7,307 research outputs found
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
Modeling cloud resources using machine learning
Cloud computing is a new Internet infrastructure paradigm where management optimization has become a challenge to be solved, as all current management systems are human-driven or ad-hoc automatic systems that must be tuned manually by experts. Management of cloud resources require accurate information about all the elements involved (host machines, resources, offered services, and clients), and some of this information can only be obtained a posteriori. Here we present the cloud and part of its architecture as a new scenario where data mining and machine learning can be applied to discover information and improve its management thanks to modeling and prediction. As a novel case of study we show in this work the modeling of basic cloud resources using machine learning, predicting resource requirements from context information like amount of load and clients, and also predicting the quality of service from resource planning, in order to feed cloud schedulers. Further, this work is an important part of our ongoing research program, where accurate models and predictors are essential to optimize cloud management autonomic systems.Postprint (published version
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
OS Scheduling Algorithms for Memory Intensive Workloads in Multi-socket Multi-core servers
Major chip manufacturers have all introduced multicore microprocessors.
Multi-socket systems built from these processors are routinely used for running
various server applications. Depending on the application that is run on the
system, remote memory accesses can impact overall performance. This paper
presents a new operating system (OS) scheduling optimization to reduce the
impact of such remote memory accesses. By observing the pattern of local and
remote DRAM accesses for every thread in each scheduling quantum and applying
different algorithms, we come up with a new schedule of threads for the next
quantum. This new schedule potentially cuts down remote DRAM accesses for the
next scheduling quantum and improves overall performance. We present three such
new algorithms of varying complexity followed by an algorithm which is an
adaptation of Hungarian algorithm. We used three different synthetic workloads
to evaluate the algorithm. We also performed sensitivity analysis with respect
to varying DRAM latency. We show that these algorithms can cut down DRAM access
latency by up to 55% depending on the algorithm used. The benefit gained from
the algorithms is dependent upon their complexity. In general higher the
complexity higher is the benefit. Hungarian algorithm results in an optimal
solution. We find that two out of four algorithms provide a good trade-off
between performance and complexity for the workloads we studied
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