H. Chen, A. Krolik, E. Lavoie, and L. Hendren, Automatic Vectorization for MATLAB, LNCS of Lecture Notes in Computer Science, vol.10136, pp.171-187, 2017.

K. E. Iverson, A programming language, 1962.

J. Language, , pp.2019-2028

,. E. Efstratios-gallopoulos, J. R. Houstis, and . Rice, Computer as thinker/-doer: problem-solving environments for computational science, IEEE Computational Science and Engineering, vol.1, issue.2, pp.11-23, 1994.

, Intel® Math Kernel Library (MKL, pp.2019-2028

H. Theodore, D. Romer, G. M. Lee, A. Voelker, W. A. Wolman et al., The structure and performance of interpreters, Proceedings of the seventh international conference on Architectural support for programming languages and operating systems -ASPLOS-VII, vol.5, pp.150-159, 1996.

A. V. Aho, M. S. Lam, R. Sethi, and J. D. Ullman, Compilers: Principles, Techniques, and Tools, 2006.

J. Aycock, A brief history of just-in-time, ACM Computing Surveys, vol.35, issue.2, pp.97-113, 2003.

V. Menon and K. Pingali, A case for source-level transformations in MATLAB, ACM SIGPLAN Notices, vol.35, issue.1, pp.53-65, 2000.

K. Luiz-de-rose, E. Gallivan, B. Gallopoulos, D. Marsolf, and . Padua, FALCON: A MATLAB interactive restructuring compiler, Lecture Notes in Computer Science, number 1448, pp.269-288, 1996.

M. J. Quinn, A. Malishevsky, and N. Seelam, Otter: bridging the gap between MATLAB and ScaLAPACK, Proceedings. The Seventh International Symposium on High Performance Distributed Computing (Cat. No.98TB100244), volume 1998-July, pp.114-121, 1998.

J. Bispo, P. Pinto, R. Nobre, T. Carvalho, J. M. Cardoso et al., The MATISSE MATLAB compiler, 11th IEEE International Conference on Industrial Informatics (INDIN), vol.7, p.2013, 2013.

L. Hendren, J. Doherty, A. Dubrau, R. Garg, N. Lameed et al., McLAB: Enabling Programming Language, Compiler and Software Engineering Research for Matlab, Proceedings of the ACM International Conference Companion on Object Oriented Programming Systems Languages and Applications Companion, pp.195-196, 2011.

X. Li and L. Hendren, Mc2For: A tool for automatically translating MATLAB to FORTRAN 95, 2014 Software Evolution Week -IEEE Conference on Software Maintenance, Reengineering, and Reverse Engineering, vol.2, pp.234-243, 2014.

V. Kumar, L. Hendren, and . Mix10, Proceedings of the 2014 ACM International Conference on Object Oriented Programming Systems Languages & Applications -OOPSLA '14, pp.617-636, 2014.

G. Pramod, P. Joisha, and . Banerjee, An algebraic array shape inference system for MATLAB®, ACM Transactions on Programming Languages and Systems, vol.28, issue.5, pp.848-907, 2006.

G. Almási and D. Padua, MaJIC: Compiling MATLAB for Speed and Responsiveness*, Proceedings of the ACM SIGPLAN 2002 Conference on Programming language design and implementation -PLDI '02, vol.294, 2002.

S. Chauveau and F. Bodin, Menhir: An Environment for High Performance Matlab, Scientific Programming, vol.7, issue.3-4, pp.303-312, 1999.

A. Chauhan and K. Kennedy, Reducing and Vectorizing Procedures for Telescoping Languages, International Journal of Parallel Programming, vol.30, issue.4, pp.291-315, 2002.

N. Birkbeck, J. Levesque, and J. N. Amaral, A Dimension Abstraction Approach to Vectorization in Matlab, International Symposium on Code Generation and Optimization (CGO'07), pp.115-130, 2007.

M. Chevalier-boisvert, L. Hendren, and C. Verbrugge, Optimizing Matlab through Just-In-Time Specialization, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume, vol.6011, pp.46-65, 2010.

G. Almasi and D. Padua, MaJIC: A Matlab just-in-time Compiler, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol.2017, pp.68-81, 2001.

I. Azzini, R. Muresano, and M. Ratto, Dragonfly: A multiplatform parallel toolbox for MATLAB/Octave. Computer Languages, Systems and Structures, vol.52, pp.21-42, 2018.

V. Menon and A. E. Trefethen, MultiMATLAB: Integrating Matlab with high performance parallel computing, Proceedings of the 1997 ACM/IEEE conference on Supercomputing (CDROM) -Supercomputing '97, vol.1, pp.1-18, 1997.

C. Ancourt and F. Irigoin, Scanning polyhedra with DO loops, ACM SIGPLAN Notices, vol.26, issue.7, pp.39-50, 1991.
URL : https://hal.archives-ouvertes.fr/hal-00752774

M. S. Lam, E. E. Rothberg, and M. E. Wolf, The cache performance and optimizations of blocked algorithms, ACM SIGARCH Computer Architecture News, vol.19, issue.2, pp.63-74, 1991.

K. S. Mckinley, S. Carr, and C. Tseng, Improving data locality with loop transformations, ACM Transactions on Programming Languages and Systems, vol.18, issue.4, pp.424-453, 1996.

L. Derose, K. Gallivan, E. Gallopoulos, B. Marsolf, and D. Padua, An environment for the rapid prototyping and development of numerical programs and libraries for scientific computation, Parallel Computing, issue.1370, 1994.

L. Derose, K. Gallivan, E. Gallopoulos, A. Bret, D. Marsolf et al., FALCON: An Environment for the Development of Scientific Libraries and Applications, Proc. First International Workshop on Knowledge-Based System for the (re)Use of Program Libraries, 1995.

P. Kiepas, J. Kozlak, C. Tadonki, and C. Ancourt, Profile-based vectorization for MATLAB, Proceedings of the 5th ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming -ARRAY 2018, pp.18-23, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01857852

S. Williams, A. Waterman, and D. Patterson, Roofline: an insightful visual performance model for multicore architectures, Communications of the ACM, vol.52, issue.4, p.65, 2009.

A. Yasin, A Top-Down method for performance analysis and counters architecture, International Symposium on Performance Analysis of Systems and Software, ISPASS 2014, pp.35-44, 2014.

J. Cavazos, G. Fursin, F. Agakov, E. Bonilla, F. P. Michael et al., Rapidly Selecting Good Compiler Optimizations using Performance Counters, International Symposium on Code Generation and Optimization (CGO'07), pp.185-197, 2007.

C. Cummins, P. Petoumenos, Z. Wang, and H. Leather, End-to-End Deep Learning of Optimization Heuristics, 26th International Conference on Parallel Architectures and Compilation Techniques (PACT), vol.2017, p.2017, 2017.

E. Barrett, C. F. Bolz-tereick, R. Killick, S. Mount, and L. Tratt, Virtual machine warmup blows hot and cold, Proceedings of the ACM on Programming Languages, vol.1, issue.OOPSLA, p.2017

K. Adams, J. Evans, B. Maher, G. Ottoni, A. Paroski et al., The hiphop virtual machine, ACM SIGPLAN Notices, vol.49, issue.10, pp.777-790, 2015.

T. Würthinger, C. Wimmer, A. Wöß, L. Stadler, G. Duboscq et al., One VM to rule them all, Proceedings of the 2013 ACM international symposium on New ideas, new paradigms, and reflections on programming & software -Onward! '13, pp.187-204, 2013.

O. Wiki, HotSpot Internals, pp.2019-2028

R. Michael, P. Jantz, and . Kulkarni, Exploring single and multilevel JIT compilation policy for modern machines, ACM Transactions on Architecture and Code Optimization, vol.10, issue.4, pp.1-29, 2014.

T. Mytkowicz, A. Diwan, M. Hauswirth, and P. F. Sweeney, Producing wrong data without doing anything obviously wrong, ! ACM SIGPLAN Notices, vol.44, issue.3, p.265, 2009.

S. Patil and D. J. Lilja, Statistical methods for computer performance evaluation, Wiley Interdisciplinary Reviews: Computational Statistics, vol.4, issue.1, p.2012

, PAPI: Performance Application Programming Interface, pp.2019-2028

, Functions and Objects Supported for C/C++ Code Generation (MATLAB R2019b)

J. Bispo, M. P. João, and . Cardoso, A MATLAB subset to C compiler targeting embedded systems. Software -Practice and Experience, vol.47, p.2017

L. Reis, J. Bispo, and J. M. Cardoso, Compiler Techniques for Efficient MATLAB to OpenCL Code Generation, Proceedings of the 5th International Workshop on OpenCL -IWOCL 2017, volume Part F1277, pp.1-2, 2017.

D. Luiz-de-rose and . Padua, A MATLAB to Fortran 90 translator and its effectiveness, Proceedings of the 10th international conference on Supercomputing -ICS '96, number 1462, pp.309-316, 1996.

L. Antonio-de-rose and D. Padua, Techniques for the translation of MATLAB programs into Fortran 90, ACM Transactions on Programming Languages and Systems, vol.21, issue.2, pp.286-323, 1999.

M. J. Quinn, A. Malishevsky, N. Seelam, and Y. Zhao, Preliminary results from a parallel MATLAB compiler, Proceedings of the First Merged International Parallel Processing Symposium and Symposium on Parallel and Distributed Processing, pp.81-87, 1998.

S. Chauveau and F. Bodin, Menhir: An Environment for High Performance Matlab, LCR '98: Selected Papers from the 4th International Workshop on Languages, Compilers, and Run-Time Systems for Scalable Computers, pp.27-40, 1998.

P. Banerjee, A. Choudhary, N. Hauck, C. Shenoy, M. Bachmann et al., MATCH: A MAT-LAB Compiler For Conngurable Computing Systems, 1999.

P. Banerjee, N. Shenoy, A. Choudhary, S. Hauck, C. Bachmann et al., A MATLAB compiler for distributed, heterogeneous, reconfigurable computing systems, Proceedings 2000 IEEE Symposium on Field-Programmable Custom Computing Machines (Cat. No.PR00871), pp.39-48, 2000.

P. Banerjee, An overview of a compiler for mapping MATLAB programs onto FPGAs, Proceedings of the 2003 conference on Asia South Pacific design automation -ASPDAC, vol.477, 2003.

G. Pramod, P. Joisha, and . Banerjee, A translator system for the MATLAB language. Software: Practice and Experience, vol.37, pp.535-578, 2007.

P. Jurica and . Van-cees-leeuwen, OMPC: an open-source MATLAB®-to-Python compiler, Frontiers in Neuroinformatics, vol.3, issue.5, 2009.

A. Prasad, J. Anantpur, and R. Govindarajan, Automatic compilation of MATLAB programs for synergistic execution on heterogeneous processors, ACM SIGPLAN Notices, vol.47, issue.6, p.152, 2012.

/. Silkan and . Cold, , pp.2019-2028

J. Geir-yngve-paulsen, X. Feinberg, B. Cai, H. Nordmoen, and . Petter-dahle, Matlab2cpp: A Matlab-to-C++ code translator, 11th System of Systems Engineering Conference (SoSE), vol.6, p.2016, 2016.

S. Geir-yngve-paulsen, B. Clark, S. Nordmoen, and . Nenakhov, Automated Translation of MATLAB Code to C++ with Performance and Traceability

, The Eleventh International Conference on Advanced Engineering Computing and Applications in Sciences, number c, pp.50-55, 2017.

J. Spazier, S. Christgau, and B. Schnor, Automatic generation of parallel C code for stencil applications written in MAT-LAB, Proceedings of the 3rd ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming -ARRAY 2016, pp.47-54, 2016.

V. Foley, -. Bourgon, and L. Hendren, Efficiently implementing the copy semantics of MATLAB's arrays in JavaScript, Proceedings of the 12th Symposium on Dynamic Languages -DLS 2016, pp.72-83, 2016.

I. Latifis, K. Parashar, and G. Dimitroulakos, Hans Cappelle, Christakis Lezos, Konstantinos Masselos, and Francky Catthoor. A MATLAB Vectorizing Compiler Targeting Application-Specific Instruction Set Processors, ACM Transactions on Design Automation of Electronic Systems, vol.22, issue.2, pp.1-28, 2017.

G. Pramod, A. Joisha, P. Kanhere, N. Banerjee, A. Shenoy et al., The Design and Implementation of a Parser and Scanner for the MATLAB Language in the MATCH Compiler, 1999.

G. Pramod, A. Joisha, P. Kanhere, . Banerjee, A. Nagaraj-shenoy et al., Handling context-sensitive syntactic issues in the design of a front-end for a MATLAB compiler, ACM SIGAPL APL Quote Quad, vol.31, issue.3, pp.27-40, 2001.

G. Pramod, P. Joisha, and . Banerjee, Implementing an Array Shape Inference System for MATLAB Using MATHEMATICA

J. Bispo, L. Reis, and J. M. Cardoso, C and OpenCL generation from MATLAB, Proceedings of the 30th Annual ACM Symposium on Applied Computing -SAC '15, pp.1315-1320, 2015.

M. P. João, T. Cardoso, . Carvalho, G. F. José, W. Coutinho et al., LARA: An aspectoriented programming language for embedded systems, AOSD'12 -Proceedings of the 11th Annual International Conference on Aspect Oriented Software Development, pp.179-190, 2012.

, Intel® Threading Building Blocks (TBB), pp.2019-2028

A. W. Dubrau, L. J. Hendren, and M. Taming, Proceedings of the ACM international conference on Object oriented programming systems languages and applications -OOPSLA '12, p.503, 2012.

R. Allen and K. Kennedy, Automatic translation of FORTRAN programs to vector form, ACM Transactions on Programming Languages and Systems, vol.9, issue.4, pp.491-542, 1987.

J. R. Allen and K. Kennedy, Optimizing Compilers for Modern Architectures, 2001.

V. Menon and K. Pingali, High-level semantic optimization of numerical codes, Proceedings of the 13th international conference on Supercomputing -ICS '99, pp.434-443, 1999.

D. Elphick, M. Leuschel, and S. Cox, Partial Evaluation of MATLAB, GPCE '03 Proceedings of the 2nd international conference on Generative programming and component engineering, pp.344-363, 2003.

C. Lattner and V. Adve, LLVM: A Compilation Framework for Lifelong Program Analysis & Transformation, Proceedings of the International Symposium on Code Generation and Optimization: Feedback-directed and Runtime Optimization, 2004.

M. Haldar, A. Nayak, A. Kanhere, P. Joisha, N. Shenoy et al., Match virtual machine: An adaptive runtime system to execute MATLAB in parallel, Proceedings of the International Conference on Parallel Processing, pp.145-152, 2000.

J. Doherty, L. Hendren, and S. Radpour, Kind analysis for MATLAB, ACM SIGPLAN Notices, vol.46, issue.10, p.99, 2011.

R. Wilhelm, M. Sagiv, and T. Reps, Shape Analysis, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol.1781, pp.1-17, 2000.

, MAGICA: A Type Inference Engine for MATLAB, pp.2019-2028

G. Pramod, P. Joisha, and . Banerjee, The MAGICA Type Inference Engine for MATLAB ®, Compiler Construction. CC 2003. Lecture Notes in Computer Science, pp.121-125, 2003.

G. Pramod and . Joisha, A type inference system for MATLAB with applications to code optimization, 2003.

E. W. Shankar-ramaswamy, P. Hodges, and . Banerjee, Compiling MAT-LAB programs to ScaLAPACK: exploiting task and data parallelism, Proceedings of International Conference on Parallel Processing, pp.613-619, 1996.

A. Abel and J. R. Info, Characterizing Latency, Throughput, and Port Usage of Instructions on Intel Microarchitectures, Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems -ASPLOS '19, pp.673-686, 2019.

L. John, D. Hennessy, and . Patterson, Computer Architecture: A Quantitive Approach, 2017.

D. Callahan, J. Cocke, and K. Kennedy, Estimating interlock and improving balance for pipelined architectures, Journal of Parallel and Distributed Computing, vol.5, issue.4, pp.334-358, 1988.

S. Carr, K. S. Mckinley, and C. Tseng, Compiler optimizations for improving data locality, ACM SIGOPS Operating Systems Review, vol.28, issue.5, pp.252-262, 1994.

A. Ilic, F. Pratas, and L. Sousa, Cache-aware roofline model: Upgrading the loft, IEEE Computer Architecture Letters, vol.13, issue.1, pp.21-24, 2014.

A. Ilic, F. Pratas, and L. Sousa, Beyond the roofline: Cache-aware power and energy-efficiency modeling for multicores, IEEE Transactions on Computers, vol.66, issue.1, pp.52-58, 2017.

C. Victoria, M. Cabezas, and . Puschel, Extending the roofline model: Bottleneck analysis with microarchitectural constraints, IISWC 2014 -IEEE International Symposium on Workload Characterization, pp.222-231, 2014.

G. Hager, J. Treibig, J. Habich, and G. Wellein, Exploring performance and power properties of modern multi-core chips via simple machine models, Concurrency and Computation: Practice and Experience, vol.28, issue.2, pp.189-210, 2016.

B. Sprunt, The basics of performance-monitoring hardware, IEEE Micro, vol.22, issue.4, pp.64-71, 2002.

S. Eranian, What can performance counters do for memory subsystem analysis?, Proceedings of the 2008 ACM SIGPLAN workshop on Memory systems performance and correctness held in conjunction with the Thirteenth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS '08) -MSPC '08, p.26, 2008.

. Oprofile, , pp.2019-2028

D. Terpstra, H. Jagode, H. You, and J. Dongarra, Collecting Performance Data with PAPI-C, Tools for High Performance Computing, pp.157-173, 2009.

, Tiptop, pp.2019-2028

E. Rohou, Tiptop: Hardware Performance Counters for the Masses, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00747064

E. Rohou, Tiptop: Hardware performance counters for the masses, Proceedings of the International Conference on Parallel Processing Workshops, pp.404-413, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00747064

I. Vtune, , pp.2019-2028

V. Weaver and J. Dongarra, Can hardware performance counters produce expected, deterministic results, Proceedings of Third Workshop on Functionality of Hardware Performance Monitoring, 2010.

M. Vincent, S. A. Weaver, and . Mckee, Can hardware performance counters be trusted?, International Symposium on Workload Characterization, IISWC 2008, pp.141-150, 2008.

D. Zaparanuks, M. Jovic, and M. Hauswirth, Accuracy of performance counter measurements, International Symposium on Performance Analysis of Systems and Software, pp.23-32, 2009.

F. T. Schneider, M. Payer, and T. R. Gross, Online optimizations driven by hardware performance monitoring, ACM SIGPLAN Notices, vol.42, issue.6, p.373, 2007.

L. Eeckhout, A. Georges, and K. Bosschere, How java programs interact with virtual machines at the microarchitectural level, ACM SIGPLAN Notices, vol.38, issue.11, p.169, 2003.

F. Peter, M. Sweeney, B. Hauswirth, P. Cahoon, A. Cheng et al., Using hardware performance monitors to understand the behavior of java applications, Machine Research And Technology Symposium, p.5, 2004.

T. Sherwood, B. Calder, and S. Diego, Time Varying Behavior of Programs, 1999.

T. Sherwood, E. Perelman, G. Hamerly, and B. Calder, Automatically characterizing large scale program behavior, Architectural Support for Programming Languages and Operating Systems, ASPLOS 2002, vol.36, p.45, 2002.

T. Sherwood, E. Perelman, G. Hamerly, S. Sair, and B. Calder, Discovering and exploiting program phases, IEEE Micro, vol.23, issue.6, pp.84-93, 2003.

E. Duesterwald, C. Cascaval, and S. Dwarkadas, Characterizing and predicting program behavior and its variability, Parallel Architectures and Compilation Techniques, PACT 2003, pp.220-231, 2003.

M. Hauswirth, P. F. Sweeney, A. Diwan, and M. Hind, Vertical profiling: Understanding the Behavior of Object-Oriented Applications, Object-Oriented Programming, vol.39, p.251, 2004.

M. Hauswirth, A. Diwan, F. Peter, M. C. Sweeney, and . Mozer, Automating vertical profiling, ACM SIGPLAN Notices, vol.40, issue.10, p.281, 2006.

M. Hauswirth, F. Peter, A. Sweeney, and . Diwan, Temporal vertical profiling. Software -Practice and Experience, vol.40, pp.627-654, 2010.

M. , A. Ertl, and D. Gregg, The Behavior of Efficient Virtual Machine Interpreters on Modern Architectures, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol.2150, pp.403-413, 2001.

A. Ertl and D. Gregg, The structure and performance of efficient interpreters, Journal of Instruction-Level Parallelism, vol.5, pp.1-25, 2003.

S. Brunthaler, Virtual-Machine Abstraction and Optimization Techniques, Electronic Notes in Theoretical Computer Science, vol.253, issue.5, pp.3-14, 2009.

E. Rohou, A. Bharath-narasimha-swamy, and . Seznec, Branch prediction and the performance of interpreters -Don't trust folklore, IEEE/ACM International Symposium on Code Generation and Optimization (CGO), vol.2, p.2015, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01100647

G. Barany, Python Interpreter Performance Deconstructed, Proceedings of the Workshop on Dynamic Languages and Applications -Dyla'14, pp.1-9, 2014.

V. Carchiolo, M. Malgeri, G. Mangioni, and V. Nicosia, Evaluating the Dynamic Behaviour of Python applications, ACSC '09 Proceedings of the Thirty-Second Australasian Conference on Computer Science, pp.19-28, 2009.

D. Bruening, T. Garnett, and S. Amarasinghe, An infrastructure for adaptive dynamic optimization, International Symposium on Code Generation and Optimization, pp.265-275, 2003.

C. Luk, R. Cohn, R. Muth, H. Patil, A. Klauser et al., Pin: building customized program analysis tools with dynamic instrumentation, Programming Language Design and Implementation, PLDI 2005, p.190, 2005.

H. Rafael, A. J. Saavedra, and . Smith, Analysis of benchmark characteristics and benchmark performance prediction, ACM Transactions on Computer Systems, vol.14, issue.4, pp.344-384, 1996.

V. Shirley and . Moore, A comparison of counting and sampling modes of using performance monitoring hardware, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 2330 LNCS, pp.904-912, 2002.

J. Doweck, W. Kao, A. Kuan-yu-lu, J. Mandelblat, A. Rahatekar et al., Inside 6th-Generation Intel Core: New Microarchitecture Code-Named Skylake, IEEE Micro, vol.37, issue.2, pp.52-62, 2017.

T. Mytkowicz, P. Sweeney, M. Hauswirth, A. Diwan, F. Peter et al., Observer Effect and Measurement Bias in Performance Analysis, 2008.

D. Molka, R. Schöne, D. Hackenberg, and W. E. Nagel, Detecting Memory-Boundedness with Hardware Performance Counters, vol.17, pp.27-38, 2017.

. John-d-mccalpin, Memory Bandwidth and Machine Balance in Current High Performance Computers, IEEE Computer Society Technical Committee on Computer Architecture (TCCA) Newsletter, pp.19-25, 1995.

, The Bandwidth Benchmark: Extended Stream, pp.2019-2028

L. Jerry, . Hintze, and . Ray-d-nelson, Violin plots: A box plot-density trace synergism, American Statistician, vol.52, issue.2, pp.181-184, 1998.

P. Samuel and A. , An APL Machine, 1970.

M. Shaver, D. Mandelin, J. Orendorff, M. Bebenita, B. Eich et al.,

B. Haghighat, J. Kaplan, A. Ruderman, and . Gal, Tracebased just-in-time type specialization for dynamic languages, ACM SIGPLAN Notices, vol.44, issue.6, p.465, 2009.

J. Brock, C. Ding, X. Xu, and Y. Zhang, PAYJIT: space-optimal JIT compilation and its practical implementation, Proceedings of the 27th International Conference on Compiler Construction -CC 2018, vol.18, pp.71-81, 2018.

. Intel, Intel ® 64 and IA-32 Architectures Software Developer's Manual, 2019.

, Intel® Processor Event Reference, pp.2019-2028

, FFTW: Fastest Fourier Transform in the West, pp.2019-2028

M. Frigo and S. G. Johnson, The design and implementation of FFTW3, Proceedings of the IEEE, vol.93, pp.216-231, 2005.

J. Bezanson, A. Edelman, S. Karpinski, and . Shah, Julia: A fresh approach to numerical computing, SIAM Review, vol.59, issue.1, pp.65-98, 2017.

C. F. Bolz, A. Cuni, M. Fijalkowski, and A. Rigo, Tracing the Meta-Level: PyPy's Tracing JIT Compiler, Proceedings of the 4th workshop on the Implementation, Compilation, Optimization of Object-Oriented Languages and Programming Systems -ICOOOLPS '09, pp.18-25, 2009.

, PyPy: Tracing JIT compiler for Python, pp.2019-2028

S. Larsen and S. Amarasinghe, Exploiting superword level parallelism with multimedia instruction sets, SIGPLAN Notices (ACM Special Interest Group on Programming Languages, vol.35, issue.5, pp.145-156, 2000.

M. Wolfe, Vector optimization vs vectorization, Journal of Parallel and Distributed Computing, vol.5, issue.5, pp.551-567, 1988.

D. Callahan, J. Dongarra, and D. Levine, Vectorizing compilers: a test suite and results, Proceedings. SUPERCOMPUTING '88, pp.98-105, 1988.

S. Maleki, Y. Gao, J. María, T. Garzarán, D. A. Wong et al., An evaluation of vectorizing compilers. Parallel Architectures and Compilation Techniques, PACT, vol.7, pp.372-382, 2011.

G. Randolph, . Scarborough, G. Harwood, and . Kolsky, A vectorizing Fortran compiler, IBM Journal of Research and Development, vol.30, issue.2, pp.163-171, 1986.

J. R. Allen, K. Kennedy, C. Porterfield, and J. Warren, Conversion of control dependence to data dependence, Proceedings of the 10th ACM SIGACT-SIGPLAN symposium on Principles of programming languages -POPL '83, pp.177-189, 2003.

A. Georges, D. Buytaert, and L. Eeckhout, Statistically rigorous java performance evaluation, ACM SIGPLAN Notices, vol.42, issue.10, p.57, 2007.

, Program Analysis and Transformation, pp.2019-2028

, PIPS: Automatic Parallelizer and Code Transformation Framework, pp.2019-2028

, Cetus: A Source-to-Source Compiler Infrastructure for C Programs, pp.2019-2028

H. Bae, D. Mustafa, J. Lee, H. Aurangzeb, C. Lin et al., The Cetus Source-to-Source Compiler Infrastructure: Overview and Evaluation, International Journal of Parallel Programming, vol.41, issue.6, p.2013

F. E. Allen, Control flow analysis, ACM SIGPLAN Notices, vol.5, issue.7, pp.1-19, 1970.

F. E. Allen and J. Cocke, A program data flow analysis procedure, Communications of the ACM, vol.19, issue.3, p.137, 1976.

D. J. Kuck, R. H. Kuhn, D. Padua, B. Leasure, and M. Wolfe, Dependence graphs and compiler optimizations, Proceedings of the 8th ACM SIGPLAN-SIGACT symposium on Principles of programming languages -POPL '81, pp.207-218, 1981.

P. , LLVM Framework for High-Level Loop and Data-Locality Optimizations, pp.2019-2028

T. Parr, Language Implementation Patterns: Create Your Own Domain-Specific and General Programming Languages, 2009.

J. Ragan-kelley, C. Barnes, A. Adams, S. Paris, F. Durand et al., Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines, Proceedings of the 34th ACM SIGPLAN conference on Programming language design and implementation -PLDI '13, vol.519, 2013.

A. Casey, S. Radpour, O. S. Belanger, L. Hendren, C. Verbrugge et al., McLab: an extensible compiler toolkit for MATLAB and related languages, Proceedings of the Third C* Conference on Computer Science and Software Engineering -C3S2E '10, pp.114-117, 2010.

A. Aslam and L. Hendren, McFLAT: A profile-based framework for MATLAB loop analysis and transformations, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume, vol.6548, pp.1-15, 2011.

A. Bodzay and L. Hendren, AspectMatlab++: annotations, types, and aspects for scientists, Proceedings of the 14th International Conference on Modularity -MODULARITY 2015, pp.41-54, 2015.

M. Sharir, A strong-connectivity algorithm and its applications in data flow analysis, vol.7, pp.67-72, 1981.

J. J. Dongarra and A. R. Hinds, Unrolling Loops in FORTRAN. Software: Practice and Experience, vol.9, pp.219-226, 1979.

L. Hendren, Typing aspects for MATLAB, Proceedings of the sixth annual workshop on Domain-specific aspect languages -DSAL '11, vol.13, 2011.

, Krun: High fidelity benchmark runner, pp.2019-2028

A. Maricq, D. Duplyakin, R. Stutsman, R. Ricci, C. Maltzahn et al., Taming Performance Variability, Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation -OSDI'18, pp.409-425, 2018.

, Linux profiling with performance counters, pp.2019-2028

A. W. Wade, P. A. Kulkarni, M. R. Jantz, and . Vs, JIT: impact of profile data on code quality, ACM SIGPLAN Notices, vol.52, issue.4, pp.1-10, 2017.

M. Arnold, S. J. Fink, D. Grove, M. Hind, and P. F. Sweeney, A Survey of Adaptive Optimization in Virtual Machines, Proceedings of the IEEE, vol.93, issue.2, pp.449-466, 2005.