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Download Update Support: Order page for. OptiVec Master License for all supported compilers: CMATH Master License: OptiVec for Visual C: CMATH for Visual C: OptiVec for C

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Navigation: Home \ Development \ Components & Libraries \ OptiVec for Visual C++ Software Description: OptiVec brings the power of vectorized programming to the PC. Replacing loops over data arrays by the hand-optimized, Assembler-written vector and matrix functions of OptiVec can speed up program execution by 2-3 times and enhances accuracy and safety. More than 3500 functions for math, statistics, analysis, signal processing, matrix operations, curve fitting, etc. For MS Visual C++. ... Download OptiVec for Visual C++ Buy Now Add to Download Basket Report virus or spyware Software Info Best Vista Download periodically updates pricing and software information of OptiVec for Visual C++ full version from the publisher, but some information may be out-of-date. You should confirm all information. Software piracy is theft, using crack, warez passwords, patches, serial numbers, registration codes, key generator, keymaker or keygen for OptiVec for Visual C++ license key is illegal and prevent future development of OptiVec for Visual C++. Download links are directly from our mirrors or publisher's website, OptiVec for Visual C++ torrent files or shared files from rapidshare, yousendit or megaupload are not allowed! Website: OptiCode - Dr Martin Sander Software Dev - OptiVec for Visual C++ homepage Released: August 28, 2020 Filesize: 4.94 MB Language: English, German Platform: Win2000, Windows XP, Windows 7 x32, Windows 7 x64, Windows 8, Windows 10, WinServer, WinOther, Windows Vista, Windows Vista x64 Requirements: Microsoft Visual Studio (at least Visual C++ 2005) Install Install and Uninstall Add Your Review or Windows Vista Compatibility Report OptiVec for Visual C++ - Releases History Software: OptiVec for Visual C++ 8.0.1 Date Released: Aug 28, 2020 Status: Major Update Release Notes: Compatibility with latest compiler versions up to VS 2019.Many new functions.Quality enhancements. Software: OptiVec for Visual C++ 7.1.3 Date Released: Mar 22, 2017 Status: Major Update Release Notes:

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Time change the licence terms for their CUDA libraries, so that we might at some point no longer be able to include them in our distributions and/or to support CUDA at all.1.1.4 Choosing the right OptiVec LibraryWhenever you want your application to run on a wide range of supported platforms, and when your vectors and matrices are only of small-to-moderate size, we recommend to use the general-purpose libraries, OVVC4.LIB (for MS Visual C++), VCF4W.LIB (Embarcadero/Borland C++ compiler series), or the units in OPTIVEC\LIB4 (for Delphi). These libraries combine good performance with back-compatibility to older hardware, even down to 486DX, Pentium, Athlon. They are all multi-thread safe and support functional parallelism. If you do not need full floating-point accuracy and that amount of back-compatibility, you can get higher performance by switching to the P8 or P9 libraries (marked by the respective number in the in the library name).For large vectors/matrices on multi-core machines, multi-core optimized libraries actively distribute the work load over the available processor cores for data parallel execution. These libraries are marked by the letter "M", as in OVVC8M.LIB (for MS Visual C++, using SSE3), VCF4M.LIB (for Embarcadero/Borland C++, full FPU accuracy), or the units in OPTIVEC\LIB8M (for Delphi, using SSE3). These libraries are designed for AMD 64 x2, Intel Core2 Duo, or machines equipped with several discrete processors of the Pentium 4+ level. The CUDA libraries are based on the "M" libraries and are marked by the letter "C", as, e.g., in OVVC8C.LIB.The "M" and "C" libraries will still run on single-core machines, but – due to the thread-management overhead – somewhat slower than the general-purpose libraries. Although the "M" libraries are designed with medium to large vectors in mind, the penalty for using them with smaller vectors is almost negligible, as the OptiVec thread-engine automatically executes a function in a single thread, if the vector size is too small for parallel execution to earn back the cost involved in the thread-to-thread communication.If you use the "M" or "C" libraries, your programme must call V_initMT( nAvailProcCores ) before any of the vector functions. Back to VectorLib Table of Contents OptiVec home 2. Elements of OptiVec Routines2.1 Synonyms for Some Data TypesTo increase the versatility and completeness of OptiVec, additional data types are defined in or the unit VecLib:a) C/C++ only:The data type ui (short for "unsigned index") is used for the indexing of vectors and is defined as "unsigned int".The 64-bit integer data type (__int64 in BC++ Builder and MS Visual C++, Int64 in Delphi) is called quad (for "quadword integer") in OptiVec. In 32-bit, the type quad is always signed. Functions for unsigned 64-bit integers are available only in the 64-bit versions of OptiVec.The data type extended,

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Site Index: OptiVec homeMatrixLibCMATHDownloadOrderUpdateSupportVectorLibVectorLib is the vector functions part of OptiVec. This file describes the basic principles of the OptiVec libraries and gives an overview over VectorLib. The new object-oriented interface, VecObj, is described in chapter 3. MatrixLib and CMATH are described separately.This is the English version. Translation of the first three chapters into Portuguese by Artur Weber for Introduction2. The Elements of OptiVec Routines3. C++ only: VecObj, the Object-Oriented Interface for VectorLib4. VectorLib Functions and Routines: A Short Overview4.1 Generation, Initialization and De-Allocation of Vectors4.2 Index-oriented Manipulations4.3 Data-Type Interconversions4.4 More about Integer Arithmetics4.5 Basic Functions of Complex Vectors4.6 Mathematical Functions4.6.1 Rounding4.6.2 Comparisons4.6.3 Direct Bit-Manipulation4.6.4 Basic Arithmetics, Accumulations4.6.5 Geometrical Vector Arithmetics4.6.6 Powers4.6.7 Exponentials and Hyperbolic Functions4.6.8 Logarithms4.6.9 Trigonometric Functions4.7 Analysis4.8 Signal Processing: Fourier Transforms and Related Topics4.9 Statistical Functions and Building Blocks4.10 Data Fitting4.11 Input and Output4.12 Graphics5. Error Handling6. Trouble-Shooting7. The Include-Files and Units of OptiVec1. IntroductionOptiVec offers a powerful set of routines for numerically demanding applications, making the philosophy of vectorized programming available for C/C++ and Pascal/Delphi languages. It serves to overcome the limitations of loop management of conventional compilers – which proved to be one of the largest obstacles in the programmer's way towards efficient coding for scientific and data analysis applications.In contrast to integrated packages like MatLab or others, OptiVec has the advantage of being incorporated into the modern and versatile languages C/C++ and Pascal/Delphi. Both C++ and Fortran do already offer some sort of vector processing, by virtue of iterator classes using templates (C++) and field functions (Fortran90). Both of these, however, are basically a convenient means of letting the compiler write the loop for you and then compile it to the usual inefficient code. The same is true for most implementations of the popular BLAS (Basic Linear Algebra Subroutine) libraries.In comparison to these approaches, OptiVec is superior mainly with respect to execution speed – on the average by a factor of 3, in some cases even up to 8. The performance is no longer limited by the quality of your compiler, but rather by the real speed of the processor!There is a certain overlap in the range of functions offered by OptiVec and by BLAS, LINPACK, and other libraries and source-code collections. However, the latter must be compiled, and, consequently, their performance is determined mainly by the quality of the compiler chosen. To the best of our knowledge, OptiVec, was. in 1996, the first product on the market offering a comprehensive vectorized-functions library realized in a true Assembler implementation.All operators and mathematical functions of C/C++ are implemented in vectorized form; additionally many more mathematical functions are included which normally would have to be calculated by more or less complicated combinations of existing functions. Not. Download Update Support: Order page for. OptiVec Master License for all supported compilers: CMATH Master License: OptiVec for Visual C: CMATH for Visual C: OptiVec for C

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Thread, if the vector size is too small for parallel execution to earn back the cost involved in the thread-to-thread communication.If you use the "M" libraries, your programme must call V_initMT before any of the vector functions. 1.1.3 CUDA Device SupportModern graphics cards are equipped with powerful multiprocessor capacity of up to several hundred processor kernels running in parallel. In recent years, interfaces have been developed, allowing to exploit this processing capacity not only for graphics rendering, but also for general calculations. One of these approaches is the CUDA concept by NVIDIA. Practically all current NVIDIA graphics cards support CUDA. Additionally, dedicated CUDA hardware is being offered by NVIDIA with the "Tesla" and "Fermi" board family. With the "C" libraries (e.g., OVVC8C.LIB), OptiVec offers a simple way to use a CUDA device for vector / matrix calculations without the hassles of actually programming in CUDA. There are a number of points to be considered:Obviously, the "C" libraries can be used only with a CUDA-enabled device installed. This means, only NVIDIA products are supported.Out of the compilers supported by OptiVec, currently, NVIDIA provides CUDA support only for MS Visual C++. This means there are presently no CUDA OptiVec libraries for the Embarcadero / Borland compilers available.It is necessary to have the latest display driver installed. Even brand-new computers most often do not have the latest drivers. They must be selected and downloaded from NVIDIA's web-site, www.nvidia.com. Already a sub-100$ graphics card can boost the performance of certain functions on a computer with a medium-range CPU by a factor of 10, dedicated hardware by much more. However, the combination of a high-end CPU with a low-end graphics card (as it is often found in laptop computers) will, at best, only marginally benefit from the "C" libraries. The cost of swapping data forth and back between main-board memory and graphics memory is so high that it can be "earned" back only for quite large vectors and matrices. E.g., for mathematical functions like the sine or exponential functions, CUDA pays off from 100,000 vector elements on. For matrix multiplication, payback occurs in the region of 200x200 elements. All OptiVec functions check if using the CUDA device makes sense and decide accordingly wether to source-out processing to the graphics processor or to stay on the CPU.Using CUDA with OptiVec is as easy as simply linking with the "C" library and with the cudaOptiVec import library. No modifications of your source code are necessary. On the other hand, by eliminating the repeated data transfers for each function, programming directly for CUDA devices with nVidia's CUDA SDK can lead to considerably higher performance than is possible with the use of the OptiVec "C" libraries.NVIDIA might at any

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While another one is sitting idle, waiting for input. Still, functional multithreading is the best option whenever your numerical tasks involve vectors and matrices of only small-to-moderate size. Data ParallelismIn order to improve the load balance between the available processor cores, thereby maximizing throughput, it is possible to employ classical parallel processing: the data to be processed is split up into several chunks, each thread getting one of these chunks. This is aptly called data parallelism. The usefulness of this approach is limited by the overhead involved in the data distribution and in the thread-to-thread communication. Moreover, there are always parts of the code which need to be processed sequentially and cannot be parallelized. Therefore, data parallelism pays off only for larger vectors and matrices. Typical break-even sizes range from about 100 (for the calculation of transcendental functions of complex input values) to several 10,000 elements (as in the simple arithmetic functions). Only when your vectors and matrices are considerably larger than that threshold, the performance is actually improved over a functional-parallelism approach. The boost then quickly approaches (but never exactly reaches) the theoretical limit of a factor equal to the number of processor cores available.Choosing the right OptiVec LibraryWhenever you want your application to run on a wide range of supported platforms, and when your vectors and matrices are only of small-to-moderate size, we recommend to use the general-purpose libraries, OVVC4.LIB (for MS Visual C++), VCF4W.LIB (for Embarcadero/Borland C++), or the units in OPTIVEC\LIB4 (for Delphi). These libraries combine good performance with back-compatibility to older hardware, down to 486DX, Pentium, old models of Athlon. They are all multi-thread safe and support functional parallelism. If you do not need full floating-point accuracy and that amount of back-compatibility, you can get higher performance by switching to the P6, P7, or P8 libraries (marked by the respective number in the in the library name).Finally, for large vectors/matrices on multi-core machines, our multi-core optimized libraries actively distribute the work load over the available processor cores for data parallel execution. These libraries are marked by the letter "M", as in OVVC7M.LIB (for MS Visual C++, using SSE2), VCF4M.LIB (for Borland C++, full FPU accuracy), or the units in OPTIVEC\LIB8M (for Delphi, using SSE3). These libraries are designed for AMD 64 x2, Intel Core2 Duo, or machines equipped with several discrete processors of the Pentium 4+ level. The "M" libraries will still run on single-core machines, but – due to the thread-management overhead – somewhat slower than the general-purpose libraries. Although the "M" libraries are designed with medium to large vectors in mind, the penalty for using them with smaller vectors is almost negligible, as the OptiVec thread-engine automatically executes a function in a single

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3.7 */Pascal/Delphi specific:As in C/C++, you may mix these vector types with the static arrays of classic Pascal style. Static arrays have to be passed to OptiVec functions with the "address of" operator. Here, the above example reads:a: array[0..99] of Single; (* classic static array *)b: fVector;(* VectorLib vector *)b := VF_vector(100); VF_equ1( @a, 100 ); (* set first 100 elements of a = 1.0 *)VF_equC( b, 100, 3.7 ); (* set first 100 elements of b = 3.7 *)Delphi also offers dynamically-allocated arrays, which may also be used as arguments for OptiVec functions. The following table compares the pointer-based vectors of VectorLib with the array types of Pascal/Delphi: OptiVec vectorsPascal/Delphi static/dynamic arraysalignment of first elementon 32-byte boundary for optimum cache-line matching2 or 4-byte boundary (may cause line-break penalty for double, QuadInt)alignment of following elementspacked (i.e., no dummy bytes between elements, even for 8, 10, and 16-bit typesarrays must be declared as "packed" for Delphi 4+ to be compatible with OptiVecindex range checkingnoneautomatic with built-in size informationdynamic allocationfunction VF_vector, VF_vector0procedure SetLength (Delphi 4+ only)initialization with 0optional by calling VF_vector0always (Delphi 4+ only)de-allocationfunction V_free, V_freeAllprocedure Finalize (Delphi 4+ only)reading single elementsfunction VF_element:a := VF_element(X,5);Delphi 4+ only: typecast into array also possible:a := fArray(X)[5];index in brackets:a := X[5];setting single elementsfunction VF_Pelement:VF_Pelement(X,5)^ := a;Delphi 4+ only: typecast into array also possible:fArray(X)[5] := a;index in brackets:X[5] := a;passing to OptiVec functiondirectly:VF_equ1( X, sz );address-of operator:VF_equ1( @X, sz );passing sub-vector to OptiVec functionfunction VF_Pelement:VF_equC( VF_Pelement(X,10), sz-10, 3.7);address-of operator:VF_equC( @X[10], sz-10, 3.7 ); Summarizing the properties of OptiVec vectors and of Pascal/Delphi arrays, the latter are somewhat more convenient and, due to the index range checking, safer, whereas the pointer-based OptiVec vectors are processed faster (due to the better alignment and to the absence of checking routines).Back to VectorLib Table of Contents OptiVec home 2.4 Vector Function PrefixesIn the plain-C, Pascal and Delphi versions, every OptiVec function has a prefix denoting the data-type on which it acts. (Read here about the overloaded C++ functions of VecObj.) Prefix Arguments and return valueVF_fVector and floatVD_dVector and doubleVE_eVector and extended (long double) VCF_cfVector and fComplexVCD_cdVector and dComplex VCE_ceVector and eComplexVPF_pfVector and fPolarVPD_ pdVector and dPolarVPE_peVector and ePolarVI_iVector and int / IntegerVBI_biVector and byte / ByteIntVSI_siVector and short int / SmallIntVLI_liVector and long int / LongIntVQI_qiVector and quad / QuadIntVU_uVector and unsigned / UIntVUB_ubVector and unsigned char / UByteVUS_usVector and unsigned short / USmallVUL_ulVector and unsigned long / ULongVUQ_uqVector and uquad / UQuad (for Win64 only!)VUI_uiVector and uiV_(data-type conversions like V_FtoD, data-type independent functions like V_initPlot) Back to VectorLib Table of Contents OptiVec home 3. VecObj, theObject-Oriented Interface for VectorLibVecObj, the object-oriented C++ interface to OptiVec vector functions was written by Brian Dale, Case Western Reserve University.Among the advantages it. Download Update Support: Order page for. OptiVec Master License for all supported compilers: CMATH Master License: OptiVec for Visual C: CMATH for Visual C: OptiVec for C OptiVec for Visual C, download gratis. OptiVec for Visual C 8.0.1: OptiVec contains more than 3500 hand-optimized, Assembler-written functions for all