AUTOMATIC ONLINE TUNING
AutoTune
Key innovation
Performance analysis and tuning is an important step in programming multicore-based parallel architectures ranging from small clusters with GPUs
(Graphics Processing Units) such as modern high-end servers as used by
Google up to supercomputers (HPC) such as the new supercomputer
SuperMUC at Leibniz Computing Centre, Garching.
While performance analysis tools exist that help the developer in analysing
the application performance, these tools do not give any recommendations
how to tune the code. AutoTune will extend Periscope, an open source
automatic online and distributed performance analysis tool developed by
Technische Universität München, with automatic online tuning plugins for
performance and energy efficiency tuning. The resulting Periscope Tuning
Framework will be able to tune serial and parallel codes with or without
GPU kernels and will return tuning recommendations that can be integrated
into the production version of the code. The whole tuning process,
consisting of automatic performance analysis and automatic tuning, will be
executed online, i.e. during a single run of the application. This new
productivity tool for parallel programming will increase multi-core
programming efficiency by orders of magnitude. Moreover, an energy
saving up to 10% can be expected.
The technical approach
AutoTune will develop the Periscope
Tuning Framework as an extension of
Periscope. It will follow the main
Periscope principles, i.e., use of
formalised expert knowledge in form of
properties and strategies, automatic
execution, online search based on
program
phases,
and
distributed
processing. Periscope will be extended by
a number of tuning plugins that fall into
two categories: online and semi-online
plugins. An online tuning plugin will
perform transformations to the application
and/or the execution environment without
requiring a restart of the application. A
semi-online tuning plugin will be based
on a restart of the application but without
re-starting Periscope.
Contract number
288038
Project coordinator
Technische Universität
München
Contact person
Michael Gerndt
TUM
Boltzmannstr. 3
D-85748 Garching
Tel: +49 89 289 17652
Fax: +49 89 289 17662
gerndt@in.tum.de
Project website
www.autotune-project.eu
Community contribution
to the project
2.35 Mio Euro
Project start date
15.10.2011
Duration
36 months
Demonstration and Use
The results of AutoTune will be demonstrated in the context of GPU programming
and High Performance Computing. We will run applications with and without
autotuning on different state of the art GPU cards available in 2014 in order to
demonstrate the increase in performance portability. We will also demonstrate the
increased power efficiency of codes on the Intel Xeon Sandy Bridge-EP nodes of the
new superMUC supercomputer at Leibniz Computing Centre in Garching. We
expect to see a 10% power saving by adapting the node’s CPU frequency.
Scientific, Economic and Societal Impact
AutoTune will enhance the international recognition of all participating
organizations and facilitate the networking of academic and industrial participating
organizations. The scientific impact will include but not be limited to extensions of
European performance analysis tools towards automatic tuning. The involved
universities will include the gained knowledge into their curricula. Thus future
engineers will be better trained, which is an important asset for the European
industry.
The industrial partner CAPS will exploit AutoTune results by improving its current
and future product tool offering enhancing it with new code optimization techniques
that will help to provide users with more portable performance and more automatic
adaptation to new hardware target architectures.
The big supercomputing centres such as Leibniz Computing Centre will exploit the
energy saving results leading directly to financial benefit for the operation cost of its
high-end system by about 300.000€ per year. Due to the utmost importance of
energy savings not only for HPC but also for high-end servers, the project will have
a big societal impact by potentially saving billions of Euros worth of energy in
servers world wide. This reduction of energy consumption will also significantly
contribute to a Greener World. In addition, the European economy will profit by
strengthening companies working on desktop accelerated systems as well as
those using those systems in their business.
Key Features
•
•
•
Automatic performance tuning of parallel codes
Increased programmer productivity on GPU accelerated
systems.
Less power consumption of petascale systems
Project partners
Country
CAPS Entreprise
FR
Leibniz Computing
Cenre
NUI Galway
DE
Technische Universität
München
Universitat Autonoma
de Barcelona
Universität Wien
DE
IE
ES
AU