Energy Efficient HPC on Embedded SoCs: Optimization Techniques for Mali GPU

Grasso, Ivan; Radojković, Petar; Rajovic, Nikola; Gelado, Isaac; Ramírez, Alex

doi:10.1109/ipdps.2014.24

Cited by 45 publications

(31 citation statements)

References 15 publications

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“…Another container-based data center is commercially available as a standalone, fuel-and battery-power supply driven resource [14]. Using mobile SoCs in the context of HPC and building small clusters of unconventional hardware [5,11] as well as exploring Jetson TK1 for this purpose has also been performed [20] or at least considered [16] by others. However, to the best of our knowledge there is currently no group or enterprise that has driven this kind of system integration this far and ICARUS is the only container-based system combined with customised renewable energies power supply.…”

Section: System Overviewmentioning

confidence: 99%

The ICARUS White Paper: A Scalable, Energy-Efficient, Solar-Powered HPC Center Based on Low Power GPUs

Geveler

Ribbrock

Donner

et al. 2017

Euro-Par 2016: Parallel Processing Workshops

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Abstract. We present a unique approach for integrating research in High Performance Computing (HPC) as well as photovoltaic (PV) solar farming and battery technologies into a container-based compute center designed for a maximum of energy efficiency, performance and extensibility/scalability. We use NVIDIA Jetson TK1 boards to build a considerably dimensioned cluster of 60 low-power GPUs, attach a 7.5 kWp solar farm and a 8 kWh Lithium-Ion battery power supply and integrate everything into a single-container, standalone housing. We demonstrate the success of our system by evaluating the performance and energy efficiency for common versatile dense and sparse linear algebra kernels as well as a full CFD code. By this work we can show, that with current technology, energy consumption-induced follow-up cost of HPC can be reduced to zero.

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Section: System Overviewmentioning

confidence: 99%

The ICARUS White Paper: A Scalable, Energy-Efficient, Solar-Powered HPC Center Based on Low Power GPUs

Geveler

Ribbrock

Donner

et al. 2017

Euro-Par 2016: Parallel Processing Workshops

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“…Grasso et al [40] use OpenCL on a Cortex A15 board with a Mali GPU and find that they can get 8.7-times better performance than the CPU with 1/3 the energy. If similar work could be done to obtain GPGPU support on the Raspberry Pi, our cluster could obtain a huge performance boost.…”

Section: Future Workmentioning

confidence: 99%

A Raspberry Pi Cluster Instrumented for Fine-Grained Power Measurement

2016

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Abstract:Power consumption has become an increasingly important metric when building large supercomputing clusters. One way to reduce power usage in large clusters is to use low-power embedded processors rather than the more typical high-end server CPUs (central processing units). We investigate various power-related metrics for seventeen different embedded ARM development boards in order to judge the appropriateness of using them in a computing cluster. We then build a custom cluster out of Raspberry Pi boards, which is specially designed for per-node detailed power measurement. In addition to serving as an embedded cluster testbed, our cluster's power measurement, visualization and thermal features make it an excellent low-cost platform for education and experimentation.

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“…The OpenCL runtime can also be used to automatically select a work-group size for the kernel if there is no data sharing among work-items [1]. Again, as reported in [9], this does not always produce the best results.…”

Section: A Work-group Size Manipulationmentioning

confidence: 99%

“…In the context executing general purpose computing applications on mobile platforms, [9] examined the Mali GPU performance for HPC workloads, and improved its energy efficiency. This work mainly focused on GPU, without considering the possible collaboration with CPU.…”

Section: Introductionmentioning

confidence: 99%

Energy-efficient execution of data-parallel applications on heterogeneous mobile platforms

Prakash

Wang

Irimiea

et al. 2015

2015 33rd IEEE International Conference on Computer Design (ICCD)

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Abstract-State-of-the-art mobile system-on-chips (SoC) include heterogeneity in various forms for accelerated and energyefficient execution of diverse range of applications. The modern SoCs now include programmable cores such as CPU and GPU with very different functionality. The SoCs also integrate performance heterogeneous cores with different power-performance characteristics but the same instruction-set architecture such as ARM big.LITTLE. In this paper, we first explore and establish the combined benefits of functional heterogeneity and performance heterogeneity in improving power-performance behavior of data parallel applications. Next, given an application specified in OpenCL, we present a static partitioning strategy to execute the application kernel across CPU and GPU cores along with voltage-frequency setting for individual cores so as to obtain the best power-performance tradeoff. We achieve over 19% runtime improvement by exploiting the functional and performance heterogeneities concurrently. In addition, energy saving of 36% is achieved by using appropriate voltage-frequency setting without significantly degrading the runtime improvement from concurrent execution.

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Energy Efficient HPC on Embedded SoCs: Optimization Techniques for Mali GPU

Cited by 45 publications

References 15 publications

The ICARUS White Paper: A Scalable, Energy-Efficient, Solar-Powered HPC Center Based on Low Power GPUs

The ICARUS White Paper: A Scalable, Energy-Efficient, Solar-Powered HPC Center Based on Low Power GPUs

A Raspberry Pi Cluster Instrumented for Fine-Grained Power Measurement

Energy-efficient execution of data-parallel applications on heterogeneous mobile platforms

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