A number of Compilers and Tools from various vendors and open source community initiatives implement the OpenMP API. If we are missing any please Contact Us with your suggestions.

Updated: November 2022


Vendor / Source




ROCm is the open source software stack for AMD CPU+GPU products. The ROCm Compiler Collection ships an LLVM enhanced compiler for C/C++/Fortran that supports OpenMP and offloading to multiple GPU acceleration targets (multi-target). Read More

AOMP is AMD’s LLVM/Clang based compiler that supports OpenMP and offloading to multiple GPU acceleration targets (multi-target). Read More

The AMD Optimizing C/C++ Compiler (AOCC) is a high performance compiler suite supporting C/C++ and Fortran applications, and providing advanced optimizations. This is a clang/LLVM and flang based compiler suite with complete OpenMP 4.5 and partial OpenMP 5.0 support for C/C++ and complete OpenMP 4.0 and partial OpenMP 4.5 support for Fortran. Read More

C/C++/Fortran – Available on Linux

C/C++ – Support for OpenMP 3.1 and all non-offloading features of OpenMP 4.0/4.5/5.0. Offloading features are under development. Fortran – Full support for OpenMP 3.1 and limited support for OpenMP 4.0/4.5. Compile and link your code with -fopenmp Read More

Mercurium – C/C++/Fortran

Mercurium is a source-to-source research compiler that is available to download at https://github.com/bsc-pm/mcxx. OpenMP 3.1 is almost fully supported for C, C++, Fortran. Apart from that, almost all tasking features introduced in newer versions of OpenMP are also supported. Read More

Flang – Classic

Flang – Fortran

Classic Flang is a Fortran compiler for LLVM. Classic Flang implements substantially full OpenMP 4.5 on Linux/x86-64, Linux/ARM, Linux/OpenPOWER with limited target offload support on NVIDIA GPUs.

By default, TARGET regions are mapped to the multicore host CPU as the target with DO and DISTRIBUTE loops parallelized across all OpenMP threads. SIMD works by passing vectorisation metadata to LLVM. Known limitations: DECLARE SIMD has no effect on SIMD code generation; TASK DEPEND/PRIORITY, TASKLOOP FIRSTPRIVATE/LASTPRIVATE, DECLARE REDUCTION and the LINEAR/SCHEDULE/ORDERED(N) clauses on the DO construct are not supported. The limited support for target offload to NVIDIA GPUs includes basic support for offload of !$omp target combined constructs.

Compile with -mp to enable OpenMP for multicore CPUs on all platforms. Compile with -fopenmp -fopenmp-targets=nvptx64-nvidia-cuda to enable target offload to NVIDIA GPUs. Read More.


The compilers in the software package of ‘Technical Computing Suite for the PRIMEHPC FX1000/700′ support OpenMP 4.5 features(*1) and some OpenMP 5.0 features. Read More.
*1 Device constructs is excluded

GCC – C/C++/Fortran

The free and open-source GNU Compiler Collection (GCC) supports among others Linux, Solaris, AIX, MacOSX, Windows, FreeBSD, NetBSD, OpenBSD, DragonFly BSD, HPUX, RTEMS, for architectures such as x86_64, PowerPC, ARM, and many more.

Code offloading to NVIDIA GPUs (nvptx) and the AMD Radeon (GCN) GPUs Fiji, Vega and Instinct (MI 100 and MI 200 series) is supported on Linux.

OpenMP 4.0 is fully supported for C, C++ and Fortran since GCC 4.9; OpenMP 4.5 is fully supported for C and C++ since GCC 6 and partially for Fortran since GCC 7. OpenMP 5.0 is partially supported for C and C++ since GCC 9 and extended in GCC 10. Since GCC 11, OpenMP 4.5 is fully supported for Fortran and OpenMP 5.0 support has been extended for C, C++ and Fortran. GCC 12 has the initial support of OpenMP 5.1 and extends the OpenMP 5.0 coverage. GCC 13 implements several of the OpenMP 5.2 features and extends the OpenMP 5.0 and 5.1 support; furthermore, AMD’s Instinct MI 200 series is now supported.

The GCC 14 development branch extends the OpenMP 5.0/5.1/5.2 coverage and contains performance improvements for offloading. The devel/omp/gcc-13 (OG13) branch augments the GCC 13 branch with OpenMP and offloading features, including relevant backports from the GCC 14 development branch.

Compile with -fopenmp to enable OpenMP.

GCC binary builds are provided by Linux distributions, often with offloading support provided by additional packages, and by multiple entities for other platforms – and you can build it from source.

CCE – C/C++/Fortran

CCE is part of the HPE Cray Programming Environment. CCE Compiling Environment (CCE) 15.0 (November 2022) supports OpenMP 4.5 for C, C++ and Fortran and near-complete support for OpenMP 5.0 (see links below). OpenMP is turned off by default for all languages. For more information on OpenMP support in current and past versions of CCE, see: CCE Release Overview and OpenMP Support in Cray Fortran compiler

XL – C/C++/Fortran

XL C/C++ for Linux V16.1.1 and XL Fortran for Linux V16.1.1 fully support OpenMP 4.5 features including the target constructs. Compile with -qsmp=omp to enable OpenMP directives and with -qoffload for offloading the target regions to GPUs. For more information, please visit: IBM XL C/C++ for Linux and IBM XL Fortran for Linux


Windows, Linux, and MacOSX.

  • OpenMP 3.1 C/C++/Fortran fully supported in version 12.0, 13.0, 14.0 compilers
  • OpenMP 4.0 C/C++/Fortran supported in version 15.0 and 16.0 compilers
  • OpenMP 4.5 C/C++/Fortran supported in version 17.0, 18.0, and 19.0 compilers
  • OpenMP 4.5 and subset of OpenMP 5.0 in C/C++/Fortran compiler classic 2021.1
  • OpenMP 4.5 and subset of OpenMP 5.1 supported in oneAPI SYCL/C++/Fortran compiler 2022.3 under -fiopenmp -fopenmp-targets=spir64

Compile with -Qopenmp on Windows, or just -qopenmp or –fiopenmp on Linux or Mac OSX. Compile with -fiopenmp -fopenmp-targets=spir64 on Windows and Linux for offloading support. Read More

Clang – C/C++

Clang is an open-source (permissively licensed) C/C++ compiler that is available to download gratis at https://releases.llvm.org/download.html.

Support for all non-offloading features of OpenMP 4.5 has been available since Clang 3.9. Support for offload constructs that run on the host is available since Clang 7.0. Support for offloading to GPU devices is available with some limitations since Clang 8.0. Support for OpenMP 5.0 is almost complete. Some OpenMP 5.1 features are available as well, se https://clang.llvm.org/docs/OpenMPSupport.html.

Clang defaults to OpenMP 5.0 semantics since release 11.0. Read More

Frontend independent LLVM/OpenMP documentation, including FAQ and information about optimizations as well as runtime configurations, can be found at https://openmp.llvm.org.

Flang – Fortran

Flang is the Fortran frontend of the LLVM compiler infrastructure. The OpenMP support in Flang is a work in progress. Flang supports parsing of all OpenMP 4.5 constructs and a few OpenMP 5.0 constructs/clauses. Semantic checks of OpenMP 4.5 and 5.0 constructs are mostly complete. Code generation of OpenMP 1.1 constructs is complete except for some instances of reduction, privatisation, atomic clauses and occurrence of unstructured code in OpenMP regions. Read More.

MSVC – C/C++

The Microsoft Visual C/C++ compiler supports the OpenMP 2.0 standard with the -openmp switch.

Experimental support for more recent versions of the standard can be enabled by using the -openmp:llvm switch instead of the -openmp switch. As of Visual Studio 2022 version 17.2, this includes all of the OpenMP 2.5 standard, as well as tasks and for loops with unsigned integer indices from the OpenMP 3.1 standard. Read More

Support for the SIMD directives from the OpenMP 4.0 standard is enabled with the -openmp:experimental switch. Read More

Nagfor – Fortran

NAG Fortran Compiler 7.1 supports OpenMP 3.1 on x86 and x64, for Linux, Mac and Windows. Compile with –openmp. Read More


NVIDIA HPC Compilers support a subset of OpenMP 5.1 in Fortran/C/C++ on Linux/x86_64, Linux/OpenPOWER, Linux/AAch64, and NVIDIA GPUs. Full support for OpenMP 3.1 in Fortran/C/C++ on Linux/x86_64, Linux/OpenPOWER, and Linux/AArch64. Compile with -mp to enable OpenMP for multicore CPUs on all platforms. Compile with -mp=gpu to enable target offload to NVIDIA GPUs. Read More


The OpenUH 3.x compiler has a full open-source implementation of OpenMP 2.5 and near-complete support for OpenMP 3.0 (including explicit task constructs) on Linux 32-bit or 64-bit platforms.  Read More & Download


Oracle Developer Studio 12.6 compilers (C, C++, and Fortran) support OpenMP 4.0 features. Compile with -xopenmp to enable OpenMP in the compiler. For this to work use at least optimization level -xO3, or the recommended -fast option to generate the most efficient code. To debug the code, compile without optimization option, add -g and use -xopenmp=noopt. Use the -xvpara option for static correctness checking and the -xloopinfo option for loop level messages. The latter is less comprehensive than the preferred er_src tool to get more detailed information on compiler optimizations. Add the -g option to the compile options to enable this and execute the command “er_src file.o” to extract the information. Read More


Refer to NVidia HPC Compilers. Read More

Sourcery CodeBench Lite – C/C++/Fortran

Sourcery CodeBench(AMD GPU) Lite for x86_64 GNU/Linux is Siemens’ free GCC-based compiler, supporting OpenMP with offloading to AMD Radeon (Graphics Core Next, GCN, and Instinct, CDNA) GPUs. Sourcery CodeBench 2022.09, released November 14, 2022, has full support for OpenMP 4.5 for C/C++/Fortran and has partial support for OpenMP 5.0, 5.1, and 5.2; it supports offloading to gfx803 Fiji (GCN3), gfx900 Vega 10 and gfx906 Vega 20 (GCN5) GPUs – and to the CDNA1 Instinct MI100 series devices (gfx908) and CDNA2 Instinct MI200 series devices (gfx90a). The compiler is based on GCC’s devel/omp/gcc-12 (og12) branch and supports all GCC 12 features, enriched by OpenMP features from GCC’s development branch and AMD GCN improvements such as support for offloading debugging. Read More & Free Download

Pyjama research compiler – Java

Pyjama is a research compiler for OpenMP directives in Java developed by the Parallel and Reconfigurable Computing lab, University of Auckland. It supports most of the OpenMP Version 2.5 specification, corresponding to the Common Core. Beyond this, it supports advanced features, including GUI-aware directives and concepts and directives for OpenMP asynchronous event-driven programming as well as Java specific features like strong Exception handling, loops over iterators etc. It is based on a source-to-source compiler and a runtime library, both published Open Source.  The Pyjama website provides Pyjama, examples, documentation and more. The source code is hosted at: https://github.com/ParallelAndReconfigurableComputing/Pyjama.


Vendor / Source



Codee – C/C++/Fortran

Codee (formerly known as Appentra Parallelware Analyzer) is a software development tool that provides automated code inspection specifically designed to improve the performance of C/C++/Fortran applications. It provides a systematic predictable approach to optimize C/C++/Fortran source code for the target environment. The tool scans the C/C++/Fortran source code without executing it, and produces a report for the developer indicating the issue, its location, why it penalizes performance, and how to fix it in order to make the code run faster. It is designed to interoperate with CI/CD systems.

Codee provides a performance optimization report with human-readable actionable items: opportunities, recommendations, defects and remarks. It annotates CPU and GPU codes with OpenMP, OpenACC and compiler-specific directives. It also detects defects in these directives, enabling the early detection of race conditions and data movement issues.

Read More

Forge  (includes DDT, Map and Performance Reports) – C, C++, Fortran, Python

Arm Forge is a software development toolkit designed to assist Linux developers write correct, scalable and performance applications for a variety of hardware architectures, including Arm (aarch64), x86_64, and NVIDIA GPUs. Beta support is now available for AMD GPU. Forge includes three components: DDT, MAP and Performance Reports and can be used for serial or parallel applications relying on MPI and/or OpenMP.

Arm DDT is a powerful, easy-to-use graphical debugger. It includes static analysis that highlights potential problems in the source code, integrated memory debugging that can catch reads and writes outside of array bounds, integration with MPI message queues and much more. It provides a complete solution for finding and fixing problems whether on a single thread or thousands of threads. Debug with Arm DDT

Arm MAP is a parallel profiler that shows you which lines of code took the most time and why. It supports both interactive and batch modes for gathering profile data, and supports MPI, OpenMP and single-threaded programs. Syntax-highlighted source code with performance annotations, enable you to drill down to the performance of a single line, and has a rich set of zero-configuration metrics, showing memory usage, floating-point calculations and MPI usage across processes. Profile with Arm MAP

Arm Performance Reports is a lightweight performance analysis tool that generates easy to read reports on an application. The tool processes data from a wide range of sources (including CPU, memory, IO or even energy sensors) and provides actionable feedback to help end-users improve the efficiency of their applications. Analyze with Performance Reports

Extrae – C, C++. Fortran, Java, Python

Extrae is an instrumentation package that collects performance data and saves it in Paraver trace format. It supports the instrumentation of MPI, OpenMP, OmpSs, pthreads, CUDA/CUPTI, OpenACC, OpenCL and GASPI programming models, with programs written in C, C++, Fortran, Java and Python, as well as combinations of different languages, hybrid and modular codes. In addition to the activity of the parallel runtime, Extrae is able to capture I/O activity, memory operations, hardware counter metrics, including uncore and network counters, as well as references to the source code. It is available for most UNIX-based operating systems and has been deployed in all relevant HPC architectures and platforms, including x86-64, ARM, ARM64, POWER, RISC-V, SPARC64, BlueGene, Cray and NVIDIA GPUs. With respect to OpenMP, it recognizes the main runtime calls for Intel, LLVM and GNU compilers, and also supports the latest standard of the OMPT interface, allowing instrumentation at loading time with the production binary. A new tracing mode that summarizes information at the level of long parallel regions, as well as improved support for OpenMP nested parallelism is currently being developed. Read More

Paraver – C, C++. Fortran, Java, Python

Paraver is a performance analyzer based on traces with a great flexibility to explore the collected data. It was developed to respond to the need to have a qualitative global perception of the application behavior by visual inspection and then to be able to focus on the detailed quantitative analysis of the problems. The tool can be considered a data browser that can explore any information expressed on its trace format. Extrae is the main provider of Paraver traces, although the trace format is public and has been used to collect information of system behavior, power metrics and user customized metrics. Read More



The gprofng application profiling tool is an open source project that is part of the GNU binutils tool set (https://sourceware.org/binutils). It supports profiling of a C, C++, or Java application and fully supports multi-threaded applications using OpenMP, Pthreads, or Java Threads.

There is no need to recompile the code. With gprofng, one can profile the same executable that is used in the production runs.

Another distinguishing feature is the ability to compare profiles. Multiple profiles can be loaded and in addition to the absolute timings, one can also look at ratios and deltas. This makes it very easy to identify regressions in a parallel application, including those parallelized with OpenMP.

Code Parallelization Assistant (Reveal) – C, C++, Fortran

HPE’s Code Parallelization Assistant, which is part of the HPE Cray Programming Environment, combines runtime performance statistics and program source code visualization with Cray Compiling Environment (CCE) compile-time optimization feedback to identify and exploit parallelism. This tool provides the ability to easily navigate through source code to highlight dependencies or bottlenecks during the optimization phase of program development or porting.

Using the program library provided by CCE and the performance data collected by HPE’s Performance Analysis Tool, the user can navigate through his or her source code to understand which high-level loops could benefit from OpenMP parallelism from loop-level optimizations such as exposing vector parallelism. It provides dependency and variable scoping information for those loops and assists the user with creating parallel directives. Read More

Performance Analysis Tool (CrayPat, Apprentice2) – C/C++/Fortran

HPE’s Performance Analysis Tools suite, which is part of the HPE Cray Programming Environment, provides an integrated infrastructure for measurement, analysis, and visualization of computation, communication, I/O, and memory utilization to help users optimize programs for faster execution and more efficient computing resource usage. With both simple and advanced interfaces, HPE’s Performance Analysis Tools allow the user to easily extract performance information from applications and use the tools’ wealth of capability to profile large, complex codes at scale.

The toolset allows developers to perform sampling and tracing experiments on executables, collecting information at the whole program, function, loop, and line level. Programs that use MPI, SHMEM, OpenMP (including target offload), CUDA, HIP, or a combination of these programming models are supported. Profiling applications built with CCE, Intel, Arm Allinea, AMD, and GNU compilers are supported. Read More

VTune Profiler – SYCL, C, C++, C#, Fortran, Python, Go, Java, OpenCL

Intel® VTune™ Profiler is a low-overhead and high resolution performance profiling and analysis tool which helps to find and fix performance bottlenecks quickly and realize all the value of your hardware. It is capable of collecting performance statistics on CPUs and GPUs for applications written in various languages including SYCL, C, C++, Fortran, Python or any combination of languages and using OpenMP and MPI. Intel® VTune™ Profiler includes various analysis types such as Hotspots, Threading, HPC Performance Characterization, Memory Consumption, Memory Access and Microarchitecture Exploration analysis. The expanded Accelerator Profiling enables to identify issues that prevent GPU code from efficiently using available execution unit threads by using the GPU Compute/Media hotspots analysis and analyze data transfer to pinpoint inefficient code paths between host and device in the GPU Offload Analysis. Intel® VTune™ Profiler also supports efficient workflows to analyze application performance characterization with pointers to deeper analysis using Performance Snapshot as well as gain insight into system configuration, performance, and behavior with Platform Profiler. Furthermore, the Application Performance Snapshot capability enables analyzing workloads at scale to identify outliers and where they occurred. Read More

Advisor – SYCL, C, C++, Fortran

Intel® Advisor is a design and analysis tool to help ensure your applications realize full performance potential on modern Intel processors. The tool supports C, C++, Fortran, SYCL, OpenMP, OpenCL™ code, and Python. It helps design performant applications on CPU for efficient threading, vectorization, and memory use. The expanded Offload Modeling capability helps to ensure efficient GPU offload: identify parts of the code that can be profitably offloaded and optimize the code for compute and memory. The Flow Graph Analyzer enables the visualization and analysis of OpenMP task dependence graphs for performance bottlenecks. Additionally, with cache-aware Roofline Analysis, visualization of actual performance against hardware-imposed performance ceilings (rooflines), such as memory bandwidth and compute capacity help you identify effective optimization strategies. Intel® Advisor supports viewing the results of roofline profiling and offload modeling in a browser with an interactive HTML report. Read More

Inspector – C, C++, Fortran

Find errors early when they are less expensive to fix. Intel® Inspector is an easy-to-use memory and threading error debugger for C, C++, and Fortran applications that run on Windows* and Linux*. Memory and threading errors analysis are enabled for SYCL and OpenMP offloaded codes that are run on a CPU target. No special compilers or builds are required. Just use a normal debug or production build. Use the graphical user interface or automate regression testing with the command line. It has a stand-alone user interface on Windows and Linux or it can be integrated with Microsoft Visual Studio. Read More

Trace Analyzer & Collector – C, C++, Fortran

Intel Trace Collector is a low-overhead tracing library that performs event-based tracing in applications at runtime. It collects data about the application MPI and serial or OpenMP* regions, and can trace custom set functions.  Intel Trace Analyzer is a GUI-based tool that provides a convenient way to monitor application activities gathered by the Intel Trace Collector. The tools can help you evaluate profiling statistics and load balancing, analyze performance of subroutines or code blocks, learn about communication patterns, parameters, performance data, check MPI correctness and identify communication hotspots.  Read More

The Scalasca Trace Tools are a collection of trace-based performance analysis tools that have been specifically designed for use on large-scale systems. A distinctive feature is the scalable automatic trace-analysis component which provides the ability to identify wait states that occur, e.g., as a result of unevenly distributed workloads. Besides merely identifying wait states, the trace analyzer is also able to pinpoint their root causes and to identify the activities on the critical path of the target application, highlighting those routines which determine the length of the program execution and therefore constitute the best candidates for optimization. The Scalasca Trace Tools process traces generated by the Score-P measurement infrastructure and produce reports that can be explored with Cube or TAU ParaProf/PerfExplorer.  Read More


NVIDIA Nsight Systems

Nsight Systems for Linux is capable of capturing information about OpenMP events. This functionality is built on the OpenMP Tools Interface (OMPT), full support is available only for runtime libraries supporting tools interface defined in OpenMP 5.0 or greater.

See the documentation for details and platform support.

ParaFormance is a software tool-chain that allows software developers to quickly and easily write multi-core software. ParaFormance enables software developers to find the sources of parallelism within their code, automatically (through user-controlled guidance) inserting the parallel business logic (using OpenMP and TBB), and checking that the parallelised code is thread-safe. Read More

Perforce Software

TotalView for HPC – C/C++/Fortran/Python

The TotalView for HPC debugger is designed to handle debugging of thousands of threads and processes at a time with an easy-to-use, modern user interface. TotalView allows you to get complete control over program execution: running, stepping, and halting line-by-line through code within a single thread or arbitrary groups of processes or threads. Resolve bugs faster by working backward from failure using reverse debugging. Track down and solve difficult problems in concurrent programs that use threads, OpenMP (including support for OpenMP 5.x), and MPI. Easily debug CUDA code running on NVIDIA GPUs and HIP code running on AMD GPUs. Find memory leaks, buffer overruns, and other memory problems with just a click of a button using TotalView’s memory debugging. Debug Python and C/C++ code with TotalView’s mixed language debugging capabilities. Quickly learn how to use TotalView with video tutorials and help documentation. Read More

HPCToolkit is an integrated suite of tools for measurement and analysis of program performance. HPCToolkit collects profiles and traces of CPU and GPU-accelerated programs with low measurement overhead on computers ranging from multicore desktop systems to the nation’s largest supercomputers. HPCToolkit supports measurement on Arm, AMD, Intel, and IBM processors as well as AMD, Intel, and NVIDIA GPUs. HPCToolkit can measure applications developed with one or more parallel programming models including MPI, OpenMP, OpenACC, RAJA, Kokkos, and DPC++. If an OpenMP runtime implements the OpenMP Standard’s OMPT interface for tools on CPUs and/or GPUs, HPCToolkit will use it to reconstruct and attribute costs to user-level calling contexts instead of implementation-level calling contexts. Read More

The Score-P measurement infrastructure is an extremely scalable and easy-to-use tool suite for call-path profiling and event tracing of applications written in C, C++, or Fortran. It supports a wide range of HPC platforms and programming models; besides OpenMP, Score-P can hook into other common models, including MPI, SHMEM, Pthreads, CUDA, HIP, OpenCL, OpenACC, Kokkos, and their valid combinations. Score-P is capable of gathering performance information through automatic instrumentation of functions, library interception/wrapping, source-to-source instrumentation, event- and interrupt-based sampling, and hardware performance counters. Score-P measurements are the primary input for a range of specialized analysis tools, such as: Cube, Vampir, Scalasca Trace Tools, TAU, or Extra-P. Read More

Vampir is an easy-to-use framework for performance analysis, which enables developers to quickly study program behavior at a fine-grained level of detail. Performance data obtained from a parallel program run can be analyzed with a collection of specialized performance views. Intuitive navigation and zooming are the key features of the tool, which help to quickly identify inefficient or faulty parts of a program code. Vampir allows analysis of load imbalances in OpenMP programs, visualizes the interplay of parallel APIs, such as MPI and OpenMP, and supports hardware performance counters to evaluate OpenMP code regions. Score-P is the primary code instrumentation and run-time measurement framework for Vampir. Read More

TAU – C, C++, Fortran, Java, Python, Spark

C, C++, Fortran, Java, Python, and Spark. For instrumentation of OpenMP programs, TAU includes source-level instrumentation (Opari), a runtime “collector” API (called ORA) built into an OpenMP compiler (OpenUH), and an OpenMP runtime library supporting OMPT from the OpenMP 5.2 standard, including asynchronous target offload events prototyped by the AMD compiler. View technical paper. TAU supports both direct probe based measurements as well as event-based sampling modes for profiling. For tracing, TAU provides an open-source trace visualizer (Jumpshot) and can generate native OTF2 trace files that may be visualized in the Vampir trace visualizer. TAU Commander simplifies the TAU workflow and installation. TAU supports both PAPI and LIKWID toolkits to access low-level processor specific hardware performance counter data to correlate it to the OpenMP code regions. TAU ships with a BSD style license. Read More.

APEX – C/C++, Fortran

APEX is an introspection and runtime adaptation library for asynchronous multitasking runtime systems. However, APEX is not only useful for AMT/AMR runtimes – it can be used by any application wanting to perform runtime adaptation to deal with heterogeneous and/or variable environments. APEX provides an API for measuring actions within the OpenMP runtime using the OpenMP 5.2 OMPT interface – including support for target offload events prototyped by the AMD compiler. APEX can generate TAU profiles, CSV files, task graphs and trees, task scatterplots, OTF2 traces, or Google Trace Events traces. APEX also provides a policy engine for autotuning of OpenMP parameters such as thread count, scheduler or chunk size or for adaptation to a changing environment such as soft or hard power caps. Read More and additional information.