Real-World Constraints of GPUs in Real-Time Systems

Elliott, Glenn A.; Anderson, James H.

doi:10.1109/rtcsa.2011.46

Cited by 35 publications

(15 citation statements)

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“…While GPUs have been traditionally used for graphics and throughputoriented applications, in recent years the idea of using GPUs to perform real-time processing has been proposed [EA11], [EA12]. In [EA11], the authors explore possible applications for GPUs in real-time systems, summarizing the challenges and limitations, and discussing possible solutions to address them.…”

Section: Real-time Computing On Gpusmentioning

confidence: 99%

“…In [EA11], the authors explore possible applications for GPUs in real-time systems, summarizing the challenges and limitations, and discussing possible solutions to address them. In [EA12], the same authors present two analysis methods that permit the integration of GPUs in soft real-time multiprocessor systems.…”

Section: Real-time Computing On Gpusmentioning

confidence: 99%

“…While the suitability of GPUs for throughput-oriented applications is accepted, their use in a real-time context is an open research issue. There are strong motivations for utilizing GPUs in real-time systems [EA11], [KLRI11]; their use can increase the performance orders of magnitude (thus, possibly improving system responsiveness, important in real-time systems), at a fraction of the power needed by traditional CPUs. FPGAs have been shown to be suitable for data mining [BP05], bioinformatics [HJL + 07], query acceleration for databases [BBZT14], digital signal processing [ASG13], [BGG13], pattern matching and image processing [SBHT13], [dRGLH06], etc.…”

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Hardware/Software Codesign of Embedded Systems with Reconfigurable and Heterogeneous Platforms

Lifa¹

2015

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M odern applications running on today's embedded systems have very high requirements. Most often, these requirements have many dimensions: the applications need high performance as well as flexibility, energyefficiency as well as real-time properties, fault tolerance as well as low cost. In order to meet these demands, the industry is adopting architectures that are more and more heterogeneous and that have reconfiguration capabilities. Unfortunately, this adds to the complexity of designing streamlined applications that can leverage the advantages of such architectures.In this context, it is very important to have appropriate tools and design methodologies for the optimization of such systems. This thesis addresses the topic of hardware/software codesign and optimization of adaptive real-time systems implemented on reconfigurable and heterogeneous platforms. We focus on performance enhancement for dynamically reconfigurable FPGA-based systems, energy minimization in multi-mode real-time systems implemented on heterogeneous platforms, and codesign techniques for fault-tolerant systems.The solutions proposed in this thesis have been validated by extensive experiments, ranging from computer simulations to proof of concept implementations on real-life platforms. The results have confirmed the importance of the addressed aspects and the applicability of our techniques for design optimization of modern embedded systems. v vi Populärvetenskaplig Sammanfattning I dag är inbyggda system vanligt förekommande och deras antal fortsätter att öka. De används inom en mängd olika domäner, t.ex. hemelektronik, fordonsindustri, flygelektronik, medicin, etc., och de hittas nästan överallt omkring oss: från våra telefoner, bärbara datorer och tvättmaskiner till våra bilar. De applikationer som körs på dessa system har flera olika ökande krav: högprestanda, energieffektivitet, flexibilitet, feltolerans, realtidsegenskaper, och naturligtvis låg kostnad. För att kunna uppfylla dessa krav har industrin anammat arkitekturer som är mer och mer heterogena och som har omkonfigureringsmöjligheter. Olyckligtvis ökar detta komplexiteten när man ska utforma effektiva inbyggda system. I detta sammanhang blir det av yttersta betydelse att utveckla effektiva optimeringsmetoder och verktyg.Dagens applikationer består av en blandning av mjukvarukomponenter som har mycket olika energi-och prestandaegenskaper beroende på vilka hårdvaruenheter där de körs på, vilket gör dem lämpliga för heterogena plattformar. En heterogen plattform består av olika typer av hårdvaruen-heter, var och en med sina egenskaper, som riktar sig till vissa tillämpn-ingsområden. Under det senaste decenniet har utvecklingen av rekonfigurerbara hårdvarutekniker accelererat, vilket bidrar till den ökande populariteten för fältprogrammerbara grindmatriser (FPGA). Idag, ger hård-varutillverkare stöd för partiell dynamisk omkonfigurering; detta innebär att delar av en FPGA kan konfigureras vid run-time, medan andra delar förblir fullt fungerande. Dessa tekniska framsteg har...

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Section: Real-time Computing On Gpusmentioning

confidence: 99%

Section: Real-time Computing On Gpusmentioning

confidence: 99%

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Hardware/Software Codesign of Embedded Systems with Reconfigurable and Heterogeneous Platforms

Lifa¹

2015

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“…Furthermore, a system designer may only be able to implement basic safety features under hard realtime (HRT) constraints (no tardiness), yet more complex and robust systems may be possible if stringent requirements are relaxed. 1 These robust systems may indeed be safer than ba- 1 True HRT constraints are problematic in a GPU-enabled system anyway due to hardware complexity in such systems, the closed-source nature of GPU drivers, and the lack of timing analysis tools for such systems [8]. sic ones, despite bounded tardiness.…”

Section: Target Platformmentioning

confidence: 99%

“…GPUSync is made up of several components: a GPU allocator that utilizes a real-time k-exclusion locking protocol; 8 per-GPU real-time mutual exclusion (mutex) engine locks, one for each GPU copy and execution engine, to arbi- trate access to those engines; and API routines that facilitate memory copies and P2P migrations. Fig.…”

Section: Gpusyncmentioning

confidence: 99%

GPUSync: A Framework for Real-Time GPU Management

Elliott

Ward²,

Anderson³

2013

2013 IEEE 34th Real-Time Systems Symposium

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107

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The integration of graphics processing units (GPUs) into real-time systems has recently become an active area of research. However, prior research on this topic has failed to produce real-time GPU allocation methods that fully exploit the available parallelism in GPU-enabled systems. In this paper, a GPU management framework called GPUSync is described that enables increased parallelism while providing predictable real-time behavior. GPUSync can be applied in multi-GPU real-time systems, is cognizant of the system bus architecture, and fully exposes the parallelism offered by modern GPUs, even when closed-source GPU drivers are used. Schedulability tests presented herein that incorporate empirically measured overheads, including those due to bus contention, demonstrate that GPUSync offers real-time predictability and performs well in practice.

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