RayBench: An Advanced NVIDIA-Centric GPU Rendering Benchmark Suite for Optimal Performance Analysis
This study aims to collect GPU rendering programs and analyze their characteristics to construct a benchmark dataset that reflects the characteristics of GPU rendering programs, providing a reference basis for designing the next generation of graphics processors. The research framework includes four...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/19/4124 |
| _version_ | 1797575973488558080 |
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| author | Peng Wang Zhibin Yu |
| author_facet | Peng Wang Zhibin Yu |
| author_sort | Peng Wang |
| collection | DOAJ |
| description | This study aims to collect GPU rendering programs and analyze their characteristics to construct a benchmark dataset that reflects the characteristics of GPU rendering programs, providing a reference basis for designing the next generation of graphics processors. The research framework includes four parts: GPU rendering program integration, data collection, program analysis, and similarity analysis. In the program integration and data collection phase, 1000 GPU rendering programs were collected from open-source repositories, and 100 representative programs were selected as the initial benchmark dataset. The program analysis phase involves instruction-level, thread-level, and memory-level analysis, as well as five machine learning algorithms for importance ranking. Finally, through Pearson similarity analysis, rendering programs with high similarity were eliminated, and the final GPU rendering program benchmark dataset was selected based on the benchmark’s comprehensiveness and representativeness. The experimental results of this study show that, due to the need to load and process texture and geometry data in rendering programs, the average global memory access efficiency is generally lower compared to the averages of the Rodinia and Parboil benchmarks. The GPU occupancy rate is related to the computationally intensive tasks of rendering programs. The efficiency of stream processor execution and thread bundle execution is influenced by branch statements and conditional judgments. Common operations such as lighting calculations and texture sampling in rendering programs require branch judgments, which reduce the execution efficiency. Bandwidth utilization is improved because rendering programs reduce frequent memory access and data transfer to the main memory through data caching and reuse. Furthermore, this study used multiple machine learning methods to rank the importance of 160 characteristics of 100 rendering programs on four different NVIDIA GPUs. Different methods demonstrate robustness and stability when facing different data distributions and characteristic relationships. By comparing the results of multiple methods, biases inherent to individual methods can be reduced, thus enhancing the reliability of the results. The contribution of this study lies in the analysis of workload characteristics of rendering programs, enabling targeted performance optimization to improve the efficiency and quality of rendering programs. By comprehensively collecting GPU rendering program data and performing characteristic analysis and importance ranking using machine learning methods, reliable reference guidelines are provided for GPU design. This is of significant importance in driving the development of rendering technology. |
| first_indexed | 2024-03-10T21:46:59Z |
| format | Article |
| id | doaj.art-bea278d749a0489aa2416fd2ee2bc686 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T21:46:59Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-bea278d749a0489aa2416fd2ee2bc6862023-11-19T14:17:34ZengMDPI AGElectronics2079-92922023-10-011219412410.3390/electronics12194124RayBench: An Advanced NVIDIA-Centric GPU Rendering Benchmark Suite for Optimal Performance AnalysisPeng Wang0Zhibin Yu1Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaThis study aims to collect GPU rendering programs and analyze their characteristics to construct a benchmark dataset that reflects the characteristics of GPU rendering programs, providing a reference basis for designing the next generation of graphics processors. The research framework includes four parts: GPU rendering program integration, data collection, program analysis, and similarity analysis. In the program integration and data collection phase, 1000 GPU rendering programs were collected from open-source repositories, and 100 representative programs were selected as the initial benchmark dataset. The program analysis phase involves instruction-level, thread-level, and memory-level analysis, as well as five machine learning algorithms for importance ranking. Finally, through Pearson similarity analysis, rendering programs with high similarity were eliminated, and the final GPU rendering program benchmark dataset was selected based on the benchmark’s comprehensiveness and representativeness. The experimental results of this study show that, due to the need to load and process texture and geometry data in rendering programs, the average global memory access efficiency is generally lower compared to the averages of the Rodinia and Parboil benchmarks. The GPU occupancy rate is related to the computationally intensive tasks of rendering programs. The efficiency of stream processor execution and thread bundle execution is influenced by branch statements and conditional judgments. Common operations such as lighting calculations and texture sampling in rendering programs require branch judgments, which reduce the execution efficiency. Bandwidth utilization is improved because rendering programs reduce frequent memory access and data transfer to the main memory through data caching and reuse. Furthermore, this study used multiple machine learning methods to rank the importance of 160 characteristics of 100 rendering programs on four different NVIDIA GPUs. Different methods demonstrate robustness and stability when facing different data distributions and characteristic relationships. By comparing the results of multiple methods, biases inherent to individual methods can be reduced, thus enhancing the reliability of the results. The contribution of this study lies in the analysis of workload characteristics of rendering programs, enabling targeted performance optimization to improve the efficiency and quality of rendering programs. By comprehensively collecting GPU rendering program data and performing characteristic analysis and importance ranking using machine learning methods, reliable reference guidelines are provided for GPU design. This is of significant importance in driving the development of rendering technology.https://www.mdpi.com/2079-9292/12/19/4124GPU rendering programbenchmark suiteperformance optimizationmachine learning |
| spellingShingle | Peng Wang Zhibin Yu RayBench: An Advanced NVIDIA-Centric GPU Rendering Benchmark Suite for Optimal Performance Analysis Electronics GPU rendering program benchmark suite performance optimization machine learning |
| title | RayBench: An Advanced NVIDIA-Centric GPU Rendering Benchmark Suite for Optimal Performance Analysis |
| title_full | RayBench: An Advanced NVIDIA-Centric GPU Rendering Benchmark Suite for Optimal Performance Analysis |
| title_fullStr | RayBench: An Advanced NVIDIA-Centric GPU Rendering Benchmark Suite for Optimal Performance Analysis |
| title_full_unstemmed | RayBench: An Advanced NVIDIA-Centric GPU Rendering Benchmark Suite for Optimal Performance Analysis |
| title_short | RayBench: An Advanced NVIDIA-Centric GPU Rendering Benchmark Suite for Optimal Performance Analysis |
| title_sort | raybench an advanced nvidia centric gpu rendering benchmark suite for optimal performance analysis |
| topic | GPU rendering program benchmark suite performance optimization machine learning |
| url | https://www.mdpi.com/2079-9292/12/19/4124 |
| work_keys_str_mv | AT pengwang raybenchanadvancednvidiacentricgpurenderingbenchmarksuiteforoptimalperformanceanalysis AT zhibinyu raybenchanadvancednvidiacentricgpurenderingbenchmarksuiteforoptimalperformanceanalysis |