NDLI: Using Machine Learning to Focus Iterative Optimization

Please wait, while we are loading the content...

International Symposium on Code Generation and Optimization - Title Page

International Symposium on Code Generation and Optimization - Copyright

Message from the General Co-Chairs

Message from the Program Chair

Committees

Reviewers

Corporate Sponsors

A Cross-Architectural Interface for Code Cache Manipulation

Thread-Shared Software Code Caches

Tailoring Graph-coloring Register Allocation For Runtime Compilation

A Self-Repairing Prefetcher in an Event-Driven Dynamic Optimization Framework

Java JNI Bridge: A Framework for Mixed Native ISA Execution

Space-Efficient 64-bit Java Objects through Selective Typed Virtual Addressing

Experiences with Multi-threading and Dynamic Class Loading in a Java Just-In-Time Compiler

Dynamic Class Hierarchy Mutation

Online Phase Detection Algorithms

Region Monitoring for Local Phase Detection in Dynamic Optimization Systems

Selecting Software Phase Markers with Code Structure Analysis

Profiling over Adaptive Ranges

2D-Profiling: Detecting Input-Dependent Branches with a Single Input Data Set

Constructing Virtual Architectures on a Tiled Processor

Compiling for EDGE Architectures

Data and Computation Transformations for Brook Streaming Applications on Multiprocessors

Compiler-directed Data Partitioning for Multicluster Processors

Inline Analysis: Beyond Selection Heuristics

Practical Structure Layout Optimization and Advice

Post Register Allocation Spill Code Optimization

A Compiler-Guided Approach for Reducing Disk Power Consumption by Exploiting Disk Access Locality

Optimizing Dynamic Binary Translation for SIMD Instructions

Multi-platform Auto-vectorization

Using Machine Learning to Focus Iterative Optimization

Exhaustive Optimization Phase Order Space Exploration

Fast and Effective Orchestration of Compiler Optimizations for Automatic Performance Tuning

Software-Based Transparent and Comprehensive Control-Flow Error Detection

Compiler Optimizations to Reduce Security Overhead

BIRD: Binary Interpretation using Runtime Disassembly

Author Index

International Symposium on Code Generation and Optimization - Front Cover

Using Machine Learning to Focus Iterative Optimization

Content Provider	ACM Digital Library
Author	Cavazos, J. Bonilla, E. Williams, C. K. I. Toussaint, M. Franke, B. O'Boyle, M. F. P. Thomson, J. Fursin, G. Agakov, F.
Abstract	Iterative compiler optimization has been shown to outperform static approaches. This, however, is at the cost of large numbers of evaluations of the program. This paper develops a new methodology to reduce this number and hence speed up iterative optimization. It uses predictive modelling from the domain of machine learning to automatically focus search on those areas likely to give greatest performance. This approach is independent of search algorithm, search space or compiler infrastructure and scales gracefully with the compiler optimization space size. Off-line, a training set of programs is iteratively evaluated and the shape of the spaces and program features are modelled. These models are learnt and used to focus the iterative optimization of a new program. We evaluate two learnt models, an independent and Markov model, and evaluate their worth on two embedded platforms, the Texas Instrument C6713 and the AMD Au1500. We show that such learnt models can speed up iterative search on large spaces by an order of magnitude. This translates into an average speedup of 1.22 on the TI C6713 and 1.27 on the AMD Au1500 in just 2 evaluations.
Starting Page	295
Ending Page	305
Page Count	11
File Format	PDF
ISBN	0769524990
DOI	10.1109/CGO.2006.37
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-03-26
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Using machine learning to focus iterative optimization

Using Machine Learning to Focus Iterative Optimization (2006)

An Iterative Constrained Optimization Approach to Classifier Design

Parallel iterative compilation: using MapReduce to speedup machine learning in compilers

Query-level learning to rank using isotonic regression

Using graph-based program characterization for predictive modeling

An evaluation of different modeling techniques for iterative compilation

An evaluation of different modeling techniques for iterative compilation

Hyperparameter Optimization Using Iterative Decision Tree (IDT)

Using Machine Learning to Focus Iterative Optimization

Similar Documents

Using machine learning to focus iterative optimization

Using Machine Learning to Focus Iterative Optimization (2006)

An Iterative Constrained Optimization Approach to Classifier Design

Parallel iterative compilation: using MapReduce to speedup machine learning in compilers

Query-level learning to rank using isotonic regression

Using graph-based program characterization for predictive modeling

An evaluation of different modeling techniques for iterative compilation

An evaluation of different modeling techniques for iterative compilation

Hyperparameter Optimization Using Iterative Decision Tree (IDT)

Using Machine Learning to Focus Iterative Optimization