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A first look at integrated GPUs for green high-performance computing

T. Scogland, H. Lin, W. Feng
Dept. of Computer Science, Virginia Tech,2202 Kraft Drive, Knowledge Works II Building, Blackburg, VA 24060, USA
Computer Science – Research and Development, Vol. 25, No. 3. (1 September 2010), pp. 125-134-134.

@article{scogland-first,

   title={A first look at integrated GPUs for green high-performance computing},

   author={Scogland, T.R.W. and Lin, H. and Feng, W.},

   journal={Computer Science-Research and Development},

   pages={1–10},

   issn={1865-2034},

   publisher={Springer}

}

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The graphics processing unit (GPU) has evolved from a single-purpose graphics accelerator to a tool that can greatly accelerate the performance of high-performance computing (HPC) applications. Previous studies have shown that discrete GPUs, while energy efficient for compute-intensive scientific applications, consume very high power. In fact, a compute-capable discrete GPU can draw more than 200 watts by itself, which can be as much as an entire compute node (without a GPU). This massive power draw presents a serious roadblock to the adoption of GPUs in low-power environments, such as embedded systems. Even when being considered for data centers, the power draw of a GPU presents a problem as it increases the demand placed on support infrastructure such as cooling and available supplies of power, driving up cost. With the advent of compute-capable integrated GPUs with power consumption in the tens of watts, we believe it is time to re-evaluate the notion of GPUs being power-hungry. In this paper, we present the first evaluation of the energy efficiency of integrated GPUs for green HPC. We make use of four specific workloads, each representative of a different computational dwarf, and evaluate them across three different platforms: a multicore system, a high-performance discrete GPU, and a low-power integrated GPU. We find that the integrated GPU delivers superior energy savings and a comparable energy-delay product (EDP) when compared to its discrete counterpart, and it can still outperform the CPUs of a multicore system at a fraction of the power.
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