Case Studies of Multi-core Energy Efficiency in Task Based Programs

Lien, Hallgeir; Natvig, Lasse; Hasib, Abdullah Al; Meyer, Jan Christian

doi:10.1007/978-3-642-32606-6_4

Cited by 11 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These counters can be accessed either by the RAPL (Running Average Power Limit ) interface (root-level) or the powercap interface (user-level). We report both core/package-energy consumption and Energy Delay Product (EDP: Joule × Second) [30,31] to perform energy efficiency analysis. For both, lower values corresponds to better energy efficiency.…”

Section: Metrics Used For Analysismentioning

confidence: 46%

Energy Efficiency Effects of Vectorization in Data Reuse Transformations for Many-Core Processors—A Case Study †

Hasib

Natvig

Kjeldsberg

et al. 2017

JLPEA

Self Cite

View full text Add to dashboard Cite

Abstract:Thread-level and data-level parallel architectures have become the design of choice in many of today's energy-efficient computing systems. However, these architectures put substantially higher requirements on the memory subsystem than scalar architectures, making memory latency and bandwidth critical in their overall efficiency. Data reuse exploration aims at reducing the pressure on the memory subsystem by exploiting the temporal locality in data accesses. In this paper, we investigate the effects on performance and energy from a data reuse methodology combined with parallelization and vectorization in multi-and many-core processors. As a test case, a full-search motion estimation kernel is evaluated on Intel R Core TM i7-4700K (Haswell) and i7-2600K (Sandy Bridge) multi-core processors, as well as on an Intel R Xeon Phi TM many-core processor (Knights Landing) with Streaming Single Instruction Multiple Data (SIMD) Extensions (SSE) and Advanced Vector Extensions (AVX) instruction sets. Results using a single-threaded execution on the Haswell and Sandy Bridge systems show that performance and EDP (Energy Delay Product) can be improved through data reuse transformations on the scalar code by a factor of ≈3× and ≈6×, respectively. Compared to scalar code without data reuse optimization, the SSE/AVX2 version achieves ≈10×/17× better performance and ≈92×/307× better EDP, respectively. These results can be improved by 10% to 15% using data reuse techniques. Finally, the most optimized version using data reuse and AVX512 achieves a speedup of ≈35× and an EDP improvement of ≈1192× on the Xeon Phi system. While single-threaded execution serves as a common reference point for all architectures to analyze the effects of data reuse on both scalar and vector codes, scalability with thread count is also discussed in the paper.

show abstract

Section: Metrics Used For Analysismentioning

confidence: 46%

Energy Efficiency Effects of Vectorization in Data Reuse Transformations for Many-Core Processors—A Case Study †

Hasib

Natvig

Kjeldsberg

et al. 2017

JLPEA

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [23], Jakobs et al demonstrated the capabilities and limitations of vectorization concerning energy efficiency, considering both automatic and manual vectorization techniques. The influence of vectorization combining with multi-threading on the energy efficiency of program executions is investigated in [24]. In [25], Rojek proposed a method that leverages the mixed precision arithmetic to reduce the energy consumption for applications executed in supercomputing centers.…”

Section: Related Workmentioning

confidence: 99%

Correlation of Performance Optimizations and Energy Consumption for Stencil-Based Application on Intel Xeon Scalable Processors

Szustak

Wyrzykowski

Olas

et al. 2020

IEEE Trans. Parallel Distrib. Syst.

View full text Add to dashboard Cite

This article provides a comprehensive study of the impact of performance optimizations on the energy efficiency of a real-world CFD application called MPDATA, as well as an insightful analysis of performance-energy interaction of these optimizations with the underlying hardware that represents the first generation of Intel Xeon Scalable processors. Considering the MPDATA iterative application as a use case, we explore the fundamentals of energy and performance analysis for a memory-bound application when exposed to a set of optimization steps that increase the application performance, by improving the operational intensity of code and utilizing resources more efficiently. It is shown that for memory-bound applications, optimizing toward high performance could be a powerful strategy for improving the energy efficiency as well. In fact, for the considered performance optimizations, the energy gain is correlated with the performance gain but with varying degrees. As a result, these optimizations allow improving both performance and energy consumption radically, up to about 10.9 and 8.8 times, respectively. The impact of the Intel AVX-512 SIMD extension on the energy consumption and performance is demonstrated. Also, we discover limitations on the usability of CPU frequency scaling as a tool for balancing energy savings with admissible performance losses.

show abstract

“…In the context of parallel and distributed computing, energy efficient voltage scheduling for multi-core processors is an important issue [21]. The improvement of energy efficiency not only depending on the problem size but also the degree of multi-threading [22]. Low-power consumption computer such as Raspberry Pi (3.5 Watt) can perform as a desktop pc and used for image processing and weed fractal dimension processing [23].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Parallel K-means for Java Paddy Mapping Using Time-series Satelite Imagery

et al. 2018

View full text Add to dashboard Cite

Spatiotemporal analysis of MODIS Vegetation Index Imagery widely used for vegetation seasonal mapping b oth on forest and agricultural site. In order to provide a long -terms of vegetation characteristic maps, a wide time-series images analysis is needed which require high -performance computer and also consumes a lot of energy resources. Meanwhile, for agriculture monitoring purpose in Indonesia, that analysis has to b e employed gradually an d endlessly to provide the latest condition of paddy field vegetation information. This research is aimed to develop a method to produce the optimized solution in classifying vegetation of paddy fields that diverse b oth spatial and temporal characteristics . The time-series EVI data from MODIS have b een filtered using wavelet transform to reduce noise that caused b y cloud. Sequential K-means and Parallel K-means unsupervised classification method were used in b oth CPU and GPU to find the efficient and the rob ust result. The developed method has b een tested and implemented using the sample case of paddy fields in Java Island. The b est system which can accommodate of the extend-ab ility, affordab ility, redundancy, energy-saving, maintainab ility indicators are ARM-b ased processor (Raspb erry Pi), with the highest speed up of 8 and the efficiency of 60% .

show abstract

Case Studies of Multi-core Energy Efficiency in Task Based Programs

Cited by 11 publications

References 9 publications

Energy Efficiency Effects of Vectorization in Data Reuse Transformations for Many-Core Processors—A Case Study †

Energy Efficiency Effects of Vectorization in Data Reuse Transformations for Many-Core Processors—A Case Study †

Correlation of Performance Optimizations and Energy Consumption for Stencil-Based Application on Intel Xeon Scalable Processors

Optimization of Parallel K-means for Java Paddy Mapping Using Time-series Satelite Imagery

Contact Info

Product

Resources

About