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Then, the electrical power can be computed by multiplying current and voltage. In first, a physical measuring device is attached between the power supply and the many-core device. Generally, the energy quantification process relies on two approaches: hardware-based and software-based measurement. This work does not accelerate a HPC algorithm, but it makes a description of performance, power and energy consumption of CPU and GPU computing devices. Historically, GPGPU researching has focused primary on accelerating scientific applications such as physical simulations, medical analysis, image and video processing. However, in this supercomputers list is not possible to isolate the power consumed by the accelerators. For instance, in the June 2018 Green 500 ranking, 7 of 10 top computer systems incorporate Nvidia accelerators. This does not indicate that the GPU has lower energy efficiency since the increasing advantage in performance can offset the larger power consumption. Further, the peak power of latest Nvidia and AMD GPUs is as high as 300W, while a typical CPU consumes only 80W at Thermal Design Power (TDP). Īs a result, power dissipation must be reduced without losing computing performance. Some direct consequences of its higher power consumption are growing dissipation of heat, more complex cooling solutions, and noisier fans. Coming along with these features, the energy consumption of GPU containers like high performance workstations and personal computers became a real problem. These devices have drawn the attention of the HPC research community because they have a great computational power next to a high memory bandwidth and are formidably suited for massively data parallel computation (Single Instruction Multiple Threads applications). Therefore, the number of devices with GPUs and the amount of GPU accelerated applications increased more and more over the past years. The computing scenario has changed substantially since the introduction of accelerators, particularly GPGPU (short for general purpose computing on graphics processing units). Palabras clave: Potencia, Rodinia, GPU, NVML, RAPL. Específicamente, se comparan las versiones secuenciales y multihilo en CPU con implementaciones GPU, caracterizando el tiempo de ejecución, la potencia real instantánea y el consumo promedio de energía, con el objetivo de probar la idea de que las GPU son dispositivos de baja eficiencia energética. Este artículo analiza un conjunto de aplicaciones del benchmark Rodinia en términos de rendimiento y consumo de energía de CPU y GPU. En los últimos años, los coprocesadores GPU se han utilizado frecuentemente para acelerar muchos de estos costosos sistemas, a pesar de que incorporan millones de transistores en sus chips, lo que genera un aumento considerable en los requerimientos de energía. Keywords: Power, Rodinia, GPU, NVML, RAPL.Ĭon el consumo de energía emergiendo como uno de los mayores problemas en el desarrollo de aplicaciones HPC (High Performance Computing), la importancia de trabajos específicos de investigación en este campo se convierte en una prioridad. Idea that GPUs are power-hungry computing devices. Specifically, it compares single-threadedĪnd multi-threaded CPU versions with GPU implementations, and characterize theĮxecution time, true instant power and average energy consumption to test the Of applications from the Rodinia benchmark suite in terms of CPU and GPU Immediate increase on power consumption necessities.
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Though they are embedding millions of transistors on their chips delivering an In the last years, GPU coprocessors haveīeen increasingly used to accelerate many of these high-priced systems even HPC (High Performance Computing) applications, the importance of detailed power-related Energy consumption emerging as one of the biggest issues in the development of