Run-Time Resource Management Controller for Power Efficiency of GP-GPU Architecture
The demand for high-performance computing (HPC) has been increasing since the invention of computing technology. This led to the invocation of sophisticated multi/many-core processors with high performance. Graphical processing units (GPUs) have emerged as an important innovation in the many-core er...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8649642/ |
| _version_ | 1819174733789265920 |
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| author | Shaheryar Najam Jameel Ahmed Saad Masood Chuadhry Mujeeb Ahmed |
| author_facet | Shaheryar Najam Jameel Ahmed Saad Masood Chuadhry Mujeeb Ahmed |
| author_sort | Shaheryar Najam |
| collection | DOAJ |
| description | The demand for high-performance computing (HPC) has been increasing since the invention of computing technology. This led to the invocation of sophisticated multi/many-core processors with high performance. Graphical processing units (GPUs) have emerged as an important innovation in the many-core era as it features a high number of processors. The GPU acts as a computational accelerator that can significantly reduce the computational time of the HPC, as it can offer standout parallelism for high-end computing applications such as graphics designing. However, increasing the resources has resulted in higher power consumption and heat dissipation which has been a challenging problem for modern HPC Units. On the other hand, because of the dynamic nature of workload, a large amount of parallelism, offered by these many-core processors, is often underutilized. An ideal system would be smart enough to efficiently utilize resources and save power where less workload is available. Reducing the resources dynamically has direct implications on the performance of the system. However, if less workload is available, reducing the resources would not harm the performance, rather it would save power with less to no trade-off in overall throughput of the system. In this paper, a smart power and performance efficient resource management controller for general purpose-GPU architecture is presented. The proposed controller, based on a feedback mechanism, keeps on analyzing the current frequency of central processing unit (CPU) and GPU, number of active cores of the CPU and utilization of CPU and GPU. On the basis of collected data, the proposed controller which features fuzzy type-2 as an optimizing mechanism tries to create a balance between performance and power consumption. The results are evaluated against various benchmarks on NVIDIA TK1 GPU kit and by using dynamic voltage and frequency scaling and core gating, up to 47 % reduction in power consumption has been achieved. |
| first_indexed | 2024-12-22T20:43:40Z |
| format | Article |
| id | doaj.art-f06dad26ba0f4a429c4718093ef97dcb |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T20:43:40Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f06dad26ba0f4a429c4718093ef97dcb2022-12-21T18:13:16ZengIEEEIEEE Access2169-35362019-01-017254932550510.1109/ACCESS.2019.29010108649642Run-Time Resource Management Controller for Power Efficiency of GP-GPU ArchitectureShaheryar Najam0https://orcid.org/0000-0002-2186-2342Jameel Ahmed1Saad Masood2Chuadhry Mujeeb Ahmed3Riphah International University, Islamabad, PakistanRiphah International University, Islamabad, PakistanPakistan Institute of Engineering and Technology, Multan, PakistanSingapore University of Technology and Design, SingaporeThe demand for high-performance computing (HPC) has been increasing since the invention of computing technology. This led to the invocation of sophisticated multi/many-core processors with high performance. Graphical processing units (GPUs) have emerged as an important innovation in the many-core era as it features a high number of processors. The GPU acts as a computational accelerator that can significantly reduce the computational time of the HPC, as it can offer standout parallelism for high-end computing applications such as graphics designing. However, increasing the resources has resulted in higher power consumption and heat dissipation which has been a challenging problem for modern HPC Units. On the other hand, because of the dynamic nature of workload, a large amount of parallelism, offered by these many-core processors, is often underutilized. An ideal system would be smart enough to efficiently utilize resources and save power where less workload is available. Reducing the resources dynamically has direct implications on the performance of the system. However, if less workload is available, reducing the resources would not harm the performance, rather it would save power with less to no trade-off in overall throughput of the system. In this paper, a smart power and performance efficient resource management controller for general purpose-GPU architecture is presented. The proposed controller, based on a feedback mechanism, keeps on analyzing the current frequency of central processing unit (CPU) and GPU, number of active cores of the CPU and utilization of CPU and GPU. On the basis of collected data, the proposed controller which features fuzzy type-2 as an optimizing mechanism tries to create a balance between performance and power consumption. The results are evaluated against various benchmarks on NVIDIA TK1 GPU kit and by using dynamic voltage and frequency scaling and core gating, up to 47 % reduction in power consumption has been achieved.https://ieeexplore.ieee.org/document/8649642/HPCCPUGP-GPUGPUNvidiafuzzy type-2 |
| spellingShingle | Shaheryar Najam Jameel Ahmed Saad Masood Chuadhry Mujeeb Ahmed Run-Time Resource Management Controller for Power Efficiency of GP-GPU Architecture IEEE Access HPC CPU GP-GPU GPU Nvidia fuzzy type-2 |
| title | Run-Time Resource Management Controller for Power Efficiency of GP-GPU Architecture |
| title_full | Run-Time Resource Management Controller for Power Efficiency of GP-GPU Architecture |
| title_fullStr | Run-Time Resource Management Controller for Power Efficiency of GP-GPU Architecture |
| title_full_unstemmed | Run-Time Resource Management Controller for Power Efficiency of GP-GPU Architecture |
| title_short | Run-Time Resource Management Controller for Power Efficiency of GP-GPU Architecture |
| title_sort | run time resource management controller for power efficiency of gp gpu architecture |
| topic | HPC CPU GP-GPU GPU Nvidia fuzzy type-2 |
| url | https://ieeexplore.ieee.org/document/8649642/ |
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