Now the OpenACC has become a popular programming interface for many-core application programming.Internationally,a lot of research have been done on OpenACC for CPU+GPU heterogeneous many-core architecture.Among them,...Now the OpenACC has become a popular programming interface for many-core application programming.Internationally,a lot of research have been done on OpenACC for CPU+GPU heterogeneous many-core architecture.Among them,the PGI OpenACC compiler developed by NVIDIA is the most advanced one.But there are few research on OpenACC related to the Home Grown Heterogeneous Many-Core(HGHM)Architecture that is different from GPU.This paper proposes an automatic mapping technique for OpenACC kernel code based on the OpenACC compiler to a heterogeneous and deeply fused many-core architecture.Our approach uses the static analysis and feedback dynamic analysis of the compiler to perform the automatic mapping of the program parallel kernel code to many-core devices,and it greatly improves the transformation quality of the compiler.Experimental results show that this technique can greatly improve the efficiency of using OpenACC to port applications to heterogeneous and fused many-core system without impacting program acceleration performance.展开更多
The timely and rapid mapping of rapeseed planting areas is desirable for national food security. Most current rapeseed mapping methods depend strongly on images with good observations obtained during the flowering sta...The timely and rapid mapping of rapeseed planting areas is desirable for national food security. Most current rapeseed mapping methods depend strongly on images with good observations obtained during the flowering stages. Although vegetation indices have been proposed to identify the rapeseed flowering stage in some areas, automatically mapping rapeseed planting areas in large regions is still challenging.We developed an automatic phenology-and pixel-based algorithm(APPA) by integrating Landsat 8 and Sentinel-1 satellite data. We found that the Normalized Rapeseed Flowering Index shows unique spectral characteristics during the flowering and post-flowering periods, which distinguish rapeseed parcels from other land-use types(urban, water, forest, grass, maize, wheat, barley, and soybean). To verify the robustness of APPA, we applied APPA to seven areas in five rapeseed-producing countries with flowering images unavailable. The rapeseed maps by APPA showed consistently high accuracies with producer accuracies of 0.87–0.93 and F-scores of 0.92–0.95 based on 4503 verification samples. They showed high spatial consistency at the pixel level with the land cover Scientific Expertise Centres(SEC) map in France,Crop Map of England in United Kingdom, national-scale crop-and land-cover map of Germany, and Annual Crop Inventory in Canada at the pixel level. We propose APPA as a highly promising method for automatically and efficiently mapping rapeseed areas.展开更多
基金supported by the National Key RD Program of China(Grant no.2017YFB02-02004)the Project of manned space engineering technology(2018-14)+1 种基金“Large-scale parallel computation of aerodynamic problems of irregular spacecraft reentry covering various flow regimes”the National Natural Science Foundation of China(91530319).
文摘Now the OpenACC has become a popular programming interface for many-core application programming.Internationally,a lot of research have been done on OpenACC for CPU+GPU heterogeneous many-core architecture.Among them,the PGI OpenACC compiler developed by NVIDIA is the most advanced one.But there are few research on OpenACC related to the Home Grown Heterogeneous Many-Core(HGHM)Architecture that is different from GPU.This paper proposes an automatic mapping technique for OpenACC kernel code based on the OpenACC compiler to a heterogeneous and deeply fused many-core architecture.Our approach uses the static analysis and feedback dynamic analysis of the compiler to perform the automatic mapping of the program parallel kernel code to many-core devices,and it greatly improves the transformation quality of the compiler.Experimental results show that this technique can greatly improve the efficiency of using OpenACC to port applications to heterogeneous and fused many-core system without impacting program acceleration performance.
基金funded by the National Natural Science Foundation of China (42061144003)。
文摘The timely and rapid mapping of rapeseed planting areas is desirable for national food security. Most current rapeseed mapping methods depend strongly on images with good observations obtained during the flowering stages. Although vegetation indices have been proposed to identify the rapeseed flowering stage in some areas, automatically mapping rapeseed planting areas in large regions is still challenging.We developed an automatic phenology-and pixel-based algorithm(APPA) by integrating Landsat 8 and Sentinel-1 satellite data. We found that the Normalized Rapeseed Flowering Index shows unique spectral characteristics during the flowering and post-flowering periods, which distinguish rapeseed parcels from other land-use types(urban, water, forest, grass, maize, wheat, barley, and soybean). To verify the robustness of APPA, we applied APPA to seven areas in five rapeseed-producing countries with flowering images unavailable. The rapeseed maps by APPA showed consistently high accuracies with producer accuracies of 0.87–0.93 and F-scores of 0.92–0.95 based on 4503 verification samples. They showed high spatial consistency at the pixel level with the land cover Scientific Expertise Centres(SEC) map in France,Crop Map of England in United Kingdom, national-scale crop-and land-cover map of Germany, and Annual Crop Inventory in Canada at the pixel level. We propose APPA as a highly promising method for automatically and efficiently mapping rapeseed areas.
