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Enabling radiation tolerant heterogeneous GPU-based onboard data processing in space
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Unibap AB Publ, Uppsala, Sweden.;Bruhnspace AB, Uppsala, Sweden..ORCID iD: 0000-0002-8785-5380
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-8096-3891
Unibap AB Publ, Uppsala, Sweden..
Unibap AB Publ, Uppsala, Sweden..
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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. Vol. 12, no 4, p. 551-564
Keywords [en]
OBDP, Machine learning, GPU, Small satellites, Heterogeneous computing
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-50616DOI: 10.1007/s12567-020-00321-9ISI: 000541029200001Scopus ID: 2-s2.0-85086737276OAI: oai:DiVA.org:mdh-50616DiVA, id: diva2:1469167
Available from: 2020-09-21 Created: 2020-09-21 Last updated: 2021-10-01Bibliographically approved
In thesis
1. Space Computing using COTS Heterogeneous Platforms: Intelligent On-Board Data Processing in Space Systems
Open this publication in new window or tab >>Space Computing using COTS Heterogeneous Platforms: Intelligent On-Board Data Processing in Space Systems
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
space computing, CPU-GPU heterogeneous computing, intelligent on-board data processing
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-56086 (URN)978-91-7485-528-9 (ISBN)
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

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Bruhn, FredrikTsog, Nandinbaatar

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