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Kim, Hyesoon

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Author: Yalamanchili, Sudhakar ×

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    Exploration of the energy and thermal behaviors of emerging architectures
    (Georgia Institute of Technology, 2014-09-30) Yalamanchili, Sudhakar ; Kim, Hyesoon
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    Power Modeling for GPU Architecture Using McPAT
    (Georgia Institute of Technology, 2013) Lim, Jieun ; Lakshminarayana, Nagesh B. ; Kim, Hyesoon ; Song, William ; Yalamanchili, Sudhakar ; Sung, Wonyong
    Graphics Processing Units (GPUs) are very popular for both graphics and general-purpose applications. Since GPUs operate many processing units and manage multiple levels of memory hierarchy, they consume a significant amount of power. Although several power models for CPUs are available, the power consumption of GPUs has not been studied much yet. In this paper, we develop a new power model for GPUs by utilizing McPAT, a CPU power tool. We generate initial power model data from McPAT with a detailed GPU configuration, and then adjust the models by comparing them with empirical data.We use the NVIDIA’s Fermi architecture for building the power model, and our model estimates the GPU power consumption with an average error of 7.7% and 12.8% for the microbenchmarks and Merge benchmarks, respectively.
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    Design Space Exploration of On-chip Ring Interconnection for a CPU-GPU Architecture
    (Georgia Institute of Technology, 2012) Lee, Jaekyu ; Li, Si ; Kim, Hyesoon ; Yalamanchili, Sudhakar
    Future chip multiprocessors (CMP) will only grow in core count and diversity in terms of frequency, power consumption, and resource distribution. Incorporating a GPU architecture into CMP, which is more efficient with certain types of applications, is the next stage in this trend. This heterogeneous mix of architectures will use an on-chip interconnection to access shared resources such as last-level cache tiles and memory controllers. The configuration of this on-chip network will likely have a significant impact on resource distribution, fairness, and overall performance. The heterogeneity of this architecture inevitably exerts different pressures on the interconnection due to the differing characteristics and requirements of applications running on CPU and GPU cores. CPU applications are sensitive to latency, while GPGPU applications require massive bandwidth. This is due to the difference in the thread-level parallelism of the two architectures. GPUs use more threads to hide the effect of memory latency but require massive bandwidth to supply those threads. On the other hand, CPU cores typically running only one or two threads concurrently are very sensitive to latency. This study surveys the impact and behavior of the interconnection network when CPU and GPGPU applications run simultaneously. This will shed light on other architectural interconnection studies on CPU-GPU heterogeneous architectures.