https://www.mdu.se/

mdu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Space Computing using COTS Heterogeneous Platforms: Intelligent On-Board Data Processing in Space Systems
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-8096-3891
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Space computing enriches space activities such as deep-space explorations and in-orbit intelligent decision making. The awareness of space computing is growing due to the technological advances of high-performance commercial off-the-shelf (COTS) computing platforms. Space offers a complex, constrained and challengeable environment to the developers, researchers, as well as human beings. The challenges are size, weight and power (SWaP) constraints, real-time requirements, communication limitations as well as radiation effects. The research conducted in this thesis aims at investigating and supporting intelligent on-board data processing using COTS heterogeneous computing platforms in space systems. These platforms embed at least one Central Processing Unit (CPU) and one Graphics Processing Unit (GPU) on the same chip. 

The main goal of the research presented in this thesis is twofold. First, to investigate the heterogeneous computing platforms for the purpose of proposing a solution to tackle the above-mentioned challenges in space systems. Second, to complement the proposed solution with novel scheduling techniques for real-time applications that run on COTS heterogeneous platforms under harsh environments like space.

The proposed techniques are based on the system model that considers the use of alternative executions of parallel segments of tasks. Although offloading a parallel segment to a parallel computation unit (such as GPU) improves the best-case execution times of most applications, it can increase the response times of tasks in some applications due to the overuse of GPU. Hence, the use of the proposed task model can be a key to decrease the response times of tasks and improve schedulability of the system. The server-based proposed scheduling techniques support the proposed task model by guaranteeing the execution slot for parallel segments on CPU(s). 

The experimental evaluation conducted in this thesis shows that the proposed task model can improve the schedulability of the real-time systems up to 90% with the static allocation of applications. Moreover, the dynamic allocation method using the server-based scheduling with the proposed task model can improve the schedulability up to 16%. Finally, the thesis presents a simulation tool that simulates real-time applications using the proposed task model while considering the different levels of radiation tolerance to different processing units.

Abstract [sv]

Rymddata berikar rymdaktiviteter som utforskningar i djupa rymden och intelligent beslutsfattande i omloppsbana. Medvetenheten om rymddatorn ökar på grund av de tekniska framstegen inom högpresterande commercial off-the-shelf (COTS). Utrymme erbjuder utvecklare, forskare och människor en komplex, begränsad och utmanande miljö. Utmaningarna är storlek, vikt och effekt (SWaP), realtidskrav, kommunikationsbegränsningar samt strålningseffekter. Forskningen som bedrivs i denna avhandling syftar till att undersöka och stödja intelligent omborddatabehandling med hjälp av COTS heterogena datorplattformar i rymdsystem. Dessa plattformar bäddar in minst en Central Processing Unit (CPU) och en Graphics Processing Unit (GPU) på samma chip.

Huvudmålet för den forskning som presenteras i denna avhandling är tvåfaldigt. För det första att undersöka de heterogena dataplattformarna i syfte att föreslå en lösning för att hantera ovan nämnda utmaningar i rymdsystem. För det andra, för att komplettera den föreslagna lösningen med nya schemaläggningstekniker för realtidsapplikationer som körs på COTS heterogena plattformar under tuffa miljöer som rymden.

De föreslagna teknikerna är baserade på systemmodellen som överväger användningen av alternativa utföranden av parallella segment av uppgifter. Även om avlastning av ett parallellt segment till en parallell beräkningsenhet (t.ex. GPU) förbättrar de bästa tillämpningstiderna för de flesta applikationer, kan det öka svarstiderna för uppgifter i vissa applikationer på grund av överanvändning av GPU. Därför kan användningen av den föreslagna uppgiftsmodellen vara en nyckel för att minska responstiderna för uppgifter och förbättra systemets schemaläggning. De serverbaserade föreslagna schemaläggningsteknikerna stöder den föreslagna uppgiftsmodellen genom att garantera exekveringsplatsen för parallella segment på CPU (er).

Den experimentella utvärderingen som utförs i denna avhandling visar att den föreslagna uppgiftsmodellen kan förbättra schemaläggningen för realtidssystem upp till 90% med statisk tilldelning av applikationer. Dessutom kan den dynamiska tilldelningsmetoden som använder den serverbaserade schemaläggningen med den föreslagna uppgiftsmodellen förbättra schemaläggningen med upp till 16%. Slutligen presenterar avhandlingen ett simuleringsverktyg som simulerar applikationer i realtid med hjälp av den föreslagna uppgiftsmodellen samtidigt som man beaktar de olika nivåerna av strålningstolerans för olika behandlingsenheter.

Place, publisher, year, edition, pages
Västerås: Mälardalen University , 2021.
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 347
Keywords [en]
space computing, CPU-GPU heterogeneous computing, intelligent on-board data processing
National Category
Computer Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:mdh:diva-56086ISBN: 978-91-7485-528-9 (print)OAI: oai:DiVA.org:mdh-56086DiVA, id: diva2:1599803
Public defence
2021-11-18, Alfa, Mälardalens högskola, Västerås, 13:15 (English)
Opponent
Supervisors
Available from: 2021-10-08 Created: 2021-10-01 Last updated: 2021-10-28Bibliographically approved
List of papers
1. Intelligent Data Processing using In-Orbit Advanced Algorithms on Heterogeneous System Architecture
Open this publication in new window or tab >>Intelligent Data Processing using In-Orbit Advanced Algorithms on Heterogeneous System Architecture
2018 (English)In: IEEE Aerospace Conference 2018 IEEEAC2018, 2018, p. 1-8Conference paper, Published paper (Refereed)
Abstract [en]

In recent years, commercial exploitation of small satellites and CubeSats has rapidly increased. Time to market of processed customer data products is becoming an important differentiator between solution providers and satellite constellation operators. Timely and accurate data dissemination is the key to success in the commercial usage of small satellite constellations which is ultimately dependent on a high degree of autonomous fleet management and automated decision support. The traditional way for disseminating data is limited by on the communication capability of the satellite and the ground terminal availability. Even though cloud computing solutions on the ground offer high analytical performance, getting the data from the space infrastructure to the ground servers poses a bottleneck of data analysis and distribution. On the other hand, adopting advanced and intelligent algorithms onboard offers the ability of autonomy, tasking of operations, and fast customer generation of low latency conclusions, or even real-time communication with assets on the ground or other sensors in a multi-sensor configuration. In this paper, the advantages of intelligent onboard processing using advanced algorithms for Heterogeneous System Architecture (HSA) compliant onboard data processing systems are explored. The onboard data processing architecture is designed to handle a large amount of high-speed streaming data and provides hardware redundancy to be qualified for the space mission application domain. We conduct an experimental study to evaluate the performance analysis by using image recognition algorithms based on an open source intelligent machine library 'MIOpen' and an open standard 'OpenVX'. OpenVX is a cross-platform computer vision library.

Series
IEEE Aerospace Conference Proceedings, ISSN 1095-323X
Keywords
Heterogeneous System Architecture (HSA)Intelligent Data ProcessingMIOpenOpenVXCubeSatCPU-GPUEnergy consumption
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-38628 (URN)10.1109/AERO.2018.8396536 (DOI)000474397401066 ()2-s2.0-85049840022 (Scopus ID)
Conference
IEEE Aerospace Conference 2018 IEEEAC2018, 03 Mar 2018, Big Sky, United States
Projects
DPAC - Dependable Platforms for Autonomous systems and Control
Available from: 2018-03-06 Created: 2018-03-06 Last updated: 2021-10-01Bibliographically approved
2. A Trade-Off between Computing Power and Energy Consumption of On-Board Data Processing in GPU Accelerated In-Orbit Space Systems
Open this publication in new window or tab >>A Trade-Off between Computing Power and Energy Consumption of On-Board Data Processing in GPU Accelerated In-Orbit Space Systems
2021 (English)In: Transactions of the Japan Society for Aeronautical and Space Sciences, Aerospace Technology Japan, E-ISSN 1884-0485, Vol. 19, no 5, p. 700-708, article id 19.700Article in journal (Refereed) Published
Abstract [en]

On-board data processing is one of the prior on-orbit activities that improves the performance capability of in-orbit space systems such as deep-space exploration, earth and atmospheric observation satellites, and CubeSat constellations. However, on-board data processing encounters higher energy consumption compared to traditional on-board space systems. This is because the traditional space systems employ simple processing units such as single-core microprocessors as the systems do not require heavy data processing. Moreover, solving the radiation hardness problem is crucial in space, and adopting a new processing unit is challenging.

In this paper, we consider a Graphics Processing Unit (GPU) accelerated in-orbit space system for on-board data processing. According to prior works, there exist radiation-tolerant GPU, and the computing capability of systems is improved by using heterogeneous computing method. We conduct experimental observations of energy consumption and computing potential using this heterogeneous computing method in our GPU accelerated in-orbit space systems.The results show that the proper use of GPU increases computing potential with 10-140 times and consumes between 8-130 times less energy. Furthermore, the entire task system consumes 10-65% of less energy compared to the traditional use of processing units.

Keywords
On-board Data Processing, Heterogeneous Computing, Energy Efficiency, GPU Accelerated On-board Computer
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-56078 (URN)10.2322/tastj.19.700 (DOI)
Available from: 2021-10-01 Created: 2021-10-01 Last updated: 2021-10-22Bibliographically approved
3. Enabling radiation tolerant heterogeneous GPU-based onboard data processing in space
Open this publication in new window or tab >>Enabling radiation tolerant heterogeneous GPU-based onboard data processing in space
Show others...
2020 (English)In: CEAS Space Journal, ISSN 1868-2502, E-ISSN 1868-2510, Vol. 12, no 4, p. 551-564Article in journal (Refereed) Published
Abstract [en]

The last decade has seen a dramatic increase in small satellite missions for commercial, public, and government intelligence applications. Given the rapid commercialization of constellation-driven services in Earth Observation, situational domain awareness, communications including machine-to-machine interface, exploration etc., small satellites represent an enabling technology for a large growth market generating truly Big Data. Examples of modern sensors that can generate very large amounts of data are optical sensing, hyperspectral, Synthetic Aperture Radar (SAR), and Infrared imaging. Traditional handling and downloading of Big Data from space requires a large onboard mass storage and high bandwidth downlink with a trend towards optical links. Many missions and applications can benefit significantly from onboard cloud computing similarly to Earth-based cloud services. Hence, enabling space systems to provide near real-time data and enable low latency distribution of critical and time sensitive information to users. In addition, the downlink capability can be more effectively utilized by applying more onboard processing to reduce the data and create high value information products. This paper discusses current implementations and roadmap for leveraging high performance computing tools and methods on small satellites with radiation tolerant hardware. This includes runtime analysis with benchmarks of convolutional neural networks and matrix multiplications using industry standard tools (e.g., TensorFlow and PlaidML). In addition, a 1/2 CubeSat volume unit (0.5U) (10 x 10 x 5 cm(3)) cloud computing solution, called SpaceCloud (TM) iX5100 based on AMD 28 nm APU technology is presented as an example of heterogeneous computer solution. An evaluation of the AMD 14 nm Ryzen APU is presented as a candidate for future advanced onboard processing for space vehicles.

Place, publisher, year, edition, pages
SPRINGER WIEN, 2020
Keywords
OBDP, Machine learning, GPU, Small satellites, Heterogeneous computing
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-50616 (URN)10.1007/s12567-020-00321-9 (DOI)000541029200001 ()2-s2.0-85086737276 (Scopus ID)
Available from: 2020-09-21 Created: 2020-09-21 Last updated: 2021-10-01Bibliographically approved
4. Simulation and Analysis of In-Orbit Applications under Radiation Effects on COTS Platforms
Open this publication in new window or tab >>Simulation and Analysis of In-Orbit Applications under Radiation Effects on COTS Platforms
Show others...
2021 (English)In: 42nd IEEE Aerospace Conference 2021 IEEEAC2021, 2021Conference paper, Published paper (Refereed)
Abstract [en]

Radiation effects research is crucial as it defines risk to both human bodies and spacecraft. Employing radiation-hardened products is one way to mitigate radiation effects on in-orbit systems. However, radiation effects prohibit most of the state-of-the-art commercial off-the-shelf (COTS) technologies from use in space. Furthermore, radiation effects on software components are less studied compared to hardware components. In this work, we introduce a simulation tool that analyzes the impact of radiation effects on schedulability of task sets executing on COTS system-on-chip (SoC) platforms in the in-orbit systems. In order to provide a meaningful verification environment, single-event effects (SEEs) are introduced as aleatory disturbances characterized by probability distribution of occurrence using their predefined models. The tool supports interoperability with several other tools as it uses the extensible markup language (XML) model files for input and output, i.e., for importing input task sets and radiation effects and exporting the simulation results.

Series
IEEE Aerospace Conference Proceedings, ISSN 1095-323X
Keywords
Radiation toleranceCPU-GPUSimulation toolSchedulabilityCOTS components
National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-53949 (URN)10.1109/AERO50100.2021.9438255 (DOI)000681710101024 ()2-s2.0-85111365681 (Scopus ID)
Conference
42nd IEEE Aerospace Conference 2021 IEEEAC2021, 06 Mar 2021, Big Sky, Montana, United States
Projects
DPAC - Dependable Platforms for Autonomous systems and Control
Available from: 2021-05-24 Created: 2021-05-24 Last updated: 2021-11-05Bibliographically approved
5. Static Allocation of Parallel Tasks to Improve Schedulability in CPU-GPU Heterogeneous Real-Time Systems
Open this publication in new window or tab >>Static Allocation of Parallel Tasks to Improve Schedulability in CPU-GPU Heterogeneous Real-Time Systems
Show others...
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Autonomous driving is one of the main challenges of modern cars. Computer visions and intelligent on-board decision making are crucial in autonomous driving and require heterogeneous processors with high computing capability under low power consumption constraints. The progress of parallel computing using heterogeneous processing units is further supported by software frameworks like OpenCL, OpenMP, CUDA, and C++AMP. These frameworks allow the allocation of parallel computation on different compute resources. This, however, creates a difficulty in allocating the right computation segments to the right processing units in such a way that the complete system meets all its timing requirements. In this paper, we consider pre-runtime static allocations of parallel tasks to perform their execution either sequentially on CPU or in parallel using a GPU. This allows for improving any unbalanced use of GPU accelerators in a heterogeneous environment. By performing several heuristic algorithms, we show that the overuse of accelerators results in a bottle-neck of the entire system execution. The experimental results show that our allocation schemes that target a balanced use of GPU improve the system schedulability up to 90%.

Keywords
Parallel task, Parallel segment, Alternative execution, CPU-GPU, Heterogeneous processors, Real-time systems
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-45934 (URN)10.1109/IECON.2019.8926767 (DOI)000522050604083 ()2-s2.0-85084110257 (Scopus ID)9781728148786 (ISBN)
Conference
IEEE 45th Annual Conference of the Industrial Electronics Society, IECON2019
Projects
DPAC - Dependable Platforms for Autonomous systems and Control
Available from: 2019-11-11 Created: 2019-11-11 Last updated: 2021-10-01Bibliographically approved
6. Offloading Accelerator-intensive Workloads in CPU-GPU Heterogeneous Processors
Open this publication in new window or tab >>Offloading Accelerator-intensive Workloads in CPU-GPU Heterogeneous Processors
Show others...
2021 (English)In: 26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021, 2021Conference paper, Published paper (Refereed)
Abstract [en]

Autonomous vehicular systems require computer vision and intelligent on-board decision making functionalities that include a mix of sequential and parallel workloads. The execution times of the workloads and power consumption in these functionalities can be lowered by utilizing the accelerators (e.g., GPU) instead of running the workloads entirely on the host processing units (CPU). However, allocating all the parallelizable workload to accelerators can create a computation bottleneck in the accelerators that, in turn, can have an adverse effect on schedulability of the systems. This paper presents a novel framework that can allocate the accelerate-intensive workloads to the accelerators as well as to the non-accelerated host processing units. Within the context of this framework, the paper introduces five offloading techniques to mitigate the accelerator-intensive workloads by utilizing excess capacity of non-accelerated processing units under dynamic scheduling in CPU-GPU heterogeneous processors. The proposed techniques are evaluated using simulation experiments. The evaluation results indicate that one of the proposed techniques can achieve up to 16% improvement in schedulability of the task sets compared to the traditional non-offloading technique.

National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-56081 (URN)10.1109/ETFA45728.2021.9613666 (DOI)000766992600230 ()2-s2.0-85122955086 (Scopus ID)9781728129891 (ISBN)
Conference
26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021; Virtual, Vasteras 7 September 2021 through 10 September 2021
Available from: 2021-10-01 Created: 2021-10-01 Last updated: 2022-11-08Bibliographically approved

Open Access in DiVA

fulltext(1379 kB)640 downloads
File information
File name FULLTEXT03.pdfFile size 1379 kBChecksum SHA-512
106d18e4f1ca5d3576a9d899439547b29fe61192e4e3f89f373ac8717e78f2e638e44fb9e6e60b08afb7ce7523e7f8db43be9b44a83eb34cf461534eb369294a
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Tsog, Nandinbaatar
By organisation
Embedded Systems
Computer Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 640 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 966 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf