode with MPI and OpenMP in the emission, advection, convection and gas-phase chemistry modules; (2) fully employing the 512 bit wide vector processing units (VPUs) on the KNL platform; (3) reducing unnecessary memory access to improve cache efficiency; (4) reducing the thread local storage (TLS) in the CBM-Z gas-phase chemistry module to improve its OpenMP performance; and (5) changing the global communication from writing/reading interface files to MPI functions to improve the performance and the parallel scalability. These optimisations greatly improved the GNAQPMS performance. The same optimisations also work well for the Intel Xeon Broadwell processor, specifically E5-2697 v4. Compared with the Baseline Version of GNAQPMS, the optimised Version was 3.51 × faster on KNL and 2.77 × faster on the CPU. Moreover, the optimised Version ran at 26 % lower average power on KNL than on the CPU. With the combined performance and energy improvement, the KNL platform was 37.5 % more efficient on power consumption compared with the CPU platform. The optimisations also enabled much further parallel scalability on both the CPU cluster and the KNL cluster scaled to 40 CPU nodes and 30 KNL nodes, with a parallel efficiency of 70.4 and 42.2 %, respectively.
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accelerating the global nested air quality prediction modeling system gnaqpms model on intel xeon phi processors
Geoscientific Model Development Discussions, 2017Co-Authors: Hui Wang, Huansheng Chen, Junming Lin, Xueshun Chen, Xinwei Xie, Rongrong Wang, Xiao Tang, Zifa WangAbstract:The GNAQPMS model is the global Version of the Nested Air Quality Prediction Modelling System (NAQPMS), which is a multi-scale chemical transport model used for air quality forecast and atmospheric environmental research. In this study, we present our work of porting and optimizing the GNAQPMS model on the second generation Intel Xeon Phi processor codename “Knights Landing” (KNL). Compared with the first generation Xeon Phi coprocessor, KNL introduced many new hardware features such as a bootable processor, high performance in-package memory and ISA compatibility with Intel Xeon processor. In particular, we described the five optimizations we applied to the key modules of GNAQPMS – CBM-Z gas chemistry, advection, convection and wet deposition. These optimizations work well on both the KNL 7250 processor as well as the Intel Xeon processor E5-2697 V4. They include: 1) updating the pure MPI parallel mode to hybrid parallel mode with MPI and OpenMP in emission, advection, convection and chemistry modules; 2) fully employ the 512-bit wide vector processing units (VPU) on the KNL platform; 3) reducing unnecessary memory access to improve caches efficiency; 4) reducing thread local storage (TLS) in CBM-Z gas phase chemistry module to improve its OpenMP performance; 5) changing global communication from interface-files writing/reading to using Message Passing Interface (MPI) functions to improve the performance and the parallel scalability. These optimizations improved GNAQPMS performance great. The same optimizations also work well for the Intel Xeon Broadwell processor, specifically, E5-2697v4. Compared with the Baseline Version of GNAQPMS, the optimized Version is 3.34x faster on KNL and 2.39x faster on CPU. Furthermore, the optimized Version on KNL runs at 26 % lower average power compare to CPU. Combining the performance and energy improvement, the KNL platform is 47% more efficient compare to the CPU platform. The optimizations also enables much further parallel scalability on both the CPU cluster and KNL cluster – scale to 40 CPU nodes and 30 KNL nodes, with a parallel efficiency of 70.4 % and 42.2 %, respectively.
Xinwei Xie - One of the best experts on this subject based on the ideXlab platform.
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gnaqpms v1 1 accelerating the global nested air quality prediction modeling system gnaqpms on intel xeon phi processors
Geoscientific Model Development, 2017Co-Authors: Hui Wang, Huansheng Chen, Xueshun Chen, Xinwei Xie, Rongrong Wang, Xiao Tang, Junmin Lin, Zifa WangAbstract:Abstract. The Global Nested Air Quality Prediction Modeling System (GNAQPMS) is the global Version of the Nested Air Quality Prediction Modeling System (NAQPMS), which is a multi-scale chemical transport model used for air quality forecast and atmospheric environmental research. In this study, we present the porting and optimisation of GNAQPMS on a second-generation Intel Xeon Phi processor, codenamed Knights Landing (KNL). Compared with the first-generation Xeon Phi coprocessor (codenamed Knights Corner, KNC), KNL has many new hardware features such as a bootable processor, high-performance in-package memory and ISA compatibility with Intel Xeon processors. In particular, we describe the five optimisations we applied to the key modules of GNAQPMS, including the CBM-Z gas-phase chemistry, advection, convection and wet deposition modules. These optimisations work well on both the KNL 7250 processor and the Intel Xeon E5-2697 V4 processor. They include (1) updating the pure Message Passing Interface (MPI) parallel mode to the hybrid parallel mode with MPI and OpenMP in the emission, advection, convection and gas-phase chemistry modules; (2) fully employing the 512 bit wide vector processing units (VPUs) on the KNL platform; (3) reducing unnecessary memory access to improve cache efficiency; (4) reducing the thread local storage (TLS) in the CBM-Z gas-phase chemistry module to improve its OpenMP performance; and (5) changing the global communication from writing/reading interface files to MPI functions to improve the performance and the parallel scalability. These optimisations greatly improved the GNAQPMS performance. The same optimisations also work well for the Intel Xeon Broadwell processor, specifically E5-2697 v4. Compared with the Baseline Version of GNAQPMS, the optimised Version was 3.51 × faster on KNL and 2.77 × faster on the CPU. Moreover, the optimised Version ran at 26 % lower average power on KNL than on the CPU. With the combined performance and energy improvement, the KNL platform was 37.5 % more efficient on power consumption compared with the CPU platform. The optimisations also enabled much further parallel scalability on both the CPU cluster and the KNL cluster scaled to 40 CPU nodes and 30 KNL nodes, with a parallel efficiency of 70.4 and 42.2 %, respectively.
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accelerating the global nested air quality prediction modeling system gnaqpms model on intel xeon phi processors
Geoscientific Model Development Discussions, 2017Co-Authors: Hui Wang, Huansheng Chen, Junming Lin, Xueshun Chen, Xinwei Xie, Rongrong Wang, Xiao Tang, Zifa WangAbstract:The GNAQPMS model is the global Version of the Nested Air Quality Prediction Modelling System (NAQPMS), which is a multi-scale chemical transport model used for air quality forecast and atmospheric environmental research. In this study, we present our work of porting and optimizing the GNAQPMS model on the second generation Intel Xeon Phi processor codename “Knights Landing” (KNL). Compared with the first generation Xeon Phi coprocessor, KNL introduced many new hardware features such as a bootable processor, high performance in-package memory and ISA compatibility with Intel Xeon processor. In particular, we described the five optimizations we applied to the key modules of GNAQPMS – CBM-Z gas chemistry, advection, convection and wet deposition. These optimizations work well on both the KNL 7250 processor as well as the Intel Xeon processor E5-2697 V4. They include: 1) updating the pure MPI parallel mode to hybrid parallel mode with MPI and OpenMP in emission, advection, convection and chemistry modules; 2) fully employ the 512-bit wide vector processing units (VPU) on the KNL platform; 3) reducing unnecessary memory access to improve caches efficiency; 4) reducing thread local storage (TLS) in CBM-Z gas phase chemistry module to improve its OpenMP performance; 5) changing global communication from interface-files writing/reading to using Message Passing Interface (MPI) functions to improve the performance and the parallel scalability. These optimizations improved GNAQPMS performance great. The same optimizations also work well for the Intel Xeon Broadwell processor, specifically, E5-2697v4. Compared with the Baseline Version of GNAQPMS, the optimized Version is 3.34x faster on KNL and 2.39x faster on CPU. Furthermore, the optimized Version on KNL runs at 26 % lower average power compare to CPU. Combining the performance and energy improvement, the KNL platform is 47% more efficient compare to the CPU platform. The optimizations also enables much further parallel scalability on both the CPU cluster and KNL cluster – scale to 40 CPU nodes and 30 KNL nodes, with a parallel efficiency of 70.4 % and 42.2 %, respectively.