6,372 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
Automatically Leveraging MapReduce Frameworks for Data-Intensive Applications
MapReduce is a popular programming paradigm for developing large-scale,
data-intensive computation. Many frameworks that implement this paradigm have
recently been developed. To leverage these frameworks, however, developers must
become familiar with their APIs and rewrite existing code. Casper is a new tool
that automatically translates sequential Java programs into the MapReduce
paradigm. Casper identifies potential code fragments to rewrite and translates
them in two steps: (1) Casper uses program synthesis to search for a program
summary (i.e., a functional specification) of each code fragment. The summary
is expressed using a high-level intermediate language resembling the MapReduce
paradigm and verified to be semantically equivalent to the original using a
theorem prover. (2) Casper generates executable code from the summary, using
either the Hadoop, Spark, or Flink API. We evaluated Casper by automatically
converting real-world, sequential Java benchmarks to MapReduce. The resulting
benchmarks perform up to 48.2x faster compared to the original.Comment: 12 pages, additional 4 pages of references and appendi
ALOJA: A benchmarking and predictive platform for big data performance analysis
The main goals of the ALOJA research project from BSC-MSR, are to explore and automate the characterization of cost-effectivenessof Big Data deployments. The development of the project over its first year, has resulted in a open source benchmarking platform, an online public repository of results with over 42,000 Hadoop job runs, and web-based analytic tools to gather insights about system's cost-performance1.
This article describes the evolution of the project's focus and research
lines from over a year of continuously benchmarking Hadoop under dif-
ferent configuration and deployments options, presents results, and dis
cusses the motivation both technical and market-based of such changes.
During this time, ALOJA's target has evolved from a previous low-level
profiling of Hadoop runtime, passing through extensive benchmarking
and evaluation of a large body of results via aggregation, to currently
leveraging Predictive Analytics (PA) techniques. Modeling benchmark
executions allow us to estimate the results of new or untested configu-
rations or hardware set-ups automatically, by learning techniques from
past observations saving in benchmarking time and costs.This work is partially supported the BSC-Microsoft Research Centre, the Span-
ish Ministry of Education (TIN2012-34557), the MINECO Severo Ochoa Research program (SEV-2011-0067) and the Generalitat de Catalunya (2014-SGR-1051).Peer ReviewedPostprint (author's final draft
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