Comparison and Analysis of GPU Energy Efficiency for CUDA and OpenCL

1. Comparison and Analysis of GPU Energy Efficiency for CUDA and OpenCL By Joe Jackson

2. Terms • Platforms • NVIDIA’s CUDA • Apple’s OpenCL • Hardware • CPU – Central Processing Unit • GPU – Graphics Processing Unit • FPGA – Field Programmable Gate Array • PCIe – Peripheral Component Interconnect Express • Parallel Computing – Carrying out many computations simultaneously.

3. Green Computing • “The study and practice of designing, manufacturing, using, and disposing of computers, servers, and associated sub-systems… efficiently and effectively with minimal or no impact on the environment” (Gupta, 234). • Motivations • Environmental Protection • Rising Energy Costs • Higher Demand for Energy • Energy efficiency is quickly becoming a high priority factor in computing design.

4. Previous Work • Previous focus was primarily large-scale • Li and Zhou suggest that a comprehensive model for single computers be created. • Kang, etal. indicated that GPUs have been found to be more energy efficient than CPUs for computationally intensive workloads. • Huang, etal. have shown that GPU performance, energy consumption, and energy efficiency are highly synchronized. • There has been no previous work towards the comparison of hardware computing platforms.

5. Hypothesis • Due to OpenCL’s emphasis on portability, compared to CUDA’s NVIDIA GPU specialization, we believed that CUDA would be more energy efficient than OpenCL. • The developers of CUDA and OpenCL had different design focuses. • CUDA’s developers were able to design the platform for their own GPUs. • OpenCL’s developers designed the platform to interface with CPUs, GPUs, and FPGAs regardless of developer. • We compared the two with Large Matrix Multiplication.

6. Methods • Equipment • NVIDIA GeForce 9800 GT Graphics Card • PCIe Riser Cable • PCIe 6pin Extension Cable • Current Sensors and Multimeters • To facilitate sensor/multimeter readings from the motherboard to the graphics card, 12V wires of the riser cable were cut and soldered together. • We compare the two platforms via matrix multiplication.

7. Methods Cont. • Though the riser cable has 3V wires, we tested them and found them to be unused. • The same method was used for the 6pin extension cable. • Current sensors were used in conjunction with VernierLoggerPro software. • Host programs and kernels for computing the product of two 6144x6144 matrices were created.

8. Results • During computation, multimeters set to read voltage registered 11.6V from the power supply and 11.65V from the motherboard. • On average, CUDA drew .11A/s less than OpenCL. • CUDA’s average power consumption was 421.4W, while OpenCL’s was 434.8W.

9. Current

10. Power Consumption

11. Results Cont. • With a 13.4W difference between the two platforms over a single computation and variances of 2.3W and 3.5W for OpenCL and CUDA respectively, one iteration of the matrix multiplication was sufficient. • The graphics card pulls over 3x as much power from the computer’s power supply than it does from the motherboard. • CUDA had a slightly larger range between it’s best and worst runs (6.9W) than OpenCL (4.5W).

12. Analysis • Despite slightly more erratic results, CUDA consistently outperformed OpenCL. • CUDA energy consumption: 196.637 kWh • OpenCL energy consumption: 201.337 kWh • The U.S. national average price of energy is $0.129 per kWh, so using CUDA rather than OpenCL saves $0.606 every hour.

13. Future Work • Compare results with newer hardware/software • Expand work to other parallelizable functions • Investigate effects of system temperature

14. References • S. Gupta. Computing with Green Responsibility. In Proceedings of the International Conference and Workshop on Emerging Trends in Technology, ICWET ‘10, pages 234-236, 2010. • S. Huang, S. Xiao, and W. Feng. On the Energy Efficiency of Graphics Processing Units for Scientific Computing. In Proceedings of the 2009 IEEE International Symposium on Parallel & Distributed Processing, IPDPS ‘09, 2009. • SeungGu Kang, Hong Jun Choi, Cheol Hong Kim, Sung Woo Chung, DongSeop Kwon, and JoongChae Na. Exploration of CPU/GPU Co-execution: From the Perspective of Performance, Energy, and Temperature. In Proceedings of the 2011 ACM Symposium on Research in Applied Computation, RACS ‘11, pages 38-43, 2011. • Qilin Li and Mingtian Zhou. The Survey and Future Evolution of Green Computing. In Proceedings of the 2011 IEEE/ACM International Conference on Green Computing and Communications, GREENCOM ’11, pages 230-233, 2011. • "Average Energy Prices in the Los Angeles Area." U.S. Bureau of Labor Statistics. U.S. Bureau of Labor Statistics, 28 Mar. 2013. Web. 15 Apr. 2013.