• ETRI Journal
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• v.37 no.1
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• pp.157-164
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• 2015

As GPU power consumption in smartphones increases with more advanced graphic performance, it becomes essential to estimate GPU power consumption accurately. The conventional GPU power model assumes, simply, that a GPU consumes constant power when turned on; however, this is no longer true for recent smartphone GPUs. In this paper, we propose an accurate GPU power model for smartphones, considering newly adopted dynamic voltage and frequency scaling. For the proposed GPU power model, our evaluation results show that the error rate for system power estimation is as low as 2.9%, on average, and 4.6% in the worst case.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3918-3934
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• 2017

This paper presents a viewport resolution scaling technique to reduce power consumption in mobile graphic processing units (GPUs). This technique controls the rendering resolution of applications in proportion to the resolution factor. In the mobile environment, it is essential to find an effective resolution factor to achieve low power consumption because both the resolution and power consumption of a GPU are in mutual trade-off. This paper presents a resolution factor that can minimize image quality degradation and gain power reduction. For this purpose, software and hardware viewport resolution scaling techniques are applied in the Android environment. Then, the correlation between image quality and power consumption is analyzed according to the resolution factor by conducting a benchmark analysis in the real commercial environment. Experimental results show that the power consumption decreased by 36.96% on average by the hardware viewport resolution scaling technique.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.181-191
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• 2018

Rapid development of information technology makes our environment become smarter and massive high performance computers are providing powerful computing for that. Graphics Processing Unit (GPU) as a typical high performance component is being widely used for both graphics and general-purpose applications. Although it can greatly improve computing power, it also delivers significant power consumption and need sufficient power supplies. To make high performance computing more sustainable, the important step is to measure it. Current power technologies for GPU have some drawbacks, such as they are not applicable for power estimation at the early stage. In this article, we present a novel power technology to correlate power consumption and the characteristics at the programmer perspective, and then to estimate power consumption of source program without prerunning. We conduct experiments on Nvidia's GT740 platform; the results show that our power model is more accurately than regression model and has an average error of 2.34% and the maximum error of 9.65%.

• Journal of the Semiconductor & Display Technology
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• v.18 no.1
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• pp.123-128
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• 2019

Currently mobile services are on the verge of full commercialization ahead of 5G mobile communication (5G). The first goal could be to preempt the 5G market through realistic media services utilizing VR (Virtual Reality) and AR (Augmented Reality) technologies that users can most easily experience. Basically this movement is based on the advanced development of smart devices and high quality graphics processing computing power of mobile application processors. Accordingly, the importance of mobile GPUs is emerging and the most concern issue becomes a model for predicting the power and performance for smooth operation of high quality mobile contents. In many cases, the performance of mobile GPUs has been introduced in terms of power consumption of mobile GPUs using dynamic voltage and frequency scaling and throttling functions for power consumption and heat management. This paper introduces several studies of mobile GPU performance prediction model with user-friendly methods not like conventional power centric performance prediction models.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1279-1286
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• 2021

The electrocardiogram (ECG) signal is a good indicator for early diagnosis of heart abnormalities. The ECG signal has a different reference normal signal for each person. And it requires lots of data to diagnosis. In this paper, we propose an adaptive OpenCL-based FPGA-GPU hybrid-layer platform to efficiently accelerate ECG signal diagnosis. As a result of diagnosing 19870 number of ECG signals of MIT-BIH arrhythmia database on the platform, the FPGA accelerator takes 1.15s, that the execution time was reduced by 89.94% and the power consumption was reduced by 84.0% compared to the software execution. The GPU accelerator takes 1.87s, that the execution time was reduced by 83.56% and the power consumption was reduced by 62.3% compared to the software execution. Although the proposed FPGA-GPU hybrid platform has a slower diagnostic speed than the FPGA accelerator, it can operate a flexible algorithm according to the situation by using the GPU.

• Journal of the Korea Society for Simulation
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• v.27 no.3
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• pp.1-11
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• 2018

Discrete event system specification (DEVS) has been used in many simulations including hybrid systems featuring both discrete and continuous behavior that require a lot of time to get results. Therefore, in this study, we proposed the acceleration of a DEVS-based hybrid system simulation using multi-cores and GPUs tightly coupled computing. We analyzed the proposed heterogeneous computing of the simulation in terms of the configuration of the target device, changing simulation parameters, and power consumption for efficient simulation. The result revealed that the proposed architecture offers an advantage for high-performance simulation in terms of execution time, although more power consumption is required. With these results, we discovered that our approach is applicable in hybrid system simulation, and we demonstrated the possibility of optimized hardware distribution in terms of power consumption versus execution time via experiments in the proposed architecture.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06a
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• pp.203-205
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• 2012

최근 컴퓨터 시스템에서는 동작 주파수 증가에 따른 전력 소모량과 높은 온도문제로 인해 CPU의 성능에만 의존할 수는 없는 상황이다. 이에 따라 GPU 병렬처리 연산능력을 CPU의 범용 데이터 처리에 이용하는 기술에 대한 관심이 높아지고 있다. 하지만 CPU와 GPU의 모든 자원을 활용하기에는 이에 따른 높은 온도와 전력 상승이 문제가 된다. 따라서 본 논문에서는 GPU의 전력효율과 성능 측면에서 최적이 되는 동작 주파수에 대한 분석을 수행하고자 한다. GPU를 활용하는 API인 CUDA를 이용하여 GPU의 동작 주파수 변화에 따른 성능 변화, 전력 변화 그리고 Energy Delay에 대해서 분석한다. 실험을 통한 분석 결과 동작 주파수의 증가에 따라 성능은 최대 30%이상 증가했고, 전력소모량은 최대 약18%의 증가를 보여주었다. 또한 Energy Delay도 최대 21% 향상되는 것을 확인할 수 있었다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.141-147
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• 2020

We studied for GPU based signal processor for naval multi-function radar. We implemented processing software both DSP and GPU, and compared computation performances and power consumption. As a result, computation performance was enhanced from 1.2 to 4.1 times compared with a DSP result. From the results, GPU can alternating DSP based signal processor for common radar processor even though Naval Multi Function Radar.

• KIISE Transactions on Computing Practices
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• v.22 no.7
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• pp.303-309
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• 2016

Recently, an increasing number of embedded processors such as ARM Mali begin to support GPGPU programming frameworks, such as OpenCL. Thus, GPGPU technologies that have been used in PC and server environments are beginning to be applied to the embedded systems. However, many embedded systems have different architectural characteristics compare to traditional PCs and low-power consumption and real-time performance are also important performance metrics in these systems. In this paper, we implement a parallel AES cryptographic algorithm for a modern embedded GPU using OpenCL, a standard parallel computing framework, and compare performance against various baselines. Experimental results show that the parallel GPU AES implementation can reduce the response time by about 1/150 and the energy consumption by approximately 1/290 compare to OpenMP implementation when 1000KB input data is applied. Furthermore, an additional 100 % performance improvement of the parallel AES algorithm was achieved by exploiting the characteristics of embedded GPUs such as removing copying data between GPU and host memory. Our results also demonstrate that higher performance improvement can be achieved with larger size of input data.

• Journal of the Korea Society of Computer and Information
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• v.23 no.10
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• pp.1-9
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• 2018

Modern graphics processing units (GPUs) have become one of the most attractive platforms in exploiting high thread level parallelism with the support of new programming tools such as CUDA and OpenCL. Recent GPUs has applied cache hierarchy to support irregular memory access patterns; however, L1 data cache (L1D) exhibits poor efficiency in the GPU. This paper shows that the L1D does not always positively affect the applications in terms of performance and energy efficiency for the GPU. The performance of the GPU is even harmed by using the L1D for lots of applications. Our proposed technique exploits the characteristics of the currently-executed applications to predict the performance impact of the L1D on the GPU and then decides whether to continuously use the cache for the application or not. Our experimental results show that the proposed technique improves the GPU performance by 9.4% and saves up to 52.1% of the power consumption in the L1D.