What is the translation of " OPENMP " in Chinese?

• openmp

Examples of using Openmp in English and their translations into Chinese

What about OpenMP?
OpenMP是什么？?

OpenMP parallerization is supported.
支持OpenMP并行化计算。

So what is OpenMP?
OpenMP是什么？?

OpenMP and MPI are two means of parallel programming.
OpenMP和MPI是并行编程的两个手段，对比如下：.

Requires a compiler that supports OpenMP.
需要一个支持OpenMP的编译器.

OpenMP 3.1 parallelization directives are offered for improved parallel programming.
提供OpenMP3.1并行指令，以改善并行编程。

Since 2006 he is a member of the OpenMP Language Committee.
自2005年以来，他一直是OpenMP语言委员会的成员。

OpenMP(Open Multi-Processing) is an API for shared memory multiprocessing programming.
OpenMP（开放式多处理）是用于共享内存多处理编程的API。

Both task parallelism and data parallelism can be achieved using OpenMP in this way.
通过这种方式，使用OpenMP可以实现任务并行和数据并行。

OpenMP is convenient because it does not lock the software into a preset number of threads.
OpenMP非常方便，因为它不会将软件锁定在事先设定的线程数量中。

After more than a decade, every major compiler for C, C++ and Fortran supports OpenMP.
在十多年以后的今天，面向C、C++和Fortran语言的每个主要编译器都支持OpenMP标准。

OpenMP uses shared storage, meaning it only adapts to SMP, DSM machines and is not suitable for clustering.
OpenMP采用共享存储，意味着它只适应于SMP,DSM机器，不适合于集群。

We are proud to continue that effort in helping to define community standards by joining the OpenMP ARB.”.
我们为加入OpenMPARB而继续努力帮助定义社区标准感到自豪。

In 1996, the OpenMP standard was proposed as a way for compilers to assist in the utilization of parallel hardware.
年提出OpenMP标准时，它是用于支持编译器协助利用并行硬件的。

The framework integrates with CUDA for GPU optimization and can be distributed with OpenMP or Hadoop.
该框架集成了CUDA来实现GPU优化，而且可通过OpenMP或Hadoop进行分发。

OpenMP 3.0 is supported to help simplify pragma-based development of parallelism in your C/C++ applications.
支持OpenMP3.0，有助于简化C/C++应用程序中基于pragma的并行性开发。

It is worth noting that for C++ programmers, using OpenMP and Intel TBB in the same program is possible as well.
值得注意的是，C++编程人员而言可以在同一个程序中使用OpenMP和英特尔TBB。

The OpenMP ARB's mission is to standardize directive-based, multi-language, high-level parallelism that is performant, productive, and portable.
OpenMPARB的任务是标准化基于指令的，多语言的，高级的，并行的，高性能的，高效的和可移植的。

The main elements of OpenACC, as with the OpenMP 4 target additions, are data management and parallelism management.
和OpenMP4的目标附件一样，OpenACC的主要元素是数据管理和并行管理。

Before C++11 standard the multithreaded applications were based on platform-specific extensions, like Intel TBB, OpenMP, Pthreads, etc.
在C++11标准之前，编写多线程应用需要依赖于特定的平台扩展，如IntelTBB、OpenMP、Pthreads等等。

In the future, the OpenMP specification will expand to standardize the emerging controls for attached computing often called“offloading” or“accelerating.”.
未来，OpenMP规范将进行扩展，以规范面向附加计算（通常称为“卸载”或“加速”）的新兴控件。

Founded in 1989, PGI has a long history of innovation in HPC compiler technology for Intel, IBM, Linux, OpenMP, GPGPU, and ARM.
PGI创立于1989年，在高性能计算编译器技术创新方面拥有悠久的历史，客户包括Intel、IBM、Linux、OpenMP、GPGPU以及ARM。

The OpenMP Architecture Review Board(ARB) published its first API specifications, OpenMP for Fortran 1.0, in October 1997.
OpenMPArchitectureReviewBoard(ARB)于1997年10月发布了OpenMPforFortran1.0。

We look forward to incorporating accelerator support with the full support of all OpenMP members in a future version of the OpenMP specification.".
凭借所有OpenMP成员的充分支持，我们期待能够在未来版本的OpenMP规范中融入加速器支持。

The OpenMP ARB owns the OpenMP brand, oversees the OpenMP specification and produces and approves new versions of the specification.
OpenMPARB拥有OpenMP品牌，监督OpenMP规范，并生产和批准该规范的新版本。

Tried and true solutions, including C, C++, Fortran, OpenMP, MPI and Intel TBB apply to these Intel architecture multicore and many-core systems.
尝试的和真正的解决方案（包括C、C++、Fortran、OpenMP、MPI和英特尔TBB）都可应用于这些英特尔架构多核和众核系统中。

OpenMP uses a portable, scalable model that gives programmers a simple and flexible interface for developing parallel applications for platforms ranging from the standard desktop computer to the supercomputer.
OpenMP采用可移植的、可扩展的模型，为程序员提供了一个简单而灵活的开发平台，从标准桌面电脑到超级计算机的并行应用程序接口。

With more desire than ever to process large data-sets OpenMP, OpenCL, Go, CUDA, and other parallel processing languages and frameworks will continue to become mainstream.
随着处理大型数据集的需求越来越多，OpenMP、OpenCL、Go、CUDA和其他一些并行处理语言和框架将成为主流。