For large-scale scientific data output in numerical simulations,volume rendering methods inevitably perform high-density ray sampling to capture complex physical features,resulting in significant computational overhead and data increment.However,on domestic autonomous-CPU supercomputers,owing to the lower computing power of a single processor core compared to that of commercial CPU,more processor cores must be used to share volume rendering tasks;this leads to scalability bottlenecks in the parallel communication of sampling data.Full utilization of domestic autonomous-CPU supercomputers to efficiently complete volume rendering tasks is an urgent problem that needs to be solved.To address this problem,this paper proposes a performance optimization technique for large-scale parallel volume rendering based on multiple rendering pipelines;here,the parallel scale of a rendering pipeline is reduced by two-level parallelism:first,at the pipeline level,and then,at the process level.In large-scale parallel volume rendering after optimization,the rendered goal image is first divided into multiple sub-regions,and all rendering processes are grouped accordingly.Each process group then executes a rendering pipeline independently,and as a result,the corresponding sub-region of the image is produced.Finally,all sub-regions of the image are collected,and the whole image is output.Experiments demonstrate that the optimized volume rendering algorithm can scale to approximately 10 000 processing cores on domestic autonomous-CPU supercomputers and can effectively complete volume rendering tasks.
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