OpenMP-based parallelization on an MPCore multiprocessor platform – A performance and power analysis

Blume, Holger; Livonius, J. von; Rotenberg, L.; Noll, Tobias G.; Bothe, H.; Brakensiek, J.

doi:10.1016/j.sysarc.2008.04.001

Cited by 18 publications

(15 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This way, an embedded system with SGM real-time performance and very little power consumption seems feasible. The potential of using OpenMP on such a platform has been shown by [2].…”

Section: Discussionmentioning

confidence: 99%

Real-time Semi-Global Matching on the CPU

Gehrig

Rabe

2010

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops

View full text Add to dashboard Cite

Among the top-performing stereo algorithms on the Middlebury Stereo Database, Semi-Global Matching (SGM) is commonly regarded as the most efficient algorithm. Consequently, real-time implementations of the algorithm for graphics hardware (GPU) and reconfigurable hardware (FPGA) exist. However, the computation time on general purpose PCs is still more than a second.In this paper, a real-time SGM implementation on a general purpose PC is introduced. Parallelization and image subsampling is used while ensuring the full disparity resolution for small disparities.This approach is especially beneficial for robotic and driver assistance systems. The system is able to compute 640x320 image pairs at more than 14Hz, leaving also computational resources for subsequent processing such as free space computation or object detection. The algorithmic approach is portable to multi-core embedded processors.

show abstract

“…This way, an embedded system with SGM real-time performance and very little power consumption seems feasible. The potential of using OpenMP on such a platform has been shown by [2].…”

Section: Discussionmentioning

confidence: 99%

Real-time Semi-Global Matching on the CPU

Gehrig

Rabe

2010

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops

View full text Add to dashboard Cite

show abstract

“…In a given area and chip process size, N core can reflect the thread-level parallel degree of processor which has a direct impact on power (Blume et al 2008). It is denoted by N core .…”

Section: Hardware Factors Affecting Energy Consumptionmentioning

confidence: 99%

Energy analysis for an executable program on a single computer based on BP neural network

Tan

Zeng

2013

Journal of Experimental & Theoretical Artificial Intelligen

View full text Add to dashboard Cite

Power management of application programs is a hot research in the area of green computing and high-productivity computing. Because of the complexity of application programs, heterogeneity of processors and uncertainty of the running environment, it is difficult to propose an accurate method to predict energy consumption for application program directly. So, we present a power analysis paradigm for application program based on artificial neural network. First, we build a power analysis model based on back propagation neural network (BPNN). The three factors of software, hardware and environment are taken as the inputs of BPNN, and energy consumption and finish time as the outputs of BPNN. Next, we choose lot of classic application programs from different fields as training samples. After learning and training, an expected BPNN is obtained which can be used to predict energy consumption for other new programs. Repeated experiments show that this power analysis paradigm is rational and feasible.

show abstract

“…Fig. 6 shows our approach for the compilation of applications using the OpenMP programming model [14]. The source code written to the OpenMP model goes through a two-phase compilation: source-to-source pre-compilation and then generic C compilation.…”

Section: Openmp Programming Model On Embedded Multi-core Processormentioning

confidence: 99%

“…Many research activities have been performed to speed up the H.264/AVC codec on multi-core platforms, and to analyze the performance impact of architectural parameters and parallelization techniques [5][6][7][8][9][10][11][12][13][14]. Some of the researches significantly increased the codec performance in the desktop multi-core platforms [5,9].…”

Section: Introductionmentioning

confidence: 99%

Exploring parallelization techniques based on OpenMP in H.264/AVC encoder for embedded multi-core processor

Song

2012

Journal of Systems Architecture

View full text Add to dashboard Cite

OpenMP-based parallelization on an MPCore multiprocessor platform – A performance and power analysis

Cited by 18 publications

References 3 publications

Real-time Semi-Global Matching on the CPU

Real-time Semi-Global Matching on the CPU

Energy analysis for an executable program on a single computer based on BP neural network

Exploring parallelization techniques based on OpenMP in H.264/AVC encoder for embedded multi-core processor

Contact Info

Product

Resources

About