Power models supporting energy-efficient co-design on ultra-low power embedded systems

Tran, Vi; Barry, Brendan; Ha, Phuong Hoai

doi:10.1109/samos.2016.7818329

Cited by 6 publications

(5 citation statements)

References 24 publications

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“…Recent researches have suggested that the energy consumption of future computing systems will be dominated by the cost of data movement [48,127,128]. It is predicted that for 10nm technology chips, the energy required between accessing data in nearby on-chip memory and accessing data across the chip, will differ as much as 75× (2pJ versus 150pJ), whereas the energy required between accessing on-chip data and accessing off-chip data will only differ 2× (150pJ versus 300pJ) [48].…”

Section: New Concurrency-aware Van Emde Boas Layoutmentioning

confidence: 99%

“…Among energy and power models for different architectures [41,40,99,101,125,129], energy roofline models [41,40] are some of the comprehensive energy models that abstract away possible algorithms in order to analyze and characterize different multicore platforms in terms of energy consumption. Our new energy model, which abstracts away possible multicore platform and characterize the energy complexity of algorithms based on their work, span and I/O complexity, complements the energy roofline models.…”

Section: D24: Report On the Final Prototype Of Programming Abstractionsmentioning

confidence: 99%

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D2.4 Report on the final prototype of programming abstractions for energy-efficient inter-process communication

Ha¹,

Tran²,

Ibrahim³

et al. 2018

Preprint

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Work package 2 (WP2) aims to develop libraries for energy-efficient inter-process communication and data sharing on the EXCESS platforms. The Deliverable D2.4 reports on the final prototype of programming abstractions for energy-efficient interprocess communication. Section 1 is the updated overview of the prototype of programming abstraction and devised power/energy models. The Section 2-6 contain the latest results of the four studies:D2.4: Report on the final prototype of programming abstractions 6 D2.3, this model proposed using Ideal Cache memory model to compute I/O complexity of the algorithms. Besides a case study of SpMV to demonstrate how to apply the ICE model to find energy complexity of parallel algorithms, Deliverable D2.4 also reports a case study to apply the ICE model to Dense Matrix Multiplication (matmul). The model is then validated with both data-intensive (i.e., SpMV) and computationintensive (i.e., matmul) algorithms according to three aspects: different algorithms, different input types/sizes and different platforms. In order to make the reading flow easy to follow, we include in this report a complete study of ICE model along with latest results.D2.4: Report on the final prototype of programming abstractions Contents 1.3 Energy Model on CPU for Lock-free Data-structures in Dynamic EnvironmentsIn this section, we firstly consider the modeling and the analysis of the performance of lockfree data structures. Then, we combine the perfomance analysis with our power model that is introduced in D2.1 [75] and D2.3 [73] to estimate the energy efficiency of lock-free data structures that are used in various settings.Lock-free data structures are based on retry loops and are called by application-specific routines. In contrast to the model and analysis provided in D2.3, we consider here the lock-free data structures in dynamic environments. The size of each of the retry loops, and the size of the application routines invoked in between, are not constant but may change dynamically.We present two analytical frameworks for calculating the performance of lock-free data structures. The new frameworks follow two different approaches. The first framework, the simplest one, is based on queuing theory. It introduces an average-based approach that facilitates a more coarse-grained analysis, with the benefit of being ignorant of size distributions. Because of this independence from the distribution nature it covers a set of complicated designs. The second approach, instantiated with an exponential distribution for the size of the application routines, uses Markov chains, and is tighter because it constructs stochastically the execution, step by step.Both frameworks provide a performance estimate which is close to what we observe in practice. We have validated our analysis on (i) several fundamental lock-free data structures such as stacks, queues, deques and counters, some of them employing dynamic helping mechanisms, and (ii) synthetic tests covering a wide range of possible lock-free designs. We show the ap...

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Section: New Concurrency-aware Van Emde Boas Layoutmentioning

confidence: 99%

Section: D24: Report On the Final Prototype Of Programming Abstractionsmentioning

confidence: 99%

D2.4 Report on the final prototype of programming abstractions for energy-efficient inter-process communication

Ha¹,

Tran²,

Ibrahim³

et al. 2018

Preprint

Self Cite

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“…Because the model has abstracted possible platform by only 4 parameters (i.e., op , I/O , π op , and π I/O ), there are the differences between the model and experiment ratios shown in the Figure 5 and 6. For accurate models that provide the precise energy estimation, the platform parameters need to be highly detailed such as RTHpower model for embedded platforms [28,27].…”

Section: Validating Ice Using Different Spmv Algorithmsmentioning

confidence: 99%

“…Understanding the energy complexity of algorithms is crucial important to improve the energy efficiency of algorithms [31,30,29,20] and reduce the energy consumption of computing systems [28,27,21]. One of the main approaches to understand the energy complexity of algorithms is to devise energy models.…”

Section: Introductionmentioning

confidence: 99%

ICE: A General and Validated Energy Complexity Model for Multithreaded Algorithms

Tran

2016

2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS)

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Like time complexity models that have significantly contributed to the analysis and development of fast algorithms, energy complexity models for parallel algorithms are desired as crucial means to develop energy efficient algorithms for ubiquitous multicore platforms. Ideal energy complexity models should be validated on real multicore platforms and applicable to a wide range of parallel algorithms. However, existing energy complexity models for parallel algorithms are either theoretical without model validation or algorithm-specific without ability to analyze energy complexity for a wide-range of parallel algorithms. This paper presents a new general validated energy complexity model for parallel (multithreaded) algorithms. The new model abstracts away possible multicore platforms by their static and dynamic energy of computational operations and data access, and derives the energy complexity of a given algorithm from its work, span and I/O complexity. The new model is validated by different sparse matrix vector multiplication (SpMV) algorithms and dense matrix multiplication (matmul) algorithms running on high performance computing (HPC) platforms (e.g., Intel Xeon and Xeon Phi). The new energy complexity model is able to characterize and compare the energy consumption of SpMV and matmul kernels according to three aspects: different algorithms, different input matrix types and different platforms. The prediction of the new model regarding which algorithm consumes more energy with different inputs on different platforms, is confirmed by the experimental results. In order to improve the usability and accuracy of the new model for a wide range of platforms, arXiv:1605.08222v2 [cs.DC] 4 Oct 2016 the platform parameters of ICE model are provided for eleven platforms including HPC, accelerator and embedded platforms.

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“…Recent researches have suggested that the energy consumption of future computing systems will be dominated by the cost of data movement [12,34,35]. It is predicted that for 10nm technology chips, the energy required between accessing data in nearby on-chip memory and accessing data across the chip, will differ as much as 75× (2pJ versus 150pJ), whereas the energy required between accessing on-chip data and accessing off-chip data will only differ 2× (150pJ versus 300pJ) [12].…”

Section: Introductionmentioning

confidence: 99%

GreenBST: Energy-Efficient Concurrent Search Tree

Umar

Anshus

2016

Euro-Par 2016: Parallel Processing

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Abstract. Like other fundamental abstractions for energy-efficient computing, search trees need to support both high concurrency and finegrained data locality. However, existing locality-aware search trees such as ones based on the van Emde Boas layout (vEB-based trees), poorly support concurrent (update) operations while existing highly-concurrent search trees such as the non-blocking binary search trees do not consider data locality. We present GreenBST, a practical energy-efficient concurrent search tree that supports fine-grained data locality as vEB-based trees do, but unlike vEB-based trees, GreenBST supports high concurrency. GreenBST is a k-ary leaf-oriented tree of GNodes where each GNode is a fixed size tree-container with the van Emde Boas layout. As a result, GreenBST minimizes data transfer between memory levels while supporting highly concurrent (update) operations. Our experimental evaluation using the recent implementation of non-blocking binary search trees, highly concurrent B-trees, conventional vEB trees, as well as the portably scalable concurrent trees shows that GreenBST is efficient: its energy efficiency (in operations/Joule) and throughput (in operations/second) are up to 65% and 69% higher, respectively, than the other trees on a high performance computing (HPC) platform (Intel Xeon), an embedded platform (ARM), and an accelerator platform (Intel Xeon Phi). The results also provide insights into how to develop energy-efficient data structures in general.

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Power models supporting energy-efficient co-design on ultra-low power embedded systems

Cited by 6 publications

References 24 publications

D2.4 Report on the final prototype of programming abstractions for energy-efficient inter-process communication

D2.4 Report on the final prototype of programming abstractions for energy-efficient inter-process communication

ICE: A General and Validated Energy Complexity Model for Multithreaded Algorithms

GreenBST: Energy-Efficient Concurrent Search Tree

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