How To Run Openmp Program In Dev C%2b%2b
In spite of all the warnings, you received a success notification from your C build. If you want an OpenMP build from the VS GUI setup, you must go into C properties and turn on the OpenMP option for your project. Further discussion about the ICL compiler is more likely to get expert advice if you post on the C forum. C compilers are able to compile C code. Now, one thing, totally unrelated but please for the love of god stop using Dev-C. Once you've got your compiler and source program ready, it is very easy to compile and run a C program. Assuming that you've installed GCC compiler, and you have a source.cpp file that you want to compile, follow the following instructions to compile and run it.
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| Developer(s) | Intel |
|---|---|
| Stable release | |
| Operating system | Windows, Linux |
| Type | Compiler |
| License | Freeware, optional priority support |
| Website | software.intel.com/content/www/us/en/develop/tools/oneapi/components/dpc-compiler.html |
| Developer(s) | Intel |
|---|---|
| Stable release | 2021.1 / December 4, 2020; 32 days ago[2] |
| Operating system | operating system = Windows, Mac, Linux, FreeBSD |
| Type | Compiler |
| License | Freeware, optional priority support |
| Website | software.intel.com/content/www/us/en/develop/tools/oneapi/components/dpc-compiler.html |
Intel C++ Compiler, is a group of C, C++, SYCL and Data Parallel C++ (DPC++) compilers from Intel available for Windows, Mac, Linux, and FreeBSD.[3]
Overview[edit]
The compilers generate code for IA-32 and Intel 64 architectures, including compatible processors from AMD. A specific release of the compiler (11.1) is available for development of Linux-based applications for IA-64 (Itanium 2) processors. /sony-spectralayers-pro-4063-crack.html.
The 2021 oneAPI release[4] added the ability to generate optimized code for Intel GPUs and FPGAs. Intel compilers support SYCL, initial OpenMP 5.1, symmetric multiprocessing, automatic parallelization, and Guided Auto-Parallelization (GAP). With the add-on Cluster OpenMP capability, the compilers can also automatically generate Message Passing Interface calls for distributed memory multiprocessing from OpenMP directives.
DPC++[5][6] builds on the SYCL specification from The Khronos Group. It is designed to allow developers to reuse code across hardware targets (CPUs and accelerators such as GPUs and FPGAs) and perform custom tuning for a specific accelerator. DPC++ = C++17 and SYCL and open source community extensions that make SYCL easier to use. Many of these extensions were adopted by the SYCL 2020 provisional specification[7] including unified shared memory, group algorithms and sub-groups.
The Intel compilers are compatible with Microsoft Visual C++ on Windows and integrate into Microsoft Visual Studio. On Linux and Mac, they are compatible with GNU Compiler Collection (GCC) and the GNU toolchain. Intel compilers are known for the application performance they can enable as measured by benchmarks, such as the SPEC CPU benchmarks.
Optimizations[edit]
Intel compilers are optimized for computer systems using processors that support Intel architectures. They are designed to minimize stalls and to produce code that executes in the fewest possible number of cycles. The Intel C++ Compiler supports three separate high-level techniques for optimizing the compiled program: interprocedural optimization (IPO), profile-guided optimization (PGO), and high-level optimizations (HLO). The Intel C++ compiler in the Parallel Studio XE products also supports tools, techniques and language extensions for adding and maintaining application parallelism on IA-32 and Intel 64 processors and enables compiling for Intel Xeon Phi processors and coprocessors.
Profile-guided optimization refers to a mode of optimization where the compiler is able to access data from a sample run of the program across a representative input set. The data would indicate which areas of the program are executed more frequently, and which areas are executed less frequently. All optimizations benefit from profile-guided feedback because they are less reliant on heuristics when making compilation decisions.
High-level optimizations are optimizations performed on a version of the program that more closely represents the source code. This includes loop interchange, loop fusion, loop fission, loop unrolling, data prefetch, and more.[8]
Interprocedural optimization applies typical compiler optimizations (such as constant propagation) but using a broader scope that may include multiple procedures, multiple files, or the entire program.[9]
David Monniaux has criticized Intel's compiler for applying, by default, floating-point optimizations which are not allowed by the C standard and which require special flags with other compilers such as gcc.[10]
Architectures[edit]
- x86-64 (Intel 64 and AMD64)
- IA-64 (Itanium 2)
Description of packaging[edit]
The compilers are available standalone from Intel APT and Yum repositories. They are also available in the Intel oneAPI Base Toolkit which includes other build tools, such as libraries, and analysis tools for error checking and performance analysis. Download game clash of clans mod terbaru 2018. Containers with the compilers are on Docker Hub.
History since 2003[edit]
| Compiler version | Release date | Major new features |
|---|---|---|
| Intel C++ Compiler 8.0 | December 15, 2003 | Precompiled headers, code-coverage tools. |
| Intel C++ Compiler 8.1 | September, 2004 | AMD64 architecture (for Linux). |
| Intel C++ Compiler 9.0 | June 14, 2005 | AMD64 architecture (for Windows), software-based speculative pre-computation (SSP) optimization, improved loop optimization reports. |
| Intel C++ Compiler 10.0 | June 5, 2007 | Improved parallelizer and vectorizer, Streaming SIMD Extensions 4 (SSE4), new and enhanced optimization reports for advanced loop transformations, new optimized exception handling implementation. |
| Intel C++ Compiler 10.1 | November 7, 2007 | New OpenMP* compatibility runtime library: if you use the new OpenMP RTL, you can mix and match with libraries and objects built by Visual C++. To use the new libraries, you need to use the new option '-Qopenmp /Qopenmp-lib:compat' on Windows, and '-openmp -openmp-lib:compat' on Linux. This version of the Intel compiler supports more intrinsics from Visual Studio 2005. VS2008 support - command line only in this release. The IDE integration was not supported yet. |
| Intel C++ Compiler 11.0 | November 2008 | Initial C++11 support. VS2008 IDE integration on Windows. OpenMP 3.0. Source Checker for static memory/parallel diagnostics. |
| Intel C++ Compiler 11.1 | June 23, 2009 | Support for latest Intel SSE SSE4.2, AVX and AES instructions. Parallel Debugger Extension. Improved integration into Microsoft Visual Studio, Eclipse CDT 5.0 and Mac Xcode IDE. |
| Intel C++ Composer XE 2011 up to Update 5 (compiler 12.0) | November 7, 2010 | Cilk Plus language extensions, Guided Auto-Parallelism, Improved C++11 support.[11] |
| Intel C++ Composer XE 2011 Update 6 and above (compiler 12.1) | September 8, 2011 | Cilk Plus language extensions updated to support specification version 1.1 and available on Mac OS X in addition to Windows and Linux, Threading Building Blocks updated to support version 4.0, Apple blocks supported on Mac OS X, improved C++11 support including support for Variadic templates, OpenMP 3.1 support. |
| Intel C++ Composer XE 2013 (compiler 13.0) | September 5, 2012 | Linux-based support for Intel Xeon Phi coprocessors, support for Microsoft Visual Studio 12 (Desktop), support for gcc 4.7, support for Intel AVX 2 instructions, updates to existing functionality focused on improved application performance.[12] |
| Intel C++ Composer XE 2013 SP1 (compiler 14.0) | September 4, 2013 | Online installer; support for Intel Xeon Phi coprocessors; preview Win32 only support for Intel graphics; improved C++11 support |
| Intel C++ Composer XE 2013 SP1 Update 1 (compiler 14.0.1) | October 18, 2013 | Japanese localization of 14.0; Windows 8.1 and Xcode 5.0 support |
| Intel C++ Compiler for Android (compiler 14.0.1) | November 12, 2013 | Hosted on Windows, Linux, or OS X, compatible with Android NDK tools including the gcc compiler and Eclipse |
| Intel C++ Composer XE 2015 (compiler 15.0) | July 25, 2014 | Full C++11 language support; Additional OpenMP 4.0 and Cilk Plus enhancements |
| Intel C++ Composer XE 2015 Update 1 (compiler 15.0.1) | October 30, 2014 | AVX-512 support; Japanese localization |
| Intel C++ 16.0 | August 25, 2015 | Suite-based availability (Intel Parallel Studio XE, Intel System Studio) |
| Intel C++ 17.0 | September 15, 2016 | Suite-based availability (Intel Parallel Studio XE, Intel System Studio) |
| Intel C++ 18.0 | January 26, 2017 | Suite-based availability (Intel Parallel Studio XE, Intel System Studio) |
| Intel C++ 19.0 | April 3, 2018 | Suite-based availability (Intel Parallel Studio XE, Intel System Studio) |
| Intel C++ Compiler Classic 19.1 | October 22, 2020 | Initial Open MP 5.1 CPU only |
| Intel oneAPI DPC++ / C++ Compiler 2021 | December 8, 2020 | SYCL, DPC++, initial Open MP 5.1 |
Flags and manuals[edit]
Documentation can be found at the Intel Software Technical Documentation site.
| Windows | Linux, macOS & FreeBSD | Comment |
|---|---|---|
| /Od | -O0 | No optimization |
| /O1 | -O1 | Optimize for size |
| /O2 | -O2 | Optimize for speed and enable some optimization |
| /O3 | -O3 | Enable all optimizations as O2, and intensive loop optimizations |
| /arch:SSE3 | /-msse3 | Enables SSE3, SSE2 and SSE instruction sets optimizations for non-Intel CPUs[13] |
| /fast | -fast | Shorthand. On Windows this equates to '/O3 /Qipo /QxHost /Opred-div-' ; on Linux '-O3 -ipo -static -xHOST -no-prec-div'. Note that the processor specific optimization flag (-xHOST) will optimize for the processor compiled on—it is the only flag of -fast that may be overridden |
| /Qprof-gen | -prof_gen | Compile the program and instrument it for a profile generating run |
| /Qprof-use | -prof_use | May only be used after running a program that was previously compiled using prof_gen. Uses profile information during each step of the compilation process |
Debugging[edit]
The Intel compiler provides debugging information that is standard for the common debuggers (DWARF 2 on Linux, similar to gdb, and COFF for Windows). The flags to compile with debugging information are /Zi on Windows and -g on Linux. Debugging is done on Windows using the Visual Studio debugger and, on Linux, using gdb.
While the Intel compiler can generate a gprof compatible profiling output, Intel also provides a kernel level, system-wide statistical profiler called Intel VTune Profiler. VTune can be used from a command line or thru an included GUI on Linux or Windows. It can also be integrated into Visual Studio on Windows, or Eclipse on Linux). In addition to the VTune profiler, there is Intel Advisor that specializes in vectorization optimization, offload modeling, flow graph design and tools for threading design and prototyping.
Intel also offers a tool for memory and threading error detection called Intel Inspector XE. Regarding memory errors, it helps detect memory leaks, memory corruption, allocation/de-allocation of API mismatches and inconsistent memory API usage. Regarding threading errors, it helps detect data races (both heap and stack), deadlocks and thread and synch API errors.
Reception[edit]
Intel and third parties have published benchmark results to substantiate performance leadership claims over other commercial, open-source and AMD compilers and libraries on Intel and non-Intel processors. Intel and AMD have documented flags to use on the Intel compilers to get optimal performance on Intel and AMD processors.[14][15] Nevertheless, the Intel compilers have been known to use sub-optimal code for processors from vendors other than Intel. For example, Steve Westfield wrote in a 2005 article at the AMD website:[16]
Intel 8.1 C/C++ compiler uses the flag -xN (for Linux) or -QxN (for Windows) to take advantage of the SSE2 extensions. For SSE3, the compiler switch is -xP (for Linux) and -QxP (for Windows). .. With the -xN/-QxN and -xP/-QxP flags set, it checks the processor vendor string—and if it's not 'GenuineIntel', it stops execution without even checking the feature flags. Ouch!
The Danish developer and scholar Agner Fog wrote in 2009:[17]
The Intel compiler and several different Intel function libraries have suboptimal performance on AMD and VIA processors. The reason is that the compiler or library can make multiple versions of a piece of code, each optimized for a certain processor and instruction set, for example SSE2, SSE3, etc. The system includes a function that detects which type of CPU it is running on and chooses the optimal code path for that CPU. This is called a CPU dispatcher. However, the Intel CPU dispatcher does not only check which instruction set is supported by the CPU, it also checks the vendor ID string. If the vendor string is 'GenuineIntel' then it uses the optimal code path. If the CPU is not from Intel then, in most cases, it will run the slowest possible version of the code, even if the CPU is fully compatible with a better version.
This vendor-specific CPU dispatching (function multi-versioning) decreases the performance on non-Intel processors of software built with an Intel compiler or an Intel function library – possibly without the knowledge of the programmer. This has allegedly led to misleading benchmarks,[17] including one incident when changing the CPUID of a VIA Nano significantly improved results.[18] A legal battle between AMD and Intel over this and other issues has been settled in November 2009.[19] In late 2010, AMD settled a US Federal Trade Commissionantitrust investigation against Intel.[20]
The FTC settlement included a disclosure provision where Intel must:[21]
publish clearly that its compiler discriminates against non-Intel processors (such as AMD's designs), not fully utilizing their features and producing inferior code.
In compliance with this rule, Intel added an 'optimization notice' to its compiler descriptions stating that they 'may or may not optimize to the same degree for non-Intel microprocessors' and that 'certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors'. It says that:[22]
Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.
As reported by The Register[23] in July 2013, Intel was suspected of 'benchmarksmanship', when it was shown that the object code produced by the Intel compiler for the AnTuTu Mobile Benchmark omitted portions of the benchmark which showed increased performance compared to ARM platforms.
See also[edit]
- Intel Integrated Performance Primitives (IPP)
- Intel Data Analytics Acceleration Library (DAAL)
- Intel Math Kernel Library (MKL)
- Intel Threading Building Blocks (TBB)
- VTune Amplifier
- Intel Developer Zone (Intel DZ; support and discussion)
References[edit]
- ^'Intel® oneAPI Toolkits DPC++/C++ Compiler Release Notes'. Intel. Retrieved 2020-12-17.
- ^'Intel® oneAPI C++ Compiler Release Notes'. Intel. Retrieved 2020-12-29.
- ^'Intel® System Studio 2016 for FreeBSD* Intel® Software'. software.intel.com. Retrieved 2018-03-15.
- ^'Intel Debuts oneAPI Gold and Provides More Details on GPU Roadmap'. HPCwire. 2020-11-11. Retrieved 2020-12-17.
- ^'Intel oneAPI DPC++ Compiler 2020-06 Released With New Features - Phoronix'. www.phoronix.com. Retrieved 2020-12-17.
- ^Team, Editorial (2019-12-16). 'Heterogeneous Computing Programming: oneAPI and Data Parallel C++'. insideBIGDATA. Retrieved 2020-12-17.
- ^'Khronos Steps Towards Widespread Deployment of SYCL with Release of SYCL 2020 Provisional Specification'. The Khronos Group. 2020-06-30. Retrieved 2020-12-17.
- ^The Software Optimization Cookbook, High-Performance Recipes for IA-32 Platforms, Richard Gerber, Aart J.C. Bik, Kevin B. Smith, and Xinmin Tian, Intel Press, 2006
- ^Intel C++ Compiler XE 13.0 User and Reference Guides
- ^The pitfalls of verifying floating-point computations, by David Monniaux, also printed in ACM Transactions on programming languages and systems (TOPLAS), May 2008; section 4.3.2 discusses nonstandard optimizations.
- ^This note is attached to the release in which Cilk Plus was introduced. This ULR points to current documentation: http://software.intel.com/en-us/intel-composer-xe/
- ^Intel C++ Composer XE 2013 Release Notes[1]http://software.intel.com/en-us/articles/intel-c-composer-xe-2013-release-notes/
- ^'Intel® Compilers Intel® Developer Zone'. Intel.com. 1999-02-22. Retrieved 2012-10-13.
- ^[2]Archived March 23, 2010, at the Wayback Machine
- ^'Archived copy'(PDF). Archived from the original(PDF) on 2011-03-22. Retrieved 2011-03-30.CS1 maint: archived copy as title (link)
- ^'Your Processor, Your Compiler, and You: The Case of the Secret CPUID String'. Archived from the original on 2012-01-05. Retrieved 2011-12-11.
- ^ ab'Agner`s CPU blog - Intel's 'cripple AMD' function'. www.agner.org.
- ^Hruska, Joel (29 July 2008). 'Low-end grudge match: Nano vs. Atom'. Ars Technica.
- ^'Settlement agreement'(PDF). download.intel.com.
- ^'Intel and U.S. Federal Trade Commission Reach Tentative Settlement'. Newsroom.intel.com. 2010-08-04. Retrieved 2012-10-13.
- ^'FTC, Intel Reach Settlement; Intel Banned From Anticompetitive Practices'. Archived from the original on 2012-02-03. Retrieved 2011-10-20.
- ^'Optimization Notice'. Intel Corporation. Retrieved 11 December 2013.
- ^'Analyst: Tests showing Intel smartphones beating ARM were rigged'.
External links[edit]
- Tim Mattson, lectures on YouTube
- Further reading at openmp.org: intro
OpenMP in a nutshell
OpenMP is a library for parallel programming in the SMP (symmetric multi-processors, or shared-memory processors) model. When programming with OpenMP, all threads share memory and data. OpenMP supports C, C++ and Fortran. The OpenMP functions are included in a header file called
omp.h . OpenMP program structure: An OpenMP program has sections that are sequential and sections that are parallel. In general an OpenMP program starts with a sequential section in which it sets up the environment, initializes the variables, and so on.
When run, an OpenMP program will use one thread (in the sequential sections), and several threads (in the parallel sections).
There is one thread that runs from the beginning to the end, and it's called the master thread. The parallel sections of the program will cause additional threads to fork. These are called the slave threads.
A section of code that is to be executed in parallel is marked by a special directive (omp pragma). When the execution reaches a parallel section (marked by omp pragma), this directive will cause slave threads to form. Each thread executes the parallel section of the code independently. When a thread finishes, it joins the master. When all threads finish, the master continues with code following the parallel section.
Each thread has an ID attached to it that can be obtained using a runtime library function (called omp_get_thread_num()). The ID of the master thread is 0.
Why OpenMP? More efficient, and lower-level parallel code is possible, however OpenMP hides the low-level details and allows the programmer to describe the parallel code with high-level constructs, which is as simple as it can get.
OpenMP has directives that allow the programmer to:
- specify the parallel region
- specify whether the variables in the parallel section are private or shared
- specify how/if the threads are synchronized
- specify how to parallelize loops
- specify how the works is divided between threads (scheduling)
Compiling and running OpenMP code
The OpenMP functions are included in a header file called omp.h . The public linux machines dover and foxcroft have gcc/g++ installed with OpenMP support. All you need to do is use the -fopenmp flag on the command line:It’s also pretty easy to get OpenMP to work on a Mac. A quick search with google reveals that the native apple compiler clang is installed without openmp support. When you installed gcc it probably got installed without openmp support. To test, go to the terminal and try to compile something: If you get an error message saying that “omp.h” is unknown, that mans your compiler does not have openmp support. Here’s what I did:
1. I installed Homebrew, the missing package manager for MacOS, http://brew.sh/index.html 2. Then I asked brew to install gcc: 3. Then type ‘gcc’ and press tab; it will complete with all the versions of gcc installed: 4. The obvious guess here is that gcc-6 is the latest version, so I use it to compile: Works!
Specifying the parallel region (creating threads)
How To Run Openmp Program In Dev C 2b 2b 2c
The basic directive is: When the master thread reaches this line, it forks additional threads to carry out the work enclosed in the block following the #pragma construct. The block is executed by all threads in parallel. The original thread will be denoted as master thread with thread-id 0.Example (C program): Display 'Hello, world.' using multiple threads. Use flag -fopenmp to compile using gcc: Output on a computer with two cores, and thus two threads: On dover, I got 24 hellos, for 24 threads. On my desktop I get (only) 8. How many do you get?
Note that the threads are all writing to the standard output, and there is a race to share it. The way the threads are interleaved is completely arbitrary, and you can get garbled output:
Private and shared variables
How To Run Openmp Program In Dev C 2b 2b 1
In a parallel section variables can be private or shared:- private: the variable is private to each thread, which means each thread will have its own local copy. A private variable is not initialized and the value is not maintained for use outside the parallel region. By default, the loop iteration counters in the OpenMP loop constructs are private.
- shared: the variable is shared, which means it is visible to and accessible by all threads simultaneously. By default, all variables in the work sharing region are shared except the loop iteration counter. Shared variables must be used with care because they cause race conditions.
The type of the variable, private or shared, is specified following the #pragma omp:
Example: Private or shared? Sometimes your algorithm will require sharing variables, other times it will require private variables. The caveat with sharing is the race conditions. The task of thinking through the details of a parallel algorithm and specifying the type of the variables is on, of course, the programmer.
Synchronization
OpenMP lets you specify how to synchronize the threads. Here’s what’s available:- critical: the enclosed code block will be executed by only one thread at a time, and not simultaneously executed by multiple threads. It is often used to protect shared data from race conditions.
- atomic: the memory update (write, or read-modify-write) in the next instruction will be performed atomically. It does not make the entire statement atomic; only the memory update is atomic. A compiler might use special hardware instructions for better performance than when using critical.
- ordered: the structured block is executed in the order in which iterations would be executed in a sequential loop
- barrier: each thread waits until all of the other threads of a team have reached this point. A work-sharing construct has an implicit barrier synchronization at the end.
- nowait: specifies that threads completing assigned work can proceed without waiting for all threads in the team to finish. In the absence of this clause, threads encounter a barrier synchronization at the end of the work sharing construct.
How To Run Openmp Program In Dev C 2b 2b 1b
Barrier example: Note above the function omp_get_num_threads(). Can you guess what it’s doing? Some other runtime functions are:
- omp_get_num_threads
- omp_get_num_procs
- omp_set_num_threads
- omp_get_max_threads
Parallelizing loops
Parallelizing loops with OpenMP is straightforward. One simply denotes the loop to be parallelized and a few parameters, and OpenMP takes care of the rest. Can't be easier!The directive is called a work-sharing construct, and must be placed inside a parallel section: The “#pragma omp for” distributes the loop among the threads. It must be used inside a parallel block: Example: Another example (here): adding all elements in an array. There exists also a “parallel for” directive which combines a parallel and a for (no need to nest a for inside a parallel): Exactly how the iterations are assigned to ecah thread, that is specified by the schedule (see below). Note:Since variable i is declared inside the parallel for, each thread will have its own private version of i.
Loop scheduling
OpenMP lets you control how the threads are scheduled. The type of schedule available are:- static: Each thread is assigned a chunk of iterations in fixed fashion (round robin). The iterations are divided among threads equally. Specifying an integer for the parameter chunk will allocate chunk number of contiguous iterations to a particular thread. Note: is this the default? check.
- dynamic: Each thread is initialized with a chunk of threads, then as each thread completes its iterations, it gets assigned the next set of iterations. The parameter chunk defines the number of contiguous iterations that are allocated to a thread at a time.
- guided: Iterations are divided into pieces that successively decrease exponentially, with chunk being the smallest size.
How To Run Openmp Program In Dev C 2b 2b Answer
after the pragma for directive:More complex directives
..which you probably won't need.- can define “sections” inside a parallel block
- can request that iterations of a loop are executed in order
- specify a block to be executed only by the master thread
- specify a block to be executed only by the first thread that reaches it
- define a section to be “critical”: will be executed by each thread, but can be executed only by a single thread at a time. This forces threads to take turns, not interrupt each other.
- define a section to be “atomic”: this forces threads to write to a shared memory location in a serial manner to avoid race conditions
Performance considerations
Critical sections and atomic sections serialize the execution and eliminate the concurrent execution of threads. If used unwisely, OpenMP code can be worse than serial code because of all the thread overhead.Some comments
OpenMP is not magic. A loop must be obviously parallelizable in order for OpenMP to unroll it and facilitate the assignment of iterations among threads. If there are any data dependencies from one iteration to the next, then OpenMP can't parallelize it.The for loop cannot exit early, for example: Values of the loop control expressions must be the same for all iterations of the loop. For example: