23,313 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
Evolution of the discrete cosine transform using genetic programming
Compression of 2 dimensional data is important for the efficient transmission, storage and manipulation
of Images. The most common technique used for lossy image compression relies on fast application of
the Discrete Cosine Transform (DCT). The cosine transform has been heavily researched and many
efficient methods have been determined and successfully applied in practice; this paper presents a novel
method for evolving a DCT algorithm using genetic programming. We show that it is possible to evolve a
very close approximation to a 4 point transform. In theory, an 8 point transform could also be evolved
using the same technique
A Linear Algebra Approach to Fast DNA Mixture Analysis Using GPUs
Analysis of DNA samples is an important step in forensics, and the speed of
analysis can impact investigations. Comparison of DNA sequences is based on the
analysis of short tandem repeats (STRs), which are short DNA sequences of 2-5
base pairs. Current forensics approaches use 20 STR loci for analysis. The use
of single nucleotide polymorphisms (SNPs) has utility for analysis of complex
DNA mixtures. The use of tens of thousands of SNPs loci for analysis poses
significant computational challenges because the forensic analysis scales by
the product of the loci count and number of DNA samples to be analyzed. In this
paper, we discuss the implementation of a DNA sequence comparison algorithm by
re-casting the algorithm in terms of linear algebra primitives. By developing
an overloaded matrix multiplication approach to DNA comparisons, we can
leverage advances in GPU hardware and algoithms for Dense Generalized
Matrix-Multiply (DGEMM) to speed up DNA sample comparisons. We show that it is
possible to compare 2048 unknown DNA samples with 20 million known samples in
under 6 seconds using a NVIDIA K80 GPU.Comment: Accepted for publication at the 2017 IEEE High Performance Extreme
Computing conferenc
A Multi-Gene Genetic Programming Application for Predicting Students Failure at School
Several efforts to predict student failure rate (SFR) at school accurately
still remains a core problem area faced by many in the educational sector. The
procedure for forecasting SFR are rigid and most often times require data
scaling or conversion into binary form such as is the case of the logistic
model which may lead to lose of information and effect size attenuation. Also,
the high number of factors, incomplete and unbalanced dataset, and black boxing
issues as in Artificial Neural Networks and Fuzzy logic systems exposes the
need for more efficient tools. Currently the application of Genetic Programming
(GP) holds great promises and has produced tremendous positive results in
different sectors. In this regard, this study developed GPSFARPS, a software
application to provide a robust solution to the prediction of SFR using an
evolutionary algorithm known as multi-gene genetic programming. The approach is
validated by feeding a testing data set to the evolved GP models. Result
obtained from GPSFARPS simulations show its unique ability to evolve a suitable
failure rate expression with a fast convergence at 30 generations from a
maximum specified generation of 500. The multi-gene system was also able to
minimize the evolved model expression and accurately predict student failure
rate using a subset of the original expressionComment: 14 pages, 9 figures, Journal paper. arXiv admin note: text overlap
with arXiv:1403.0623 by other author
Second-generation PLINK: rising to the challenge of larger and richer datasets
PLINK 1 is a widely used open-source C/C++ toolset for genome-wide
association studies (GWAS) and research in population genetics. However, the
steady accumulation of data from imputation and whole-genome sequencing studies
has exposed a strong need for even faster and more scalable implementations of
key functions. In addition, GWAS and population-genetic data now frequently
contain probabilistic calls, phase information, and/or multiallelic variants,
none of which can be represented by PLINK 1's primary data format.
To address these issues, we are developing a second-generation codebase for
PLINK. The first major release from this codebase, PLINK 1.9, introduces
extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space
Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic
improvements. In combination, these changes accelerate most operations by 1-4
orders of magnitude, and allow the program to handle datasets too large to fit
in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data
format capable of efficiently representing probabilities, phase, and
multiallelic variants, and (b) extensions of many functions to account for the
new types of information.
The second-generation versions of PLINK will offer dramatic improvements in
performance and compatibility. For the first time, users without access to
high-end computing resources can perform several essential analyses of the
feature-rich and very large genetic datasets coming into use.Comment: 2 figures, 1 additional fil
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