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Statistical Power Consumption Analysis and Modeling for GPU Based Computing

By

Xiaohan Ma, Mian Dong, Lin Zhong and Zhigang Deng

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Content

1.GPU2.Statistical Power Consumption Analyzing3.Statistical GPU Power Model4.Evaluation and Validation5.Discussion6.References

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1.GPU

• Graphics Processing Unit• Accelerate scientific and engineering

applications. (Example : 3D Gaming)

Fig1 :NVidia GeForce 8800gt

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Why ?

• More integrated transistors• Rising power consumption• Dissipation of heat , Complex cooling

solution, Nosier fans• Challenge of developing energy efficient code• Analyzing and modeling consumption of

runtime GPUs

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2. Statistical Power Consumption Analyzing

• High level methodology to model• First work that applies statistical analysis to

model the power consumption of a GPU • Using coupling among power consumption

characteristics , run time performance, dynamic workloads

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2.1 How?

• Record power consumption , run time workload signals, performance data

• Build a statistical regression model oAbility to estimate the power consumption of GPU

dynamically o Bridge the dynamic workload of runtime GPUs

their estimated power consumptions • Uses NVidia GeForce 8800gt graphics card

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2.2 Data Acquisition

• Power consumption data

• GPU Workload Signal Recording

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2.2.1 Power Consumption Data Acquisition

• Test Computer Programs designed to test GPUo NVidia GeForce 8800gt graphics card with a 200 Watt power specificationo AMD Athlon 64x2 3.0GHz Dual-Core Processoro 2GB memoryo Corsair TX 750W power supply

• Host Computer Specialized data recording software ,Power acquisition system (FLUKE 2680A)

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2.2.2 GPU Workload Signal Processing

• Record using NVidia PerfKit performance analysis tool simultaneously

- Cable of dynamically extracting 39 GPU workload variables

• Choose 5 major variables- Represent the runtime utilizations of major ` pipeline stages on the GPU

• Record GPU workload signals• Resample GPU workload signals

04/10/2023 10Fig 2: Recorded and Corresponding Resampled Data

Five Major variables

1. vertex_shader_busy (the percentage of time when the vertex shader is busy),2. pixel_shader_busy (the percentage of time when the pixel shader is busy)3. texture_busy (the percentage of time when the texture unit is busy)4. goem_busy (the percentage of time when the geometry shader is busy)5. rop_busy (the percentage of time when the ROP unit is active)

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3. Statistical GPU Power Model

• Assuming,–Processed power consumption data is Y =

{Yt1, Yt2, …….Ytn} (ti denotes the time index)

–Aligned GPU workload data is Xj = {XJ t1 , Xj

t2 ,….., Xj tn } (1 j N, Xj represents jth

GPU workload variable)

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Constructed a statistical multivariable function (model) Yt = F(Xt

1, Xt2,…..Xt

N)

That can robustly and accurately predict the GPU power consumption Yt ,given any GPU workload variables (Xt

1, Xt2,…..Xt

N).

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3.1 Methodology

• 5 major GPU workload variables.• Split the data set into training subset and a

cross validation subset (test data).• Used the training subset to learn a Support

Vector Regression model using LIBSVM.• Compared the cross validation results of the

chosen SVR model with a Simple Least Square Based Linear Regression (SLR) model.

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Graphic Program

GPGPU Jorik benchmark

Fig 3 :Cross Validation Comparison Result

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Open GL Geometry Benchmark 1.0 (Graphic Program)

GPGPU Jorik Benchmark

SLR 656.83 44.523

SVR 589.73 39.427

Sum Square Error Comparison Between SLR and SVR for Cross Validation Data

Table 1 : Sum Square Error Comparison Results between SLR and SVR

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It’s Clear ….!!!!

Regardless of whether graphic computing or GPGPU applications are used chosen SVR model measurably performed better than traditional SLR on the cross validation data(test data ) set .

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4. Evaluation and Validation

• What are the accuracy and the robustness of the proposed statistical model if the GPU runs non bench mark programs ?

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Eight test programs ( 4 graphics programs and 4 GPGPU computing applications) were selected for the testing.

Graphic Program – Nexuiz Xmas Tree HDR

Dual Depth Peeling

Each of the program ran for 100 seconds.

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GPGPU Programs – GNN N-body simulation

Option Pricing Fast Walsh Transform

N-body simulation ran for 20 seconds and other three stopped automatically once they generated outputs.

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Table 2 : Summary of Power Prediction Errors as a percentage of mean GPU Power Consumption

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4.1 Results

Fig 4 : Comparison between the ground truth (blue) and the predicted GPU power consumption data (red) for the chosen four graphics programs

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Fig 5 : Comparison between the ground truth (blue) and the predicted GPU power consumption data (red) for the chosen four GPGPU programs

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5. Discussion

1. This research work studied correlation of power consumption and performance of graphic applications using NVIDIA Perfkit.

2. NVIDIA Perfkit is designed to identify usage of GPU components by conventional graphic applications.

3. It cannot identify GPGPU special events such as Global Memory Access which has the largest factor in power consumption of GPU.

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4. This model completely depends on the recorded workload signals of the runtime GPU. But sometimes it fails to indicate the power consumption of the underlying GPU.

5. Can not accurately model power consumption peaks. (due to some other factors as bus communication, or memory access)

6. It is hard to predict how much training data is sufficient and will be needed in advance.

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5.1 Related Work

• Statistical power modeling of GPU kernels using performance counters – 2010

• Quantifying the impact of GPUs on performance and energy efficiency in HPC clusters – 2010

• Performance and Power Analysis of ATI GPU: A Statistical Approach- 2011

• Tree Structured Analysis on GPU Power Study - 2011

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6. References

• H.Nagasaka, N. Maruyama, A. Nukada, S. Matsuoka, T.Endo, Statistical Power Modeling of GPU Kernels Using Performance Counters, In Proc. of International Conference on Green Computing, p. 115-122, 2010

• J.Chen, B. Li, Y.Shang, L. Peng, J.Pier, Tree Structured Power Analysis on GPU Power Study, In Proc. Of 29th International Conference on Computer Design, p.57-64, 2011

• Y.Zhang, Y.Hu, B.Li, L.Pen, Performance and Power Analysis of ATI GPU : Statistical Approach, In Proc. of 6th IEEE International Conference on NAS, p. 149-158,2011

• R.Suda, D.Q. Ren, Accurate Measurements and Precise Modeling of Power Dissipation of CUDA Kernels toward Power Optimized High Performance CPU-GPU Computing , In Proc. of International Conference on Parallel and Distributed Computing, Application and Development, p. 432-438, 2009

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Thank You