A Review of Parallel Heterogeneous Computing Algorithms in Power Systems

Rodriguez, Diego; Gómez, Diego Fernando; Álvarez, David; Rivera, Sergio

doi:10.3390/a14100275

Cited by 12 publications

(6 citation statements)

References 240 publications

(230 reference statements)

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“…Newer MPSoCs are mainly heterogeneous, with the integration of application specific instructions also called MPSoCs customization [21] [23] [14]. MPSoCs customization consists on coupling the processors to Specific Functional Units (SFU).…”

Section: Related Workmentioning

confidence: 99%

Design Space Exploration of HW Accelerators and Network Infrastructure for FPGA-Based MPSoC

Dammak,

Baklouti,

Alsekait

2024

IEEE Access

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Supercomputing systems are increasingly reliant on heterogeneous Multiprocessors System on-Chip (MPSoCs), merging multiple processors and hardware accelerators (HWAcc) on the same die to achieve power and performance needs. Due to CPU complication and timing closure challenges of tightly coupled design approach, the state-of-the art of HWAcc design methodology relies on coupling the processors with loosely coupled HWAccs. Loosely-coupled HWAccs can be shared or private accelerators running custom instructions to form a heterogeneous multi-processor system. Some works discussed the determination of the sharing degree of the HWAcc over the processors, however the impact of the integrated communication infrastructure is not discussed. Thus, we propose a high-level design exploration tool to select the accelerators and generate the adequate communication interconnect under performance and area constraints. Different homogeneous and heterogeneous multi-processor configurations are evaluated and compared running different signal processing benchmarks. Experimental results show the efficiency of the proposed exploration tool to rapidly explore and select the adequate architecture.

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Section: Related Workmentioning

confidence: 99%

Design Space Exploration of HW Accelerators and Network Infrastructure for FPGA-Based MPSoC

Dammak,

Baklouti,

Alsekait

2024

IEEE Access

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“…In the field of genetics, the parallelization strategy of processing information has shown significant improvement in the results, especially in reducing the computational time required to solve various problems [10] [11] [12] . As a validation method, the numerical performance profile measures the relative efficiency of the algorithm in solving the set of problems [13] .…”

Section: Introductionmentioning

confidence: 99%

Enhancing Multiple Sequence Alignment with Genetic Algorithms: A Bioinformatics Approach in Biomedical Engineering

Rios-Willars,

Velez-Segura,

Delabra-Salinas

2024

Rev. Mex. Ing. Biomed.

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This study aimed to create a genetic information processing technique for the problem of multiple alignment of genetic sequences in bioinformatics. The objective was to take advantage of the computer hardware's capabilities and analyze the results obtained regarding quality, processing time, and the number of evaluated functions. The methodology was based on developing a genetic algorithm in Java, which resulted in four different versions: Gp1, Gp2, Gp3 and Gp4 . A set of genetic sequences were processed, and the results were evaluated by analyzing numerical behavior profiles. The research found that algorithms that maintained diversity in the population produced better quality solutions, and parallel processing reduced processing time. It was observed that the time required to perform the process decreased, according to the generated performance profile. The study concluded that conventional computer equipment can produce excellent results when processing genetic information if algorithms are optimized to exploit hardware resources. The computational effort of the hardware used is directly related to the number of evaluated functions. Additionally, the comparison method based on the determination of the performance profile is highlighted as a strategy for comparing the algorithm results in different metrics of interest, which can guide the development of more efficient genetic information processing techniques.

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“…Yet, accelerating applications at the CPU level is still respectable compared to what is required in terms of computing. Therefore, many research fields were benefited from GPU acceleration techniques [ 10 , 14 , 22 , 39 , 40 , 44 ].…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of spatial fuzzy C-means clustering algorithm on GPU for image segmentation

Ali

Abbassi

Bouattane

2022

Multimed Tools Appl

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Image processing by segmentation technique is an important phase in medical imaging such as MRI. Its objective is to analyze the different tissues in human body. In research area, Fuzzy set is one of the most successful techniques that guarantees a robust classification. Spatial FCM (SFCM); one of the fuzzy c-means variants; considers spatial information to deal with the noisy images. To reduce this iterative algorithm’s execution time, a hard SIMD architecture has been planted named the Graphical Processing Unit (GPU). In this work, a great contribution has been done to diagnose, confront and implement three different parallel implementations on GPU. A parallel implementations’ extensive study of SFCM entitled PSFCM using 3 × 3 window is presented, and the experiments illustrate a significant decrease in terms of running time of this algorithm known by its high complexity. The experimental results indicate that the parallel version’s execution time is about 9.46 times faster than the sequential implementation on image segmentation. This gain in terms of speed-up is achieved on the Nvidia GeForce GT 740 m GPU.

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A Review of Parallel Heterogeneous Computing Algorithms in Power Systems

Cited by 12 publications

References 240 publications

Design Space Exploration of HW Accelerators and Network Infrastructure for FPGA-Based MPSoC

Design Space Exploration of HW Accelerators and Network Infrastructure for FPGA-Based MPSoC

Enhancing Multiple Sequence Alignment with Genetic Algorithms: A Bioinformatics Approach in Biomedical Engineering

Performance evaluation of spatial fuzzy C-means clustering algorithm on GPU for image segmentation

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