Active Data Structures on GPGPUs

O'Donnell, John T.; Hall, Cordelia; Monro, Stuart

doi:10.1007/978-3-642-54420-0_85

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…The concept of the Active Data structure was originally proposed by G. Andrews, D. Dobkin, and P. Downey [ 25 ], in opposition to classical data structures, considered to by passive software agents. The idea was formulated in the area of computer architectures, in 1981, as an innovative mechanism to address the new challenges brought about by the multiprocessors era.…”

Section: Previous Workmentioning

confidence: 99%

Code Integrity and Confidentiality: An Active Data Approach for Active and Healthy Ageing

Litvinov

Llumiguano

Santofimia

et al. 2023

Sensors

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Internet of Things cybersecurity is gaining attention as the number of devices installed in IoT environments is exponentially increasing while the number of attacks successfully addressed to these devices are also proliferating. Security concerns have, however, been mainly addressed to service availability and information integrity and confidentiality. Code integrity, on the other hand, is not receiving proper attention, mainly because of the limited resources of these devices, thus preventing the implementation of advanced protection mechanisms. This situation calls for further research on how traditional mechanisms for code integrity can be adapted to IoT devices. This work presents a mechanism for code integrity in IoT devices based on a virtual-machine approach. A proof-of-concept virtual machine is presented, specially designed for providing code integrity during firmware updates. The proposed approach has been experimentally validated in terms of resource consumption among the most-widespread micro-controller units. The obtained results demonstrate the feasibility of this robust mechanism for code integrity.

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Section: Previous Workmentioning

confidence: 99%

Code Integrity and Confidentiality: An Active Data Approach for Active and Healthy Ageing

Litvinov

Llumiguano

Santofimia

et al. 2023

Sensors

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Pointlessness is Better than Listlessness

O'Donnell

Hall

2016

A List of Successes That Can Change the World

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Efficient Implementation of Genetic Algorithms on GP-GPU with Scheduled Persistent CUDA Threads

Capodieci

Burgio

2015

2015 Seventh International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)

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In this paper we present a heavily exploration oriented implementation of genetic algorithms to be executed on graphic processor units (GPUs) that is optimized with our novel mechanism for scheduling GPU-side synchronized jobs that takes inspiration from the concept of persistent threads. Persistent Threads allow an efficient distribution of work loads throughout the GPU so to fully exploit the CUDA (NVIDIA's proprietary Compute Unified Device Architecture) architecture. Our approach (named Scheduled Light Kernel, SLK) uses a specifically designed data structure for issuing sequences of commands from the CPU to the GPU able to minimize CPUGPU communications, exploit streams of concurrent execution of different device side functions within different Streaming Multiprocessors and minimize kernels launch overhead. Results obtained on two completely different experimental settings show that our approach is able to dramatically increase the performance of the tested genetic algorithms compared to the baseline implementation that (while still running on a GPU) does not exploit our proposed approach. Our proposed SLK approach does not require substantial code rewriting and is also compared to newly introduced features in the last CUDA development toolkit, such as nested kernel invocations for dynamic parallelism

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Active Data Structures on GPGPUs

Cited by 3 publications

References 13 publications

Code Integrity and Confidentiality: An Active Data Approach for Active and Healthy Ageing

Code Integrity and Confidentiality: An Active Data Approach for Active and Healthy Ageing

Pointlessness is Better than Listlessness

Efficient Implementation of Genetic Algorithms on GP-GPU with Scheduled Persistent CUDA Threads

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