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Resource-aware Programming in a High-level Language - Improved performance with manageable effort on clustered MPSoCs

Zwinkau, Andreas

Bis 2001 bedeutete Moores und Dennards Gesetz eine Verdoppelung der Ausführungszeit alle 18 Monate durch verbesserte CPUs.
Heute ist Nebenläufigkeit das dominante Mittel zur Beschleunigung von Supercomputern bis zu mobilen Geräten.
Allerdings behindern neuere Phänomene wie "Dark Silicon" zunehmend eine weitere Beschleunigung durch Hardware.
Um weitere Beschleunigung zu erreichen muss sich auch die Soft­ware mehr ihrer Hardware Resourcen gewahr werden.
Verbunden mit diesem Phänomen ist eine immer heterogenere Hardware.
Supercomputer integrieren Beschleuniger wie GPUs.
Mobile SoCs (bspw. ... mehr

Abstract (englisch):
Until 2001 Moore's Law and Dennard Scaling implied that execution speed doubled every 18 months due
to better CPUs.
Today, concurrency is the dominant way for speedups from supercomputers to mobiles.
However, more recent phenomenons like Dark Silicon increasingly complicate speedups from hardware.
To realize further performance gains, software has to become more aware of the hardware resources.
A related phenomenon is increasingly heterogeneous hardware.
Supercomputers integrate accelerators like GPUs.
Mobile SoCs (for example in smartphones) integrate more and more features.
Exploiting special hardware is a well-known technique to lower energy consumption,
which is another important aspect that must be balanced with raw performance.
For example, supercomputers are also rated by "performance per watt".
Currently, low-level programmers are used to think about hardware,
while the mainstream high-level programmers prefer to abstract
as much of the platform as possible (for example clouds).
High-level does not imply that hardware is irrelevant, just that it can be abstracted.
If you write a Java application for Android, battery use might be an important aspect.
Eventually, even high-level programming languages are pressured
to become resource-aware to improve speed or energy consumption.

Within the transregional collaborative research center "Invasive Computing",
I worked on these problems.
In my dissertation, I propose a framework to make high-level applications
resource-aware and thus improve performance,
which for example might result in improved efficiency or speedups for the system as a whole.

One core idea is that applications do not optimize on their own.
Instead, they give information to the operating system.
The operating system with its global view makes resource decisions.
This process we call "invasion" of resources.
The job of the application is to adapt to the operating system's decision, not to make its own.
The challenge is to define the language,
which applications use to communicate resource constraints and performance hints.
Such a language must be expressive enough for complex information,
extensible for future resource types, and convenient for the programmer.

The major contributions in this dissertation are:

A theoretic model of resource allocation to precisely describe
the essence of the resource-aware framework,
to reason about the correctness of the operating system decisions with respect to the constraints of an application,
and to proof my claims of efficiency and speedup in theory.

A framework and compilation path for resource-aware programming
implemented for the high-level programming language X10.
We implemented applications from High Performance Computing to evaluate this approach.
Speedups of 5x can be demonstrated.

A consistency model for the X10 programming language
as a necessary step for a formal semantics,
which bridges the theoretic model to the concrete implementation.

In one sentence: Resource-aware programming in high-level languages on
MPSoCs is feasible with manageable effort and improves performance.

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Volltext §
DOI: 10.5445/IR/1000083526
Veröffentlicht am 20.06.2018
Zugehörige Institution(en) am KIT Institut für Programmstrukturen und Datenorganisation (IPD)
Publikationstyp Hochschulschrift
Jahr 2018
Sprache Englisch
Identifikator urn:nbn:de:swb:90-835267
KITopen-ID: 1000083526
Verlag KIT, Karlsruhe
Umfang X, 172 S.
Abschlussart Dissertation
Fakultät Fakultät für Informatik (INFORMATIK)
Institut Institut für Programmstrukturen und Datenorganisation (IPD)
Prüfungsdatum 26.04.2018
Referent/Betreuer Prof. G. Snelting
Projektinformation SFB/TRR 89/2 (DFG, DFG KOORD, TRR 89/2 2014)
Schlagworte Invasive Computing, Resource-Awareness
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
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