POPL '88 Proceedings of the 15th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Some efficient solutions to the affine scheduling problem: I. One-dimensional time
International Journal of Parallel Programming
Improving data locality with loop transformations
ACM Transactions on Programming Languages and Systems (TOPLAS)
ICS '96 Proceedings of the 10th international conference on Supercomputing
Combining loop transformations considering caches and scheduling
Proceedings of the 29th annual ACM/IEEE international symposium on Microarchitecture
Optimal weighted loop fusion for parallel programs
Proceedings of the ninth annual ACM symposium on Parallel algorithms and architectures
Optimizing compilers for modern architectures: a dependence-based approach
Optimizing compilers for modern architectures: a dependence-based approach
High Performance Compilers for Parallel Computing
High Performance Compilers for Parallel Computing
Maximizing Loop Parallelism and Improving Data Locality via Loop Fusion and Distribution
Proceedings of the 6th International Workshop on Languages and Compilers for Parallel Computing
Meta optimization: improving compiler heuristics with machine learning
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
Code Generation in the Polyhedral Model Is Easier Than You Think
Proceedings of the 13th International Conference on Parallel Architectures and Compilation Techniques
Space–time mapping and tiling: a helpful combination: Research Articles
Concurrency and Computation: Practice & Experience - Compilers for Parallel Computers
Formal loop merging for signal transforms
Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation
A Heuristic Search Algorithm Based on Unified Transformation Framework
ICPPW '05 Proceedings of the 2005 International Conference on Parallel Processing Workshops
Facilitating the search for compositions of program transformations
Proceedings of the 19th annual international conference on Supercomputing
Using Machine Learning to Focus Iterative Optimization
Proceedings of the International Symposium on Code Generation and Optimization
Semi-automatic composition of loop transformations for deep parallelism and memory hierarchies
International Journal of Parallel Programming
Profitable loop fusion and tiling using model-driven empirical search
Proceedings of the 20th annual international conference on Supercomputing
Iterative Optimization in the Polyhedral Model: Part I, One-Dimensional Time
Proceedings of the International Symposium on Code Generation and Optimization
Iterative optimization in the polyhedral model: part ii, multidimensional time
Proceedings of the 2008 ACM SIGPLAN conference on Programming language design and implementation
A practical automatic polyhedral parallelizer and locality optimizer
Proceedings of the 2008 ACM SIGPLAN conference on Programming language design and implementation
Computer Generation of General Size Linear Transform Libraries
Proceedings of the 7th annual IEEE/ACM International Symposium on Code Generation and Optimization
A scalable auto-tuning framework for compiler optimization
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Polyhedral-Model Guided Loop-Nest Auto-Vectorization
PACT '09 Proceedings of the 2009 18th International Conference on Parallel Architectures and Compilation Techniques
CC'08/ETAPS'08 Proceedings of the Joint European Conferences on Theory and Practice of Software 17th international conference on Compiler construction
The polyhedral model is more widely applicable than you think
CC'10/ETAPS'10 Proceedings of the 19th joint European conference on Theory and Practice of Software, international conference on Compiler Construction
A model for fusion and code motion in an automatic parallelizing compiler
Proceedings of the 19th international conference on Parallel architectures and compilation techniques
Loop transformations: convexity, pruning and optimization
Proceedings of the 38th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
Journal of Computational and Applied Mathematics
Polyhedral parallelization of binary code
ACM Transactions on Architecture and Code Optimization (TACO) - HIPEAC Papers
A performance analysis framework for identifying potential benefits in GPGPU applications
Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming
Predictive modeling in a polyhedral optimization space
CGO '11 Proceedings of the 9th Annual IEEE/ACM International Symposium on Code Generation and Optimization
Vapor SIMD: Auto-vectorize once, run everywhere
CGO '11 Proceedings of the 9th Annual IEEE/ACM International Symposium on Code Generation and Optimization
Portable section-level tuning of compiler parallelized applications
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Vectorization technology to improve interpreter performance
ACM Transactions on Architecture and Code Optimization (TACO) - Special Issue on High-Performance Embedded Architectures and Compilers
When polyhedral transformations meet SIMD code generation
Proceedings of the 34th ACM SIGPLAN conference on Programming language design and implementation
Enabling fair pricing on HPC systems with node sharing
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Compiling affine loop nests for distributed-memory parallel architectures
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Integrating profile-driven parallelism detection and machine-learning-based mapping
ACM Transactions on Architecture and Code Optimization (TACO)
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Today's multi-core era places significant demands on an optimizing compiler, which must parallelize programs, exploit memory hierarchy, and leverage the ever-increasing SIMD capabilities of modern processors. Existing model-based heuristics for performance optimization used in compilers are limited in their ability to identify profitable parallelism/locality trade-offs and usually lead to sub-optimal performance. To address this problem, we distinguish optimizations for which effective model-based heuristics and profitability estimates exist, from optimizations that require empirical search to achieve good performance in a portable fashion. We have developed a completely automatic framework in which we focus the empirical search on the set of valid possibilities to perform fusion/code motion, and rely on model-based mechanisms to perform tiling, vectorization and parallelization on the transformed program. We demonstrate the effectiveness of this approach in terms of strong performance improvements on a single target as well as performance portability across different target architectures.