NDLI: Inducing heuristics to decide whether to schedule

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

Race checking by context inference

Scalable lock-free dynamic memory allocation

Min-cut program decomposition for thread-level speculation

Cost effective dynamic program slicing

Cloning-based context-sensitive pointer alias analysis using binary decision diagrams

Fast searches for effective optimization phase sequences

The set constraint/CFL reachability connection in practice

Region inference for an object-oriented language

A generalized algorithm for graph-coloring register allocation

Social processes and proofs of theorems and programs, revisited

KISS: keep it simple and sequential

Kill-safe synchronization abstractions

A cost-driven compilation framework for speculative parallelization of sequential programs

Dynamic path-based software watermarking

Symbolic pointer analysis revisited

Inducing heuristics to decide whether to schedule

Parametric regular path queries

Array regrouping and structure splitting using whole-program reference affinity

Balancing register allocation across threads for a multithreaded network processor

Verifying safety properties using separation and heterogeneous abstractions

Vectorization for SIMD architectures with alignment constraints

Parametric analysis for adaptive computation offloading

Jedd: a BDD-based relational extension of Java

The liberty structural specification language: a high-level modeling language for component reuse

Precise and efficient static array bound checking for large embedded C programs

Prefetch injection based on hardware monitoring and object metadata

Inducing heuristics to decide whether to schedule

Content Provider	ACM Digital Library
Author	Cavazos, John Moss, J. Eliot B.
Abstract	Instruction scheduling is a compiler optimization that can improve program speed, sometimes by 10% or more, but it can also be expensive. Furthermore, time spent optimizing is more important in a Java just-in-time (JIT) compiler than in a traditional one because a JIT compiles code at run time, adding to the running time of the program. We found that, on any given block of code, instruction scheduling often does not produce significant benefit and sometimes degrades speed. Thus, we hoped that we could focus scheduling effort on those blocks that benefit from it.Using supervised learning we induced heuristics to predict which blocks benefit from scheduling. The induced function chooses, for each block, between list scheduling and not scheduling the block at all. Using the induced function we obtained over 90% of the improvement of scheduling every block but with less than 25% of the scheduling effort. When used in combination with profile-based adaptive optimization, the induced function remains effective but gives a smaller reduction in scheduling effort. Deciding when to optimize, and which optimization(s) to apply, is an important open problem area in compiler research. We show that supervised learning solves one of these problems well.
Starting Page	183
Ending Page	194
Page Count	12
File Format	PDF
ISBN	1581138075
DOI	10.1145/996841.996864
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2004-06-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Java Supervised learning Instruction scheduling Machine learning Compiler optimization Jikes rvm
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

Inducing heuristics to decide whether to schedule

Inducing Heuristics To Decide Whether To Schedule (2004)

Method-specific dynamic compilation using logistic regression

Mitigating the compiler optimization phase-ordering problem using machine learning

Automating instruction selector generation in jikes rvm (2009).

Mitigating the compiler optimization phase-ordering problem using machine learning

Jikes RDB: a debugger for the Jikes RVM

Machine-Learning-based Performance Heuristics for Runtime CPU/GPU Selection

Method-specific dynamic compilation using logistic regression

Inducing heuristics to decide whether to schedule

Similar Documents

Inducing heuristics to decide whether to schedule

Inducing Heuristics To Decide Whether To Schedule (2004)

Method-specific dynamic compilation using logistic regression

Mitigating the compiler optimization phase-ordering problem using machine learning

Automating instruction selector generation in jikes rvm (2009).

Mitigating the compiler optimization phase-ordering problem using machine learning

Jikes RDB: a debugger for the Jikes RVM

Machine-Learning-based Performance Heuristics for Runtime CPU/GPU Selection

Method-specific dynamic compilation using logistic regression

Inducing heuristics to decide whether to schedule