An approach to optimise the energy efficiency of iterative computation on integrated GPU–CPU systems

Garzón, Eduardo Garzón; Moreno, Juan José Hurtado; Martínez, José A.

doi:10.1007/s11227-016-1643-9

Cited by 13 publications

(4 citation statements)

References 21 publications

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“…Zhang et al (2015Zhang et al ( , 2017b studied the co-running behaviors of different devices for the same application, while Zhu et al (2014Zhu et al ( , 2017b) studied co-running performance degradation for different devices for separate applications. Garzón et al (2017) proposed an approach to optimize the energy efficiency of iterative computation on heterogeneous processors. Zhu et al (2017a) presented a systematic study on heterogeneous processors with power caps considered.…”

Section: Performance Analysis For Coupled Heterogeneous Processorsmentioning

confidence: 99%

Performance evaluation and analysis of sparse matrix and graph kernels on heterogeneous processors

et al. 2019

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Heterogeneous processors integrate very distinct compute resources such as CPUs and GPUs into the same chip, thus can exploit the advantages and avoid disadvantages of those compute units. We in this work evaluate and analyze eight sparse matrix and graph kernels on an AMD CPU-GPU heterogeneous processor by using 956 sparse matrices. Five characteristics, i.e., load balancing, indirect addressing, memory reallocation, atomic operations, and dynamic characteristics are our major considerations. The experimental results show that although the CPU and GPU parts access the same DRAM, very different performance behaviors are observed. For example, though the GPU part in general outperforms the CPU part, it cannot achieve the best performance in all cases given by the CPU part. Moreover, the bandwidth utilization of atomic operations on heterogeneous processors can be much higher than a high-end discrete GPU.

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Section: Performance Analysis For Coupled Heterogeneous Processorsmentioning

confidence: 99%

Performance evaluation and analysis of sparse matrix and graph kernels on heterogeneous processors

et al. 2019

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“…There are no explicit communication in this architecture. [2] proposes E-ADITHE for improving performance and energy efficiency of iterative computations. E-ADITHE does not take irregular iterative computations into account.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Partitioning for Iterated Sequences of Irregular OpenCL Kernels

Huchant

Barthou

Counilh

2018

2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

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OpenCL defines a common parallel programming language for all devices, although writing tasks adapted to the devices, managing communication and load-balancing issues are left to the programmer. We propose in this paper a static/dynamic approach for the execution of an iterated sequence of datadependent kernels on a multi-device heterogeneous architecture. The method allows to automatically distribute irregular kernels onto multiple devices and tackles, without training, both load balancing and data transfers issues coming from hardware heterogeneity, load imbalance within the application itself and load variations between repeated executions of the sequence.

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“…Energy Optimization How to effectively exploit heterogeneous architectures for energy efficient computing is a heavily studied area. Some of the recent examples in the area include: how to distribute workloads across CPUs and GPUs [25] and MPSoCs [4], power modeling for GPGPUs [24], and energy aware iterative compilation [7,12] etc. Unlike these approaches which all use analytic models or hard-wired heuristics to perform optimization for a specific goal, we develop a portable method that can automatically re-target for any optimization metric.…”

Section: Related Workmentioning

confidence: 99%

Adaptive optimization for OpenCL programs on embedded heterogeneous systems

Taylor

Marco

Wang

2017

Proceedings of the 18th ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems

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Heterogeneous multi-core architectures consisting of CPUs and GPUs are commonplace in today's embedded systems. These architectures offer potential for energy efficient computing if the application task is mapped to the right core. Realizing such potential is challenging due to the complex and evolving nature of hardware and applications. This paper presents an automatic approach to map OPENCL kernels onto heterogeneous multi-cores for a given optimization criterion-whether it is faster runtime, lower energy consumption or a trade-off between them. This is achieved by developing a machine learning based approach to predict which processor to use to run the OPENCL kernel and the host program, and at what frequency the processor should operate. Instead of hand-tuning a model for each optimization metric, we use machine learning to develop a unified framework that first automatically learns the optimization heuristic for each metric off-line, then uses the learned knowledge to schedule OPENCL kernels at runtime based on code and runtime information of the program. We apply our approach to a set of representative OPENCL benchmarks and evaluate it on an ARM big.LITTLE mobile platform. Our approach achieves over 93% of the performance delivered by a perfect predictor. We obtain, on average, 1.2x, 1.6x, and 1.8x improvement respectively for runtime, energy consumption and the energy delay product when compared to a comparative heterogeneous-aware OPENCL task mapping scheme.

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An approach to optimise the energy efficiency of iterative computation on integrated GPU–CPU systems

Cited by 13 publications

References 21 publications

Performance evaluation and analysis of sparse matrix and graph kernels on heterogeneous processors

Performance evaluation and analysis of sparse matrix and graph kernels on heterogeneous processors

Adaptive Partitioning for Iterated Sequences of Irregular OpenCL Kernels

Adaptive optimization for OpenCL programs on embedded heterogeneous systems

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