Self-adjusting binary search trees
Journal of the ACM (JACM)
A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Learning internal representations by error propagation
Neurocomputing: foundations of research
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
An Automatic Technique for Selection of Data Representations in SETL Programs
ACM Transactions on Programming Languages and Systems (TOPLAS)
Automatic data structure choice in a language of very high level
POPL '75 Proceedings of the 2nd ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
Algorithms + Data Structures = Programs
Algorithms + Data Structures = Programs
Machine Learning
A Machine Learning Approach to Automatic Production of Compiler Heuristics
AIMSA '02 Proceedings of the 10th International Conference on Artificial Intelligence: Methodology, Systems, and Applications
Genetic Algorithms for Feature Selection and Weighting, A Review and Study
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
A Survey of Software Refactoring
IEEE Transactions on Software Engineering
Overfitting problem: a new perspective from the geometrical interpretation of MLP
Design and application of hybrid intelligent systems
Towards automatic performance tuning
Towards automatic performance tuning
Fast compiler optimisation evaluation using code-feature based performance prediction
Proceedings of the 4th international conference on Computing frontiers
Automating the construction of compiler heuristics using machine learning
Automating the construction of compiler heuristics using machine learning
The causes of bloat, the limits of health
Proceedings of the 22nd annual ACM SIGPLAN conference on Object-oriented programming systems and applications
Optimization of sparse matrix-vector multiplication on emerging multicore platforms
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Proceedings of the 17th international conference on Parallel architectures and compilation techniques
Mapping parallelism to multi-cores: a machine learning based approach
Proceedings of the 14th ACM SIGPLAN symposium on Principles and practice of parallel programming
Proceedings of the 2009 ACM SIGPLAN conference on Programming language design and implementation
Chameleon: adaptive selection of collections
Proceedings of the 2009 ACM SIGPLAN conference on Programming language design and implementation
Automatic Feature Generation for Machine Learning Based Optimizing Compilation
Proceedings of the 7th annual IEEE/ACM International Symposium on Code Generation and Optimization
Perflint: A Context Sensitive Performance Advisor for C++ Programs
Proceedings of the 7th annual IEEE/ACM International Symposium on Code Generation and Optimization
DDT: design and evaluation of a dynamic program analysis for optimizing data structure usage
Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture
Four Trends Leading to Java Runtime Bloat
IEEE Software
Detecting inefficiently-used containers to avoid bloat
PLDI '10 Proceedings of the 2010 ACM SIGPLAN conference on Programming language design and implementation
Proceedings of the FSE/SDP workshop on Future of software engineering research
Algorithm engineering: bridging the gap between algorithm theory and practice
Algorithm engineering: bridging the gap between algorithm theory and practice
Dynamic analysis of inefficiently-used containers
Proceedings of the 2012 Workshop on Dynamic Analysis
From relational verification to SIMD loop synthesis
Proceedings of the 18th ACM SIGPLAN symposium on Principles and practice of parallel programming
CoCo: sound and adaptive replacement of java collections
ECOOP'13 Proceedings of the 27th European conference on Object-Oriented Programming
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Data structure selection is one of the most critical aspects of developing effective applications. By analyzing data structures' behavior and their interaction with the rest of the application on the underlying architecture, tools can make suggestions for alternative data structures better suited for the program input on which the application runs. Consequently, developers can optimize their data structure usage to make the application conscious of an underlying architecture and a particular program input. This paper presents the design and evaluation of Brainy, a new program analysis tool that automatically selects the best data structure for a given program and its input on a specific microarchitecture. The data structure's interface functions are instrumented to dynamically monitor how the data structure interacts with the application for a given input. The instrumentation records traces of various runtime characteristics including underlying architecture-specific events. These generated traces are analyzed and fed into an offline model, constructed using machine learning, to select the best data structure. That is, Brainy exploits runtime feedback of data structures to model the situation an application runs on, and selects the best data structure for a given application/input/architecture combination based on the constructed model. The empirical evaluation shows that this technique is highly accurate across several real-world applications with various program input sets on two different state-of-the-art microarchitectures. Consequently, Brainy achieved an average performance improvement of 27% and 33% on both microarchitectures, respectively.