CLIP: Cluster-Level Intelligent Power Coordination for Power-Bounded Systems

Zou, Pengfei; Allen, Tyler; Davis, Claude H.; Feng, Xueyang; Ge, Rong

doi:10.1109/cluster.2017.98

Cited by 13 publications

(7 citation statements)

References 32 publications

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“…Furthermore, Refs. [31,32] focused on a cluster. Firstly, when the power of a cluster is limited, they needed to set the number of active nodes according to an application's scalability.…”

Section: Related Workmentioning

confidence: 99%

More bang for your buck: Boosting performance with capped power consumption

Chen

et al. 2021

Tinshhua Sci. Technol.

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Achieving faster performance without increasing power and energy consumption for computing systems is an outstanding challenge. This paper develops a novel resource allocation scheme for memory-bound applications running on High-Performance Computing (HPC) clusters, aiming to improve application performance without breaching peak power constraints and total energy consumption. Our scheme estimates how the number of processor cores and CPU frequency setting affects the application performance. It then uses the estimate to provide additional compute nodes to memory-bound applications if it is profitable to do so. We implement and apply our algorithm to 12 representative benchmarks from the NAS parallel benchmark and HPC Challenge (HPCC) benchmark suites and evaluate it on a representative HPC cluster. Experimental results show that our approach can effectively mitigate memory contention to improve application performance, and it achieves this without significantly increasing the peak power and overall energy consumption. Our approach obtains on average 12.69% performance improvement over the default resource allocation strategy, but uses 7.06% less total power, which translates into 17.77% energy savings.

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“…Furthermore, Refs. [31,32] focused on a cluster. Firstly, when the power of a cluster is limited, they needed to set the number of active nodes according to an application's scalability.…”

Section: Related Workmentioning

confidence: 99%

More bang for your buck: Boosting performance with capped power consumption

Chen

et al. 2021

Tinshhua Sci. Technol.

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“…A runtime system termed conductor was proposed in Reference 10 which utilizes configuration space exploration and adaptive power balancing to maximize performance under a hardware‐enforced power cap. A multilevel power distribution framework termed CLIP was proposed in Reference 11, which estimates per node power budget using the workload characteristics and utilizes memory accesses to determine CPU and memory affinity.…”

Section: Related Workmentioning

confidence: 99%

Runtime power allocation approach for GAMESS hybrid CPU‐GPU implementation

Sundriyal

Sosonkina

Poole

et al. 2020

Concurrency and Computation

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To improve power consumption of applications at the runtime, modern processors provide frequency scaling capabilities, which along with workload optimization, are also available on GPU accelerators. In this work, a runtime strategy is proposed to distribute a given power allocation among the host components and the GPU according to the current application performance and power usage, such that GPU execution is prioritized over CPU for power allocation to maximize application performance. Next, the strategy is tailored to an application, a quantum-chemistry package GAMESS for ab initio electronic structure calculations. Specifically, GAMESS hybrid CPU-GPU implementation as provided in the Libcchem library is considered. Experiments, performed on a 28-core node with a Kepler GPU, resulted in performance gains of up to 50% under the proposed strategy and the largest power allocation considered here as compared with the scenario when this allocation was equally distributed among the computing-platform components.

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“…A strategy termed conductor was proposed in [5] that, given a power budget, utilized configuration space exploration and adaptive power balancing for maximizing application performance. The work [6] proposes a multilevel power distribution framework termed CLIP that estimates a per-node power cap by utilizing the workload characteristics and memory accesses to determine processor and memory affinity.…”

Section: Related Workmentioning

confidence: 99%