Announcing the Preliminary IWOMP 2025 Program
 IWOMP, the annual workshop dedicated to the promotion and advancement of parallel programming with OpenMP, will take place on October 1-3 in Charlotte, NC. IWOMP is the premier forum to present and discuss issues, trends, recent research ideas, and results related to parallel programming with OpenMP.
See the IWOMP 2025 Program!
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The OpenMP ARB Welcomes Three New Members
NextSilicon, BayLibre, and UNC Charlotte are the most recent members of the OpenMP ARB, bringing our total member count to 32. We welcome their expertise and contributions toward the development of the OpenMP API standard. [Learn more about OpenMP ARB membership]
See the OpenMP ARB Members
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GCC 15.1 Brings Support for OpenMP 6.0 Offloading
 The GCC 15.1 release brings major new OpenMP related additions. These additions include support for unified-shared memory for some AMD and NVIDIA GPU devices, for the self_maps clause, for unroll and tile loop-transforming constructs, for the metadirectives, and more.
See the GCC release notes
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Over 140 Attendees at the OpenMP BOF at ISC'25
The OpenMP BOF at ISC'25 in Hamburg had over 140 attendees. The session, titled "What to Expect from OpenMP API Version 6.0" covered the following:
- A dive into key features of OpenMP API version 6.0
- A preview of what's coming in 6.1 and 7.0
- Updates from toolchain developers
- Lively Q&A to help shape future OpenMP directions.
Learn more about ISC
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Member News:
Intel oneAPI HPC Toolkit 2025.2 includes new OpenMP Features
 In Intel's latest oneAPI Toolkit, the DPC++ and Fortran compilers added functionality to enhance OpenMP API support. This includes:
- Adding the OpenMP 6.0 stripe loop-transforming construct for tuning GPU offload performance.
- Enabling a boolean argument to the nowait clause to choose between asynchronous or synchronous offloading.
Download the Intel oneAPI HPC Toolkit
Learn more about these features
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Application News:
NumPY 2.3 Adds Support for OpenMP
 Great news for OpenMP and Python users: NumPy 2.3 now includes support for OpenMP, making sorting operations like np.sort and np.argsort faster by using multiple processor cores — a big step for performance! This new feature is off by default but can be turned on during installation with a simple setting:
-Denable_openmp=true.
Read the NumPy release notes
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