Compilers: principles, techniques, and tools
Compilers: principles, techniques, and tools
Maximizing parallelism and minimizing synchronization with affine transforms
Proceedings of the 24th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Automatic selection of high-order transformations in the IBM XL FORTRAN compilers
IBM Journal of Research and Development - Special issue: performance analysis and its impact on design
Optimizing for reduced code space using genetic algorithms
Proceedings of the ACM SIGPLAN 1999 workshop on Languages, compilers, and tools for embedded systems
Adaptive optimization in the Jalapeño JVM
OOPSLA '00 Proceedings of the 15th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Loop Transformations for Restructuring Compilers: The Foundations
Loop Transformations for Restructuring Compilers: The Foundations
Optimizing Supercompilers for Supercomputers
Optimizing Supercompilers for Supercomputers
Dependence Analysis for Supercomputing
Dependence Analysis for Supercomputing
A Loop Transformation Theory and an Algorithm to Maximize Parallelism
IEEE Transactions on Parallel and Distributed Systems
A Matlab Just-In-time Compiler
LCPC '00 Proceedings of the 13th International Workshop on Languages and Compilers for Parallel Computing-Revised Papers
Predicting the impact of optimizations for embedded systems
Proceedings of the 2003 ACM SIGPLAN conference on Language, compiler, and tool for embedded systems
Interprocedural dependence analysis and parallelization
ACM SIGPLAN Notices - Best of PLDI 1979-1999
A data locality optimizing algorithm
ACM SIGPLAN Notices - Best of PLDI 1979-1999
A Dimension Abstraction Approach to Vectorization in Matlab
Proceedings of the International Symposium on Code Generation and Optimization
Proceedings of the 2009 ACM SIGPLAN conference on Programming language design and implementation
AspectMatlab: an aspect-oriented scientific programming language
Proceedings of the 9th International Conference on Aspect-Oriented Software Development
Optimizing MATLAB through just-in-time specialization
CC'10/ETAPS'10 Proceedings of the 19th joint European conference on Theory and Practice of Software, international conference on Compiler Construction
Automating GPU computing in MATLAB
Proceedings of the international conference on Supercomputing
Riposte: a trace-driven compiler and parallel VM for vector code in R
Proceedings of the 21st international conference on Parallel architectures and compilation techniques
Vectorized simulation of groundwater flow and streamline transport
Environmental Modelling & Software
Integrating profile-driven parallelism detection and machine-learning-based mapping
ACM Transactions on Architecture and Code Optimization (TACO)
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Parallelization and optimization of the MATLAB programming language presents several challenges due to the dynamic nature of MATLAB. Since MATLAB does not have static type declarations, neither the shape and size of arrays, nor the loop bounds are known at compile-time. This means that many standard array dependence tests and associated transformations cannot be applied straight-forwardly. On the other hand, many MATLAB programs operate on arrays using loops and thus are ideal candidates for loop transformations and possibly loop vectorization/parallelization. This paper presents a new framework, MCFLAT, which uses profile-based training runs to determine likely loop-bounds ranges for which specialized versions of the loops may be generated. The main idea is to collect information about observed loop bounds and hot loops using training data which is then used to heuristically decide upon which loops and which ranges are worth specializing using a variety of loop transformations. Our MCFLAT framework has been implemented as part of the McLAB extensible compiler toolkit. Currently, MCFLAT, is used to automatically transform ordinary MATLAB code into specialized MATLAB code with transformations applied to it. This specialized code can be executed on any MATLAB system, and we report results for four execution engines, Mathwork's proprietary MATLAB system, the GNU Octave open-source interpreter, McLAB's McVM interpreter and the McVM JIT. For several benchmarks, we observed significant speedups for the specialized versions, and noted that loop transformations had different impacts depending on the loop range and execution engine.