The shift to multicores in real-time and safety-critical systems

Dimensionality reduction represents a critical preprocessing step in order to increase the efficiency and the performance of many hyperspectral imaging algorithms. However, dimensionality reduction algorithms, such as the Principal Component Analysis (PCA), suffer from their computationally demanding nature, becoming advisable for their implementation onto high-performance computer architectures for applications under strict latency constraints. This work presents the implementation of the PCA algorithm onto two different high-performance devices, namely, an NVIDIA Graphics Processing Unit (GPU) and a Kalray manycore, uncovering a highly valuable set of tips and tricks in order to take full advantage of the inherent parallelism of these high-performance computing platforms, and hence, reducing the time that is required to process a given hyperspectral image. Moreover, the achieved results obtained with different hyperspectral images have been compared with the ones that were obtained with a field programmable gate array (FPGA)-based implementation of the PCA algorithm that has been recently published, providing, for the first time in the literature, a comprehensive analysis in order to highlight the pros and cons of each option.

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“…Processing cores 256 Total amount of global memory (MB) 32 MPPA clock rate (MHz) 400 Typical power consumption (W) [28] 10-20…”

Section: Mppa-256-nmentioning

confidence: 99%

Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons

et al. 2018

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“…We consider a domain controller model very similar to Kalray MPPA-256 Bostan [5], [33], which is a clustered manycore platform organized as illustrated in Fig. 1.…”

Section: A Platform Modelmentioning

confidence: 99%

“…The parameters of the hardware platform are chosen based on the Kalray MPPA-256 Bostan as presented in [35], [33]. Each local memory bank has a capacity of 128 KB.…”

Section: Case Studymentioning

confidence: 99%

Contention-Free Execution of Automotive Applications on a Clustered Many-Core Platform

Becker

Dasari

Nicolic

et al. 2016

2016 28th Euromicro Conference on Real-Time Systems (ECRTS)

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Abstract-Next generations of compute-intensive real-time applications in automotive systems will require more powerful computing platforms. One promising power-efficient solution for such applications is to use clustered many-core architectures. However, ensuring that real-time requirements are satisfied in the presence of contention in shared resources, such as memories, remains an open issue.This work presents a novel contention-free execution framework to execute automotive applications on such platforms. Privatization of memory banks together with defined access phases to shared memory resources is the backbone of the framework. An Integer Linear Programming (ILP) formulation is presented to find the optimal time-triggered schedule for the on-core execution as well as for the access to shared memory. Additionally a heuristic solution is presented that generates the schedule in a fraction of the time required by the ILP. Extensive evaluations show that the proposed heuristic performs only 0.5% away from the optimal solution while it outperforms a baseline heuristic by 67%. The applicability of the approach to industrially sized problems is demonstrated in a case study of a software for Engine Management Systems.

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“…In the field of high-performance computing (HPC) and cloud computing, on the other hand, the scalability problems in tradi- 1 Graduate School of Information Science, Nagoya University, Nagoya, Aichi 464-0814, Japan a) liyixiao@ertl.jp b) yutaka@ertl.jp c) hiro@ertl.jp tional general-purpose operating system (GPOS) have been actively researched for decades. Linux, the de facto standard kernel for HPC and cloud servers, has been considered as a bottleneck for processors with a huge number (hundreds to thousands) of cores.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the embedded systems are based on a multi-core real-time operating system (RTOS) because they usually include applications with real-time constraints. Previous studies of multi-core RTOS have been mainly focused on the schedulability and response time analysis of task sets on multi-core processors [1], [2]. Meanwhile, many researchers have claimed that high-end embedded systems in the near future will also include high-performance parallel applications in order to support complex tasks like autonomous driving of vehicles [3].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of RTOS and Linux Scalability on an Embedded Many-core Processor

Matsubara

Takada

2018

Journal of Information Processing

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Current embedded systems are usually based on real-time operating system (RTOS). In the near future, embedded systems will include parallel applications for tasks like autonomous driving, and adopt many-core processors to satisfy the performance requirements. However, traditional RTOSes are not designed for high performance applications and whether they can scale well on many-core processors remains unclear. Meanwhile, research has shown that Linux can provide good scalability for processors with tens of cores. In this paper, an experiment environment based on a traditional multi-core RTOS (TOPPERS/FMP) and an off-the-shelf 72-core many-core processor (TILE-Gx72) is presented. By a comparative analysis of RTOS based and Linux based runtime systems, several bottlenecks in RTOS are identified and the methods to avoid them are proposed. After that, the PARSEC benchmark suite is used to evaluate the performance of RTOS and Linux. The results show that the optimized RTOS runtime system tends to deliver better scalability than Linux in many cases. Therefore, we believe that traditional RTOS like TOPPERS/FMP can still be a good choice for embedded many-core processors in the near future.

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The shift to multicores in real-time and safety-critical systems

Cited by 41 publications

References 12 publications

Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons

Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons

Contention-Free Execution of Automotive Applications on a Clustered Many-Core Platform

A Comparative Analysis of RTOS and Linux Scalability on an Embedded Many-core Processor

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