Inhalt des Dokuments
Beschreibung
[2]
- © LPGPU2
Low-power GPUs have become ubiquitous. They can be
found in domains ranging from wearable and mobile computing, to
automotive systems. This places an ever increasing demand on the
expected performance and power efficiency of the devices.
Future
low-power system-on-chips will have to provide higher performance and
be able to support more complex applications, without using additional
power.
These demands cannot be met through hardware improvements
alone, but the software must fully exploit the available resources.
Unfortunately, application developers are seriously hindered when
creating low-power GPU software by the limited quality of current
performance analysis tools. In low-power GPU contexts there is only a
minimal amount of performance information, and essentially no power
information, available to the programmer. As software becomes more
complex it becomes increasingly unmanageable for programmers to
optimise the software for low-power devices.
This project
proposes to aid the application developer in creating software for
low-power GPUs by building on the results of the first LPGPU project
by providing a complete performance and power analysis process for the
programmer. This project will address all aspects of performance
analysis, from hardware power and performance counters, to a framework
that processes and visualises information from these counters, to
applications that will be used as use-cases to drive the entire
design. To access the new hardware performance counters a
standardisable API will be produced to interface to a prototype
hardware implementation. This will let the analysis and visualisation
framework connect to any GPU driver that implements the API. The
consortium’s expertise will be used to drive the initial design of
the API and analyses, but multiple application use-cases will also be
used to inform further iterations. This use-case driven approach will
result in a performance and power optimisation framework that allows
programmers to optimise applications in domains where there is a
genuine need.
For more information: lpgpu.org [3]
Beteilligte Personen
AES group
principle investigator: | Prof. Dr.
Ben Juurlink [4] |
---|---|
LPGPU Technical lead: | Jan Lucas [5]
|
PhD
students: | Jan Lucas [6] Sohan Lal [7] Nadjib Mammeri [8] |
Master student: | Felix Goroncy
[9] |
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