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General Purpose Computing on Low-Power Embedded GPUs: Has It Come of Age?
Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska högskolan.
Linköpings universitet, Institutionen för datavetenskap, ESLAB - Laboratoriet för inbyggda system. Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska högskolan.
Linköpings universitet, Institutionen för datavetenskap, ESLAB - Laboratoriet för inbyggda system. Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska högskolan.
Linköpings universitet, Institutionen för datavetenskap, ESLAB - Laboratoriet för inbyggda system. Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska högskolan.
2013 (Engelska)Ingår i: 13th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS 2013), Samos, Greece, July 15-18, 2013., IEEE Press, 2013Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

In this paper we evaluate the promise held by low power GPUs for non-graphic workloads that arise in embedded systems. Towards this, we map and implement 5 benchmarks, that find utility in very different application domains, to an embedded GPU. Our results show that apart from accelerated performance, embedded GPUs are promising also because of their energy efficiency which is an important design goal for battery-driven mobile devices. We show that adopting the same optimization strategies as those used for programming high-end GPUs might lead to worse performance on embedded GPUs. This is due to restricted features of embedded GPUs, such as, limited or no user-defined memory, small instruction-set, limited number of registers, among others. We propose techniques to overcome such challenges, e.g., by distributing the workload between GPUs and multi-core CPUs, similar to the spirit of heterogeneous computation.

Ort, förlag, år, upplaga, sidor
IEEE Press, 2013.
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:liu:diva-92626DOI: 10.1109/SAMOS.2013.6621099ISI: 000332458100004OAI: oai:DiVA.org:liu-92626DiVA, id: diva2:621316
Konferens
SAMOS'13
Tillgänglig från: 2013-05-14 Skapad: 2013-05-14 Senast uppdaterad: 2018-12-07
Ingår i avhandling
1. System-Level Design of GPU-Based Embedded Systems
Öppna denna publikation i ny flik eller fönster >>System-Level Design of GPU-Based Embedded Systems
2018 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Modern embedded systems deploy several hardware accelerators, in a heterogeneous manner, to deliver high-performance computing. Among such devices, graphics processing units (GPUs) have earned a prominent position by virtue of their immense computing power. However, a system design that relies on sheer throughput of GPUs is often incapable of satisfying the strict power- and time-related constraints faced by the embedded systems.

This thesis presents several system-level software techniques to optimize the design of GPU-based embedded systems under various graphics and non-graphics applications. As compared to the conventional application-level optimizations, the system-wide view of our proposed techniques brings about several advantages: First, it allows for fully incorporating the limitations and requirements of the various system parts in the design process. Second, it can unveil optimization opportunities through exposing the information flow between the processing components. Third, the techniques are generally applicable to a wide range of applications with similar characteristics. In addition, multiple system-level techniques can be combined together or with application-level techniques to further improve the performance.

We begin by studying some of the unique attributes of GPU-based embedded systems and discussing several factors that distinguish the design of these systems from that of the conventional high-end GPU-based systems. We then proceed to develop two techniques that address an important challenge in the design of GPU-based embedded systems from different perspectives. The challenge arises from the fact that GPUs require a large amount of workload to be present at runtime in order to deliver a high throughput. However, for some embedded applications, collecting large batches of input data requires an unacceptable waiting time, prompting a trade-off between throughput and latency. We also develop an optimization technique for GPU-based applications to address the memory bottleneck issue by utilizing the GPU L2 cache to shorten data access time. Moreover, in the area of graphics applications, and in particular with a focus on mobile games, we propose a power management scheme to reduce the GPU power consumption by dynamically adjusting the display resolution, while considering the user's visual perception at various resolutions. We also discuss the collective impact of the proposed techniques in tackling the design challenges of emerging complex systems.

The proposed techniques are assessed by real-life experimentations on GPU-based hardware platforms, which demonstrate the superior performance of our approaches as compared to the state-of-the-art techniques.

Ort, förlag, år, upplaga, sidor
Linköping: Linköping University Electronic Press, 2018. s. 62
Serie
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1964
Nyckelord
GPU, GPGPU, embedded system, heterogeneous computing, system-level design
Nationell ämneskategori
Inbäddad systemteknik
Identifikatorer
urn:nbn:se:liu:diva-152469 (URN)10.3384/diss.diva-152469 (DOI)9789176851753 (ISBN)
Disputation
2018-12-19, Nobel BL32, B-Huset, Campus Valla, Linköping, 13:15 (Engelska)
Opponent
Handledare
Forskningsfinansiär
CUGS (National Graduate School in Computer Science), 995523
Tillgänglig från: 2018-12-07 Skapad: 2018-12-07 Senast uppdaterad: 2019-09-30Bibliografiskt granskad

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