11,730 research outputs found
Adaptive planning for distributed systems using goal accomplishment tracking
Goal accomplishment tracking is the process of monitoring the progress of a task or series of tasks towards completing a goal. Goal accomplishment tracking is used to monitor goal progress in a variety of domains, including workflow processing, teleoperation and industrial manufacturing. Practically, it involves the constant monitoring of task execution, analysis of this data to determine the task progress and notification of interested parties. This information is usually used in a passive way to observe goal progress. However, responding to this information may prevent goal failures. In addition, responding proactively in an opportunistic way can also lead to goals being completed faster. This paper proposes an architecture to support the adaptive planning of tasks for fault tolerance or opportunistic task execution based on goal accomplishment tracking. It argues that dramatically increased performance can be gained by monitoring task execution and altering plans dynamically
Microgrid - The microthreaded many-core architecture
Traditional processors use the von Neumann execution model, some other
processors in the past have used the dataflow execution model. A combination of
von Neuman model and dataflow model is also tried in the past and the resultant
model is referred as hybrid dataflow execution model. We describe a hybrid
dataflow model known as the microthreading. It provides constructs for
creation, synchronization and communication between threads in an intermediate
language. The microthreading model is an abstract programming and machine model
for many-core architecture. A particular instance of this model is named as the
microthreaded architecture or the Microgrid. This architecture implements all
the concurrency constructs of the microthreading model in the hardware with the
management of these constructs in the hardware.Comment: 30 pages, 16 figure
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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