GPU Shader Analysis and Power Optimization Model

Authors

  • Guruprasad Konnurmath School of Computer Science and Engineering, KLE Technological University, India
  • Satyadhyan Chickerur School of Computer Science and Engineering, KLE Technological University, India
Volume: 14 | Issue: 1 | Pages: 12925-12930 | February 2024 | https://doi.org/10.48084/etasr.6695

Abstract

With the rapid advancements in 3D game technology, workload characterization has become crucial for each new generation of games. The increased complexity of scenes in 3D games allows for stunning real-time visual quality. However, handling such workloads results in significant power consumption over the GPU rendering pipeline. The focus of the current paper is low power optimization, targeting texture memory, geometry engine, pixel, and rasterization, as these components are significant contributors to the power consumption of a typical GPU. The proposed methodology integrates the Dynamic Voltage Frequency Scaling (DVFS) technique, adjusting voltage and frequency based on the workload analysis of frame rates with respect to the scenes of 3D games. Frame rates of 60 fps and 30 fps are set up to understand and manage the workload on frames. Furthermore, for comparative analysis, various frame-level power analysis schemes such as No DVFS implemented, Frame History Method, Frame Signature Method, and Tiled History-based are introduced. The proposed scheme consistently surpasses these frame-level schemes, with fewer missed deadlines, while having the lowest energy consumption per frame rate. The implementation resulted in a remarkable 65% improvement in quality, indicated by a reduction in deadline misses, along with a substantial 60% energy saving.

Keywords:

3D scene rendering pipeline, frames, GPU, DVFS, geometry, pixel, texture, workload

Downloads

Download data is not yet available.

References

"Home - | TOP500." https://top500.org/.

R. Li, A. Arora, S. Li, Q. Wu, and L. K. John, "Hardware-aware 3D Model Workload Selection and Characterization for Graphics and ML Applications," in 2022 23rd International Symposium on Quality Electronic Design (ISQED), Apr. 2022.

A. Mishra and N. Khare, "Analysis of DVFS Techniques for Improving the GPU Energy Efficiency," Open Journal of Energy Efficiency, vol. 4, no. 4, pp. 77–86, Nov. 2015.

J. Guerreiro, A. Ilic, N. Roma, and P. Tomás, "DVFS-aware application classification to improve GPGPUs energy efficiency," Parallel Computing, vol. 83, pp. 93–117, Apr. 2019.

N. C. Kundur, B. C. Anil, P. M. Dhulavvagol, R. Ganiger, and B. Ramadoss, "Pneumonia Detection in Chest X-Rays using Transfer Learning and TPUs," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11878–11883, Oct. 2023.

P. D. Chung, "Smoothing the Power Output of a Wind Turbine Group with a Compensation Strategy of Power Variation," Engineering, Technology & Applied Science Research, vol. 11, no. 4, pp. 7343–7348, Aug. 2021.

J. R. Monfort and M. Grossman, "Scaling of 3D game engine workloads on modern multi-GPU systems," in Proceedings of the Conference on High Performance Graphics 2009, New York, NY, USA, May 2009, pp. 37–46.

G. Konnurmath and S. Chickerur, "Power-Aware Characteristics of Matrix Operations on Multicores," Applied Artificial Intelligence, vol. 35, no. 15, pp. 2102–2123, Dec. 2021.

G. Konnurmath and S. Chickerur, "An Investigation into Power Aware Aspects of Rendering 3D Models on Multi-Core Processors," Procedia Computer Science, vol. 218, pp. 887–898, Jan. 2023.

T. P. Minh et al., "Finite Element Modeling of Shunt Reactors Used in High Voltage Power Systems," Engineering, Technology & Applied Science Research, vol. 11, no. 4, pp. 7411–7416, Aug. 2021.

A. Zhang, K. Chen, H. Johan, and M. Erdt, "High-performance adaptive texture streaming and rendering of large 3D cities," The Visual Computer, vol. 38, no. 4, pp. 1245–1262, Apr. 2022.

D. Geisler, I. Yoon, A. Kabra, H. He, Y. Sanders, and A. Sampson, "Geometry types for graphics programming," Proceedings of the ACM on Programming Languages, vol. 4, no. OOPSLA, Aug. 2020, Art. np./ 173.

A. Mackin, F. Zhang, and D. R. Bull, "A Study of High Frame Rate Video Formats," IEEE Transactions on Multimedia, vol. 21, no. 6, pp. 1499–1512, Jun. 2019.

N. Karpinsky and S. Zhang, "Holovideo: Real-time 3D range video encoding and decoding on GPU," Optics and Lasers in Engineering, vol. 50, no. 2, pp. 280–286, Feb. 2012.

"NVIDIA Nsight Systems," NVIDIA Developer. https://developer.nvidia.com/nsight-systems.

Downloads

How to Cite

[1]
G. Konnurmath and S. Chickerur, “GPU Shader Analysis and Power Optimization Model”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 1, pp. 12925–12930, Feb. 2024.

Metrics

Abstract Views: 220
PDF Downloads: 300

Metrics Information

Most read articles by the same author(s)