MaJIC: A Matlab Just-In-Time Compiler

Padua, David; Almasi, Gheorghe

doi:10.1007/3-540-45574-4_5

Cited by 14 publications

(11 citation statements)

References 12 publications

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“…There are many implementations of JIT compilers targeting di erent programming languages. Majic, a Matlab JIT compiler [7], OpenJIT [188] a Java JIT compiler, and IBM's JIT compiler targets the Java virtual machine [241].…”

Section: Just-in-timementioning

confidence: 99%

A Survey on Compiler Autotuning using Machine Learning

et al. 2018

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Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of di erent compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing number of compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey summarizes and classi es the recent advances in using machine learning for the compiler optimization eld, particularly on the two major problems of (1) selecting the best optimizations, and (2) the phase-ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the ne-grain classi cation among di erent approaches and nally, the in uential papers of the eld. A. H. Ashouri et al.unrolling, register allocation, etc.) could substantially bene t several performance metrics. Depending on the objectives, these metrics could be execution time, code size, or power consumption. A holistic exploration approach to trade-o these metrics also represents a challenging problem [193].Autotuning [35,256] addresses automatic code-generation and optimization by using di erent scenarios and architectures. It constructs techniques for automatic optimization of di erent parameters to maximize or minimize the satis ability of an objective function. Historically, several optimizations were done in the backend where scheduling, resource-allocation and code-generation are done [56,93]. The constraints and resources form a linear system (ILP) which needs to be solved. Recently, researchers have shown increased e ort in introducing front-end and IR-optimizations. Two observations support this claim: (1) the complexity of a backend compiler requires exclusive knowledge strictly by the compiler designers, and (2) lower overheads with external compiler modi cation compared with back-end modi cations. The IR-optimization process normally involves ne-tuning compiler optimization parameters by a multi-objective optimization formulation which can be harder to explore. Nonetheless, each approach has its bene ts and drawbacks and are subject to analysis under their scope.A major challenge in choosing the right set of compiler optimizations is the fact that these code optimizations are programming language, application, and architecture dependent. Additionally, the word optimization is a misnomer -there is no guarantee the transformed code will perform better than the original version. In fact, aggressive optimizations can even degrade the performance of the code to which they are applied [251]. Understanding the behavior of the optimization...

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Section: Just-in-timementioning

confidence: 99%

A Survey on Compiler Autotuning using Machine Learning

et al. 2018

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“…For instance, DeRose and Padua developed the FAL-CON environment [14][15] that translates MATLAB to FORTRAN90 code. They leverage an aggressive use of static and type inference for base types (doubles and complex) as well as shape (or rank) of the matrices.…”

Section: Related Workmentioning

confidence: 99%

Techniques for efficient MATLAB-to-C compilation

Bispo

Reis

Cardoso

2015

Proceedings of the 2nd ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming

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MATLAB to C translation is foreseen to raise the overall abstraction level when mapping computations to embedded systems (possibly consisting of software and hardware components), and thus for increasing productivity and for providing an automated modeldriven design-flow. This paper describes recent work developed in the context of MATISSE, a MATLAB to C compiler targeting embedded systems. We introduce several techniques to allow the efficient generation of C code, such as weak types, primitives and matrix views. We evaluate the compiler with a set of 9 publicly available benchmarks, targeting both embedded systems and a desktop system. We compare the execution time of the generated C code with the original code running on MATLAB, achieving a geometric mean speedup of 8.1, and qualitatively compare our results with the performance of related approaches. The use of the new techniques allowed the compiler to achieve performance improvements of 46% on average.

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“…6 An exception has been the lattice L s = {W × W, ⊥ s , s , s } of two-dimensional shapes used in Almási [2001], and Almási and Padua [2002], where ⊥ s = 0, 0 and s = ∞, ∞ were the least and greatest elements in L s , respectively, and the lattice's partial order s was defined as…”

Section: Unsafe Shape Solutions In Majicmentioning

confidence: 99%

An algebraic array shape inference system for MATLAB®

Joisha

Banerjee

2006

ACM Trans. Program. Lang. Syst.

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The problem of inferring array shapes ahead of time in languages that exhibit both implicit and dynamic typing is a critical one because the ramifications of its solution are the better organization of array storage through compaction and reuse, and the generation of high-performance code through specialization by shape. This article addresses the problem in a prototypical implicitly and dynamically typed array language called MATLAB. The approach involves modeling the language's shape semantics using an algebraic system, and applying term rewriting techniques to evaluate expressions under this algebra. Unlike prior efforts at array shape determination, this enables the deduction of valuable shape information even when array extents are compile-time unknowns. Furthermore, unlike some previous methods, our approach doesn't impose monotonicity requirements on an operator's shape semantics. The work also describes an inference methodology and reports measurements from a type inference engine called . In a benchmark suite of 17 programs, the shape inference subsystem in detected the equivalence of over 61% of the symbolic shapes in six programs, and over 57% and 37% of the symbolic shapes in two others. In the remaining nine programs, all array shapes were inferred to be compile-time constants.

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MaJIC: A Matlab Just-In-Time Compiler

Cited by 14 publications

References 12 publications

A Survey on Compiler Autotuning using Machine Learning

A Survey on Compiler Autotuning using Machine Learning

Techniques for efficient MATLAB-to-C compilation

An algebraic array shape inference system for MATLAB®

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