Introduction to OpenMP Offloading

OpenMP began with OpenMP 1.0 in 1997, initially supporting FORTRAN and later adding support for C/C++ as the demand for multi-language support grew. Over the years, OpenMP has seen numerous updates, incorporating advanced features for emerging hardware, including SIMD parallelism and task-based parallelism. The most notable enhancement for accelerators came with OpenMP 4.0, introducing device support to offload computation to accelerators like GPUs and FPGAs. This support has continued to evolve, with GPUs now being the primary focus due to their wide adoption and extensive optimization support in the OpenMP community.

OpenMP Offloading to Devices

OpenMP offloading allows specific parts of a program, such as loops or compute-intensive sections, to be transferred (or “offloaded”) to a device like a GPU for execution. This device-based execution model is achieved through OpenMP’s target directives, which specify regions of code for offloading. These target directives enable OpenMP to handle data transfer, memory management, and execution on the device, reducing the complexity of parallel programming across heterogeneous architectures.

• Device Memory Management: OpenMP’s device constructs automatically handle memory allocation on the device. However, developers can use explicit data clauses like map to control data movement between the host and device, ensuring efficient data handling and avoiding unnecessary transfers.
• Offloading Directives: Using directives like #pragma omp target, OpenMP transfers code execution to a device while allowing fine-grained control over memory and data allocation through additional clauses.

Benefits and Challenges of OpenMP Offloading

Benefits:

• Performance Scaling: OpenMP offloading enables parallel workloads to scale by leveraging device compute resources, especially GPUs, with their large core counts and high throughput.
• Ease of Use: OpenMP’s offloading capabilities simplify complex device programming, allowing developers to focus on high-level parallelization while OpenMP manages data and device synchronization.

Challenges:

• Data Transfer Overhead: Moving data between the host and device can introduce latency, particularly for large datasets.
• Resource Utilization: Ensuring efficient usage of device resources and avoiding memory contention or insufficient parallelism require a deep understanding of the device architecture.

Compilers and Tools for OpenMP Offloading

This table provides an overview of popular compilers and tools used for OpenMP Offloading, focusing on support for languages like C, C++, and Fortran. The table includes options for targeting NVIDIA GPUs, AMD GPUs, and other accelerators using different compilers and debugging or profiling tools.

Table 1: Compilers and tools for OpenMP Offloading

Various Compiler Notes:

• Clang/LLVM: Provides robust support for OpenMP Offloading targeting NVIDIA and AMD GPUs. For AMD GPUs, -fopenmp-targets=amdgcn-amd-amdhsa along with the -march flag (e.g., -march=gfx906 for specific GPU architectures) can be used.
• GCC: Supports OpenMP Offloading with targets like NVIDIA and AMD. NVIDIA GPUs are specified with -fopenmp-targets=nvptx-none and AMD GPUs with -fopenmp-targets=amdgcn-amd-amdhsa.
• NVIDIA HPC SDK (NVC): NVIDIA's compilers (nvc, nvfortran) fully support OpenMP Offloading for NVIDIA GPUs.
• Intel oneAPI: Provides offloading support for Intel GPUs and other SPIR-compatible devices, using -fopenmp-targets=spir64.
• ARM Compiler: Supports OpenMP Offloading for ARM-based CPUs and GPUs.
• Cray Compiler: Cray supports OpenMP Offloading for both NVIDIA and AMD GPUs. Use -h omp for OpenMP and -h omp_offload along with -h target=gpu to target GPUs, including AMD GPUs in Cray's Shasta and EX systems.
• AMD AOCC: AMD's AOCC compiler suite includes an OpenMP-enabled variant of Clang, which can target AMD GPUs using -fopenmp-targets=amdgcn-amd-amdhsa.
• AMD ROCm (hipcc): HIPCC is the compiler driver in ROCm and supports OpenMP Offloading for AMD GPUs. Use -fopenmp along with target specifications like -march=gfx906 to target specific AMD architectures.

This table lists options for offloading parallel workloads to accelerators, enabling efficient usage of GPUs and other hardware accelerators across various architectures. Each compiler and tool provides options for device selection, debugging, and profiling, making OpenMP Offloading a versatile choice for heterogeneous HPC applications.

Basic OpenMP Offloading Syntax

To offload computations using OpenMP, you apply the target directive, which specifies regions of code to run on an accelerator. The basic syntax for OpenMP Offloading in C/C++ and Fortran is shown below:

// C/C++
#include <omp.h>
#pragma omp target [clauses]
{
    #pragma omp parallel for
    // Code to be offloaded
}

! Fortran
use omp_lib
!$omp target [clauses]
  !$omp parallel do
  ! Code to be offloaded
!$omp end target

Explanation of Key Elements

• target directive: Specifies that the enclosed code block should be offloaded to the target device (e.g., GPU).
• parallel directive: Initiates parallel execution on the device, dividing work among threads.
• Clauses: Optional arguments that provide additional control over the behavior of the offloaded code, data handling, and execution flow.

Directives and Clauses in OpenMP Offloading

OpenMP Offloading supports multiple directives and clauses, enabling detailed control over parallel regions and memory management. These directives and clauses can be categorized into three main types:

1. Compute Directives:

   • target: Offloads the specified region of code to a device.
   • teams: Divides parallel work among teams of threads, especially useful on GPUs where each team can correspond to a block in CUDA.
   • distribute: Distributes loop iterations across the teams on the device.
   • parallel and for/do: Divide work across threads within a team.

2. Data Management Directives:

   • map: Controls data movement between the host and the device, using clauses like map(to:), map(from:), and map(tofrom:) to specify data direction.
   • declare target: Marks functions or variables that should be accessible on the target device.
   • allocate: Specifies memory allocation on the device.

3. Synchronization Directives:

   • barrier: Synchronizes all threads in a team.
   • taskwait: Waits for all tasks in the current parallel region to complete.
   • nowait: Used within target or teams to allow asynchronous execution without waiting for completion.

Data Mapping Clauses

The map clause in OpenMP Offloading is crucial for managing data movement between the host and device. Below are the most commonly used map clauses:

• map(to: array[:N]): Transfers the array array to the device.
• map(from: array[:N]): Brings the array array back from the device to the host after execution.
• map(tofrom: array[:N]): Ensures the array array is updated both on the device and the host.

Compilation Options for OpenMP Offloading

The following table provides an overview of compilation flags for various OpenMP Offloading compilers, along with additional flags required to target specific hardware, including NVIDIA GPUs, AMD GPUs, and specialized architectures from ARM and Cray.

Table 2: Compilation flags for OpenMP Offloading on NVIDIA, AMD, and other architectures

Compilation Flag Notes:

• Clang/LLVM: Clang supports both NVIDIA and AMD GPUs through the -fopenmp-targets flag. For NVIDIA, use nvptx64-nvidia-cuda, and for AMD, use amdgcn-amd-amdhsa. The -march flag specifies the GPU architecture (e.g., gfx906 for AMD's Vega20 GPUs).
• GCC: GCC supports NVIDIA GPUs (-foffload=nvptx-none) and AMD GPUs with amdgcn-amd-amdhsa. Use -march=gfx906 for AMD architectures.
• NVIDIA (NVC): The -mp flag enables OpenMP Offloading for NVIDIA GPUs. The -gpu=target_architecture flag specifies the GPU architecture (e.g., cc70 for compute capability 7.0).
• Intel oneAPI: Targets Intel GPUs and other SPIR-compatible devices using -fopenmp-targets=spir64.
• ARM Compiler: Supports ARM-based accelerators with -fopenmp-targets=arm-sve. The -march flag specifies the ARM architecture version.
• Cray: Cray compilers support OpenMP Offloading with -h omp and GPU targeting with -h omp_offload -h target=gpu. The -h omplace=1 flag optimizes thread placement for performance.
• AMD Compiler: AMD compilers provide OpenMP Offloading support for AMD GPUs. Use -march=gfx906 or -march=gfx908 depending on the specific AMD GPU architecture.

Each compiler provides specific options for targeting various GPU and CPU architectures, enabling flexibility and optimized performance across heterogeneous HPC systems.

Key Directives and Clauses

• Compute Directives: target, teams, distribute, parallel, for, which structure work across teams and threads.
• Data Management: map clauses control data transfer, while declare target and allocate manage memory allocation on devices.
• Synchronization: barrier, taskwait, and nowait manage parallel execution flow.

OpenMP Offloading makes it possible to scale parallel code across CPUs, GPUs, and other accelerators without sacrificing portability, providing a versatile, high-level programming model for HPC applications.

Summary

OpenMP Offloading provides a straightforward way to extend parallel programming to GPUs and other accelerators using familiar OpenMP directives. This model offers key benefits for scientists and engineers who want to accelerate their applications on heterogeneous systems, as it minimizes the need for deep GPU programming knowledge.