227,793 research outputs found
Multi-architecture Value Analysis for Machine Code
International audienceSafety verification of critical real-time embedded systems requires Worst Case Execution Time information (WCET). Among the existing approaches to estimate the WCET, static analysis at the machine code level has proven to get safe results. A lot of different architectures are used in real-time systems but no generic solution provides the ability to perform static analysis of values handled by machine instructions. Nonetheless, results of such analyses are worth to improve the precision of other analyzes like data cache, indirect branches, etc. This paper proposes a semantic language aimed at expressing semantics of machine instructions whatever the underlying instruction set is. This ensures abstraction and portability of the value analysis or any analysis based on the semantic expression of the instructions. As a proof of concept, we adapted and refined an existing analysis representing values as Circular-Linear Progression (CLP), that is, as a sparse integer interval effective to model pointers. In addition, we show how our semantic instructions allow to build back conditions of loop in order to refine the CLP values and improve the precision of the analysis. Both contributions have been implemented in our framework, OTAWA, and experimented on the Malardalen benchmark to demonstrate the effectiveness of the approach
Predicting Post-Procedural Complications Using Neural Networks on MIMIC-III Data
The primary focus of this paper is the creation of a Machine Learning based algorithm for the analysis of large health based data sets. Our input was extracted from MIMIC-III, a large Health Record database of more than 40,000 patients. The main question was to predict if a patient will have complications during certain specified procedures performed in the hospital. These events are denoted by the icd9 code 996 in the individuals\u27 health record. The output of our predictive model is a binary variable which outputs the value 1 if the patient is diagnosed with the specific complication or 0 if the patient is not. Our prediction algorithm is based on a Neural Network architecture, with a 90%-10% training-testing ratio. Our preliminary analysis yielded a prediction accuracy above 80%, outperforming various multi-linear models. A comparative analysis of various optimizers as well as time based performance measures is also included
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
Towards an Achievable Performance for the Loop Nests
Numerous code optimization techniques, including loop nest optimizations,
have been developed over the last four decades. Loop optimization techniques
transform loop nests to improve the performance of the code on a target
architecture, including exposing parallelism. Finding and evaluating an
optimal, semantic-preserving sequence of transformations is a complex problem.
The sequence is guided using heuristics and/or analytical models and there is
no way of knowing how close it gets to optimal performance or if there is any
headroom for improvement. This paper makes two contributions. First, it uses a
comparative analysis of loop optimizations/transformations across multiple
compilers to determine how much headroom may exist for each compiler. And
second, it presents an approach to characterize the loop nests based on their
hardware performance counter values and a Machine Learning approach that
predicts which compiler will generate the fastest code for a loop nest. The
prediction is made for both auto-vectorized, serial compilation and for
auto-parallelization. The results show that the headroom for state-of-the-art
compilers ranges from 1.10x to 1.42x for the serial code and from 1.30x to
1.71x for the auto-parallelized code. These results are based on the Machine
Learning predictions.Comment: Accepted at the 31st International Workshop on Languages and
Compilers for Parallel Computing (LCPC 2018
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