High performance implementations for Ada interrupts in real-time systems

1988 ◽  
Author(s):  
D. Nelson-Gal
Keyword(s):  
Real Time ◽  
High Performance ◽  
Real Time Systems ◽  
Time Systems

Implementation and Comparative Analysis of k-means and Fuzzy c-means Clustering Algorithms for Tasks Allocation in Distributed Real Time System

2019 ◽  
Vol 10 (2) ◽  
pp. 66-86 ◽  
Author(s):  
Harendra Kumar ◽  
Isha Tyagi
Keyword(s):  
Real Time ◽  
High Performance ◽  
Distributed Processing ◽  
Clustering Algorithms ◽  
Processing System ◽  
Real Time Systems ◽  
Real Time System ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Time Systems

Distributing tasks to processors in distributed real time systems is an important step for obtaining high performance. Scheduling algorithms play a vital role in achieving better performance and high throughput in heterogeneous distributed real time systems. To make the best use of the computational power available, it is essential to assign the tasks to the processor whose characteristics are most appropriate for the execution of the tasks in a distributed processing system. This study develops two algorithms for clustering the heavily-communicating tasks to reduce the inter-tasks communication costs by using k-means and fuzzy c-means clustering techniques respectively. In order to minimize the system cost and response time, an algorithm is developed for the proper allocation of formed clusters to the most suitable processor. The present algorithms are collated with problems in literature. The proposed algorithms are formulated and applied to numerous numerical examples to demonstrate their effectiveness.

scholarly journals Ideal and Predictable Hit Ratio for Matrix Transposition in Data Caches

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 184
Author(s):  
Alba Pedro-Zapater ◽  
Clemente Rodríguez ◽  
Juan Segarra ◽  
Rubén Gran Tejero ◽  
Víctor Viñals-Yúfera
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
High Performance ◽  
Negative Impact ◽  
Data Cache ◽  
Real Time Systems ◽  
Worst Case ◽  
Tile Size ◽  
Matrix Transposition ◽  
Time Systems

Matrix transposition is a fundamental operation, but it may present a very low and hardly predictable data cache hit ratio for large matrices. Safe (worst-case) hit ratio predictability is required in real-time systems. In this paper, we obtain the relations among the cache parameters that guarantee the ideal (predictable) data hit ratio assuming a Least-Recently-Used (LRU) data cache. Considering our analytical assessments, we compare a tiling matrix transposition to a cache oblivious algorithm, modified with phantom padding to improve its data hit ratio. Our results show that, with an adequate tile size, the tiling version results in an equal or better data hit ratio. We also analyze the energy consumption and execution time of matrix transposition on real hardware with pseudo-LRU (PLRU) caches. Our analytical hit/miss assessment enables the usage of a data cache for matrix transposition in real-time systems, since the number of misses in the worst case is bound. In general and high-performance computation, our analysis enables us to restrict the cache resources devoted to matrix transposition with no negative impact, in order to reduce both the energy consumption and the pollution to other computations.

Comparison of Face Detection and Recognition Algorithms in Real-Time Video

2020 ◽  
Author(s):  
Alejandra Sarahi Sanchez-Moreno ◽  
Hector Manuel Perez-Meana ◽  
Jesus Olivares-Mercado ◽  
Gabriel Sanchez-Perez ◽  
Karina Toscano-Medina
Keyword(s):  
Real Time ◽  
Face Detection ◽  
High Performance ◽  
Facial Recognition ◽  
Rapid Development ◽  
Real Time Systems ◽  
The Face ◽  
Face Detection And Recognition ◽  
Time Systems ◽  
Detection And Recognition

Facial recognition systems has captivated research attention in recent years. Facial recognition technology is often required in real-time systems. With the rapid development, diverse algorithms of machine learning for detection and facial recognition have been proposed to address the challenges existing. In the present paper we proposed a system for facial detection and recognition under unconstrained conditions in video sequences. We analyze learning based and hand-crafted feature extraction approaches that have demonstrated high performance in task of facial recognition. In the proposed system, we compare different traditional algorithms with the avant-garde algorithms of facial recognition based on approaches discussed. The experiments on unconstrained datasets to study the face detection and face recognition show that learning based algorithms achieves a remarkable performance to face the challenges in real-time systems.

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