115,669 research outputs found
A Survey of Prediction and Classification Techniques in Multicore Processor Systems
In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems
Modeling and visualizing networked multi-core embedded software energy consumption
In this report we present a network-level multi-core energy model and a
software development process workflow that allows software developers to
estimate the energy consumption of multi-core embedded programs. This work
focuses on a high performance, cache-less and timing predictable embedded
processor architecture, XS1. Prior modelling work is improved to increase
accuracy, then extended to be parametric with respect to voltage and frequency
scaling (VFS) and then integrated into a larger scale model of a network of
interconnected cores. The modelling is supported by enhancements to an open
source instruction set simulator to provide the first network timing aware
simulations of the target architecture. Simulation based modelling techniques
are combined with methods of results presentation to demonstrate how such work
can be integrated into a software developer's workflow, enabling the developer
to make informed, energy aware coding decisions. A set of single-,
multi-threaded and multi-core benchmarks are used to exercise and evaluate the
models and provide use case examples for how results can be presented and
interpreted. The models all yield accuracy within an average +/-5 % error
margin
TANGO: Transparent heterogeneous hardware Architecture deployment for eNergy Gain in Operation
The paper is concerned with the issue of how software systems actually use
Heterogeneous Parallel Architectures (HPAs), with the goal of optimizing power
consumption on these resources. It argues the need for novel methods and tools
to support software developers aiming to optimise power consumption resulting
from designing, developing, deploying and running software on HPAs, while
maintaining other quality aspects of software to adequate and agreed levels. To
do so, a reference architecture to support energy efficiency at application
construction, deployment, and operation is discussed, as well as its
implementation and evaluation plans.Comment: Part of the Program Transformation for Programmability in
Heterogeneous Architectures (PROHA) workshop, Barcelona, Spain, 12th March
2016, 7 pages, LaTeX, 3 PNG figure
Recommended from our members
Context-awareness for mobile sensing: a survey and future directions
The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions
A Review on Software Architectures for Heterogeneous Platforms
The increasing demands for computing performance have been a reality
regardless of the requirements for smaller and more energy efficient devices.
Throughout the years, the strategy adopted by industry was to increase the
robustness of a single processor by increasing its clock frequency and mounting
more transistors so more calculations could be executed. However, it is known
that the physical limits of such processors are being reached, and one way to
fulfill such increasing computing demands has been to adopt a strategy based on
heterogeneous computing, i.e., using a heterogeneous platform containing more
than one type of processor. This way, different types of tasks can be executed
by processors that are specialized in them. Heterogeneous computing, however,
poses a number of challenges to software engineering, especially in the
architecture and deployment phases. In this paper, we conduct an empirical
study that aims at discovering the state-of-the-art in software architecture
for heterogeneous computing, with focus on deployment. We conduct a systematic
mapping study that retrieved 28 studies, which were critically assessed to
obtain an overview of the research field. We identified gaps and trends that
can be used by both researchers and practitioners as guides to further
investigate the topic
A Survey on Compiler Autotuning using Machine Learning
Since the mid-1990s, researchers have been trying to use machine-learning
based approaches to solve a number of different compiler optimization problems.
These techniques primarily enhance the quality of the obtained results and,
more importantly, make it feasible to tackle two main compiler optimization
problems: optimization selection (choosing which optimizations to apply) and
phase-ordering (choosing the order of applying optimizations). The compiler
optimization space continues to grow due to the advancement of applications,
increasing number of compiler optimizations, and new target architectures.
Generic optimization passes in compilers cannot fully leverage newly introduced
optimizations and, therefore, cannot keep up with the pace of increasing
options. This survey summarizes and classifies the recent advances in using
machine learning for the compiler optimization field, particularly on the two
major problems of (1) selecting the best optimizations and (2) the
phase-ordering of optimizations. The survey highlights the approaches taken so
far, the obtained results, the fine-grain classification among different
approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our
Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated
quarterly here (Send me your new published papers to be added in the
subsequent version) History: Received November 2016; Revised August 2017;
Revised February 2018; Accepted March 2018
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