Kerstin Eder

This talk will cover a new open source framework, EACOF, that provides energy transparency to enable energy-aware software development.

EACOF, an Energy Aware COmputing Framework, is a modular framework that provides a layer of abstraction between sources of energy data and the applications that exploit them. It replaces platform specific instrumentation with two APIs, to input and output data from the framework. This design allows developers to profile their code for energy consumption in a simple and portable manner.

This talk will provide an overview of the structure and implementation of EACOF. It will also demonstrate how the framework can be integrated into real code to provide useful information about software energy consumption.

Other people: Hayden Field James Pedlingham

Bring along your applications and have their energy consumption measured on a pre-instrumented Arduino, Raspberry Pi or BeagleBone. Alternatively, bring along your own design on a breadboard and we'll hook up a PowerSense shield to measure the energy usage.

Other people: Andrew Back Jeremy Bennett Simon Hollis James Pallister Simon Cook

Energy models can be constructed by characterizing the energy consumed by executing each instruction in a processor's instruction set. This can be used to determine how much energy is required to execute a sequence of assembly instructions.

However, statically analyzing low level program structures is hard, and the gap between the high-level program structure and the low-level energy models needs to be bridged. We have developed a tool for performing a static analysis on the intermediate compiler representations of a program. Specifically, we target LLVM IR, a representation used by most modern compilers including Clang.

One of the challenges in doing so is that of determining an energy cost of executing LLVM IR program segments, for which we have developed a mapping tool. This tool associates LLVM IR program segments with assembly program segments, producing a mapping. Mapping information is useful when performing an analysis at one layer using energy models defined at a lower layer. Essentially, this propagates the energy model defined at the instruction set level up to the LLVM IR level, at which the analysis is performed. When this is used with our analysis tool, we are able to infer energy formulae that characterize the energy consumption for a particular program. This approach can be applied to any languages targeting the LLVM toolchain or architectures supported by LLVM.

Static energy estimation has applications in program optimization, and enables energy-aware software development.

Other people: Kyriakos Georgiou Neville Grech

Despite a decade of innovative development, and despite improvements in battery technology, a modern smartphone needs recharging far more often than its turn-of-the-century predecessor. Yet the blame cannot be laid at the door of hardware engineers; the problem lies in the software. Fortunately free and open source technology is racing to the rescue. With this talk we aim to promote energy efficiency to a first class software design goal.

Modern silicon chips are able use very little power. Multiple clock domains, clock gating and dynamic voltage and frequency control have all served to make modern hardware highly energy efficient. Yet all that can be destroyed by the software running on the chip. In this presentation we will look at how the entire software design process needs reworking to bring the software engineering team into low power design from day one. Central to this is the availability of good software development tool support, debug functionality, and energy transparency from hardware to software. We will explain why developers should consider energy consumption as a primary design goal for software, give an insight into how energy consumption of code can be measured and present some of the tools we are currently working on to enable more energy efficient software development.

Open source is playing a central role in giving developers access to tools that enable energy efficient design. We will present details of MAGEEC, a UK government funded project to build the next generation of open source machine learning compilers, which will optimize for energy efficiency. We will present the compiler framework, and the energy measurement hardware, both of which are fully open source. We will also introduce the EU ENTRA project, which aims to promote energy aware system development by enabling energy transparency from the hardware to the software in a computer system. This will be achieved using advanced energy modelling and program analysis techniques to make predictions of energy usage available to the system developer and to the software engineering tool chain. We will present energy consumption analysis tools developed in the ENTRA project.

The talk leads into the Energy Efficient Computing devroom happening on Sunday.

Other people: Jeremy Bennett