- author = "Arian Maghazeh",
- title = "System-Level Design of {GPU}-Based Embedded Systems",
- school = "Linkoping University, Faculty of Science \& Engineering",
- year = "2018",
- address = "Sweden",
- keywords = "genetic algorithms, genetic programming, GPU, GPGPU, embedded system, heterogeneous computing, system-level design",
- ISSN = "0345-7524",
- isbn13 = "9789176851753",
- series = "Linkoping Studies in Science and Technology. Dissertations",
- number = "1964",
-
URL = "
https://books.google.co.uk/books/about/System_Level_Design_of_GPU_Based_Embedde.html?id=139-DwAAQBAJ&redir_esc=y",
-
URL = "
http://liu.diva-portal.org/smash/get/diva2:1268932/FULLTEXT02.pdf",
-
DOI = "
doi:10.3384/diss.diva-152469",
- size = "62 pages",
-
abstract = "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 using 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.",
-
notes = "Limited mention of GP
Supervisors: Zebo Peng, Petru Ion Eles, Unmesh D. Bordoloi",
