vectorized programming languages "loop based" programming like C

vectorized programming languages (e.g. Matlab, Numpy, Torch) will go a long ways - it is fairly different than "loop based" programming like C Vectorization - MATLAB & Simulink - MathWorks www.mathworks.com › ... › Code Performance MathWorks The process of revising loop-based, scalar-oriented code to use MATLAB ... Fewer lines of code mean fewer opportunities to introduce programming errors. Vectorization - Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Vectorization Wikipedia Array programming, a style of computer programming where operations are applied to ... Automatic vectorization, a compiler optimization that transforms loops to ... Automatic vectorization - Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Automatic_vectorization Wikipedia However, in most programming languages one typically writes loops that sequentially ... The correct vector instruction must be chosen based on the size and ... What is "vectorization"? - Stack Overflow stackoverflow.com/questions/1422149/what-is-vectorization Sep 14, 2009 - Ask programming questions; Answer and help your peers; Get recognized for your expertise ... The difference between vectorization and loopunrolling: Consider the following very simple .... White spots at the base of a cactus. GNU Octave: Vectorization and Faster Code Execution www.gnu.org/...0.../Vectorization-and-Faster-Code-Execution.html GNU Vectorization is a programming technique that uses vector operations instead of element-by-element loop-based operations. Besides frequently producing more ... Programming Guidelines for Vectorizing C/C++ Compilers ... www.drdobbs.com/programming...vectorizing.../1844... Dr. Dobb's Journal Feb 1, 2003 - The semantics of a sequential programming language like C or C++ impose a ... if (p+n < q || q+n < p) /* vector loop */ for (i = 0; i < n; i++) p[i] = q[i]; else ... (Namely the property a MOD base = offset holds for the address a at ... Program Optimization through Loop Vectorization | Intel ... https://software.intel.com/.../program-optimization-through-loop-ve... Intel Jan 6, 2014 - Download Article Download Program Optimization through Loop ... Likewise, we expect standard-based vector programming to have a wide ... What is Vectorization? | Parallel Programming in Native Code https://blogs.msdn.microsoft.com/.../2012/04/12/what-is-vectorization/ Apr 12, 2012 - Parallel programming using C++ AMP, PPL and Agents libraries. ... The compiler will transform this code into a loop that uses registers to ... [PDF]Vector loops and Parallel Loops - Cilk Plus https://www.cilkplus.org/.../N3419_vector_loops_and_parallel_loops.pdf Sep 21, 2012 - vector hardware and the need to support programming this new .... parallel loops that look more like the current range-based for loops ... Matlab Programming - Vectorized Operations www.cs.utah.edu/.../vectorized_or_array_operations.h... University of Utah A "Vector" operation in Matlab is the ability to write condensed code to apply an action ... over EVERY element of the array (very similar to our notion of a "loop"). ... list of values (an array) based on a "start value" : "increment value" : "end value". https://www.reddit.com/r/MachineLearning/comments/3lydz3/i_work_in_the_film_industry_and_have_strange/ Corrigendum: Algorithm 902: GPOPS, a MATLAB software for solving multiple-phase optimal control problems using the gauss pseudospectral method Full Text: PDF Get this Article Authors: Anil V. Rao University of Florida David A. Benson The Charles Stark Draper Laboratory, Inc. Christopher Darby University of Florida Michael A. Patterson University of Florida Camila Francolin University of Florida Ilyssa Sanders University of Florida Geoffrey T. Huntington Blue Origin, LLC  2011 Article Research Refereed   Bibliometrics · Downloads (6 Weeks): 6 · Downloads (12 Months): 36 · Downloads (cumulative): 190 · Citation Count: 0 Published in: · Journal ACM Transactions on Mathematical Software (TOMS) TOMS Homepage archive Volume 38 Issue 1, November 2011 Article No. 9 ACM New York, NY, USA table of contents doi>10.1145/2049662.2049671     Recent authors with related interests     Concepts in this article  powered by

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An algorithm is described to solve multiple-phase optimal control problems using a recently developed numerical method called theGauss pseudospectral method. The algorithm is well suited for use in modern vectorized programming languages such as FORTRAN 95 and MATLAB. The algorithm discretizes the cost functional and the differential-algebraic equations in each phase of the optimal control problem. The phases are then connected using linkage conditions on the state and time. A large-scale nonlinear programming problem (NLP) arises from the discretization and the significant features of the NLP are described in detail. A particular reusable MATLAB implementation of the algorithm, called GPOPS, is applied to three classical optimal control problems to demonstrate its utility. The algorithm described in this article will provide researchers and engineers a useful software tool and a reference when it is desired to implement the Gauss pseudospectral method in other programming languages.

or more pieces of data. Modern x86 chips have the SSE instructions, many PPC chips have the "Altivec" instructions, and even some ARM chips have a vector instruction set, called NEON. "Vectorization" (simplified) is the process of rewriting a loop so that instead of processing a single element of an array N times, it processes (say) 4 elements of the array simultaneously N/4 times. (I chose 4 because it's what modern hardware is most likely to directly support; the term "vectorization" is also used to describe a higher level software transformation where you might just abstract away the loop altogether and just describe operating on arrays instead of the elements that comprise them) The difference between vectorization and loop unrolling: Consider the following very simple loop that adds the elements of two arrays and stores the results to a third array. for (int i=0; i<16; ++i) C[i] = A[i] + B[i]; Unrolling this loop would transform it into something like this: for (int i=0; i<16; i+=4) { C[i] = A[i] + B[i]; C[i+1] = A[i+1] + B[i+1]; C[i+2] = A[i+2] + B[i+2]; C[i+3] = A[i+3] + B[i+3]; } Vectorizing it, on the other hand, produces something like this: for (int i=0; i<16; i+=4) addFourThingsAtOnceAndStoreResult(&C[i], &A[i], &B[i]); Where "addFourThingsAtOnceAndStoreResult" is a placeholder for whatever intrinsic(s) your compiler uses to specify vector instructions. Note that some compilers are able to auto vectorize very simple loops like this, which can often be enabled via a compile option. More complex algorithms still require help from the programmer to generate good vector code. shareedit edited Sep 14 '09 at 17:01 answered Sep 14 '09 at 15:12 Stephen Canon 72.8k11119204

3   What's the difference between this and loop unwinding/unrolling? – Jeremy Powell Sep 14 '09 at 16:28 Isn't it true that a compiler would have an easier job auto-vectorizing the unrolled loop ? – Nikos AthanasiouJun 10 '15 at 23:35 @NikosAthanasiou: It's plausible, but generally speaking a compiler should be able to autovectorize either loop, as they are both quite simple. – Stephen Canon Jun 12 '15 at 2:43 add a comment

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Vectorization is the term for converting a scalar program to a vector program. Vectorized programs can run multiple operations from a single instruction, whereas scalar can only operate on pairs of operands at once. From wikipedia: Scalar approach: for (i = 0; i < 1024; i++) { C[i] = A[i]*B[i]; } Vectorized approach: for (i = 0; i < 1024; i+=4) { C[i:i+3] = A[i:i+3]*B[i:i+3]; } shareedit answered Sep 14 '09 at 15:13 Anders 3,9652173124

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It refers to a the ability to do single mathematical operation on a list -- or "vector" -- of numbers in a single step. You see it often with Fortran because that's associated with scientific computing, which is associated with supercomputing, where vectorized arithmetic first appeared. Nowadays almost all desktop CPUs offer some form of vectorized arithmetic, through technologies like Intel's SSE. GPUs also offer a form of vectorized arithmetic. shareedit answered Sep 14 '09 at 15:09 Warren Young 22.4k45779

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up vote1down vote	See the two answers above. I just wanted to add that the reason for wanting to do vectorization is that these operations can easily be performed in paraell by supercomputers and multi-processors, yielding a big performance gain. On single processor computers there will be no performance gain. shareedit answered Sep 14 '09 at 15:13 Larry Watanabe 7,86162837
	3   "On single processor computers there will be no performance gain": not true. Most modern processors have (limited) hardware support for vectorization (SSE, Altivec. etc. as named by stephentyrone), which can give significant speedup when used. – sleske Sep 14 '09 at 15:24 thanks, I forgot that parallelization can be done at that level as well. – Larry Watanabe Sep 15 '09 at 13:14