Unlocking High-Performance Parallel Apps with OpenMP Offload

Table of Contents:

1. Introduction
2. OpenMP: A Brief Overview
3. OpenMP Offload Capabilities in the OneAPI HPC Toolkit
4. Understanding OpenMP Offload Programming
   • 4.1 Basic Features of OpenMP for Accelerators
   • 4.2 Managing Data Movement in OpenMP Offload
   • 4.3 Expressing Parallelism in OpenMP Offload
5. Advancements in OpenMP and OneAPI
6. Getting Started with the OneAPI HPC Toolkit
   • 6.1 Setting Up the OneAPI Environment
   • 6.2 Compiling OpenMP Target Code
   • 6.3 Compiling with Data Parallel C++
7. Optimizing Parallel Applications with OpenMP Offload
   • 7.1 Mapping Parallel Algorithms to GPUs
   • 7.2 Controlling Data Movement in OpenMP Offload
   • 7.3 Using Unified Shared Memory in OpenMP
   • 7.4 Prefetching Data in USM with OpenMP
   • 7.5 Combining OpenMP and DPCT for Optimal Performance
8. Writing High-Performance Parallel Applications with OpenMP Offload
9. Conclusion

Introduction

Welcome to the Early Adopter Series for June 2020! In this series, we aim to foster discussions between software and hardware developers and early users of new technologies. Today, our topic is OpenMP offload capabilities in the OneAPI HPC Toolkit. We are pleased to have Chun Min King, a principal engineer in the HPC organization of the Core Enterprise Solution Group at Intel, joining us to talk about the implementation of the latest OpenMP standards in the OneAPI HPC Toolkit.

OpenMP: A Brief Overview

OpenMP is an industry standard for multi-platform shared-memory multiprocessor programming in C, C++, and Fortran. It allows developers to write portable and scalable parallel programs through compiler directives, library routines, and environmental variables. OpenMP can run on various platforms, from laptops to large-Scale clusters. The mixed or hybrid programming model of MPI and OpenMP has been the foundation of many parallel applications today.

OpenMP Offload Capabilities in the OneAPI HPC Toolkit

The OneAPI HPC Toolkit is specifically designed for Intel platforms, from CPUs to GPUs. It encompasses a range of tools, including OpenMP compilers and runtime, to empower developers in writing high-performance parallel applications. In this toolkit, OpenMP plays a critical role in enabling offloading computations onto GPUs and other accelerators, driving advancements in the HPC ecosystem.

Understanding OpenMP Offload Programming

OpenMP offload programming allows developers to express parallelism and manage data movement efficiently. With directives and tools provided by OpenMP, applications can be developed to fully utilize the capabilities of GPUs for massively parallel computing. The offload programming model in OpenMP differs from other models, such as DPC++ and CUDA, as it allows developers to focus on expressing parallelism and leveraging the shared memory model without having to explicitly program from the bottom up.

Basic Features of OpenMP for Accelerators

OpenMP provides a set of directives and constructs to instruct the compiler and runtime to offload code blocks to the device. These directives not only specify the data locations and movement but also express the parallelism in the applications, allowing the runtime to execute code blocks in parallel using multiple compute engines. This mechanism enables developers to achieve great acceleration compared to sequential execution.

Managing Data Movement in OpenMP Offload

One key aspect of OpenMP offload programming is managing the movement of data between the host and device. GPUs have their own dedicated memory space, and data created on the CPU needs to be explicitly moved to the GPU for computation. OpenMP provides the map clause to control the allocation of data on the device and specify whether data needs to be copied back to the CPU. Proper data allocation and movement are crucial for optimizing performance on GPUs.

Expressing Parallelism in OpenMP Offload

To fully utilize the parallelism offered by GPUs, developers must express the parallel algorithm in their code effectively. OpenMP provides constructs like parallel for and nested parallelism to express different levels of parallelism and maximize GPU utilization. By leveraging the hierarchical memory and compute engine structures of GPUs, developers can achieve high levels of parallelism and significant acceleration in their applications.

Advancements in OpenMP and OneAPI

OpenMP is constantly evolving to support new hardware and programming paradigms. The latest standard, OpenMP 4.5, provides features to support multi-core processors and accelerators like GPUs. Work is underway to implement OpenMP 5.0, which will introduce additional features to improve the performance and ease of use of OpenMP programming. The OneAPI HPC Toolkit will continue to incorporate these advancements, providing developers with the latest tools and capabilities for parallel programming.

Getting Started with the OneAPI HPC Toolkit

To get started with the OneAPI HPC Toolkit, developers can download the latest version release and follow the installation instructions. Once the toolkit is set up, the environment can be configured using provided scripts. The toolkit includes OpenMP compilers, runtime, and related libraries, enabling developers to compile and run their C, C++, and Fortran applications on a variety of Intel platforms.

Setting Up the OneAPI Environment

Setting up the OneAPI environment involves sourcing the appropriate scripts provided by the toolkit. These scripts will configure the necessary paths and environment variables for using the OneAPI compilers and runtime. The specific details may vary depending on the chosen environment and shell, but the general process remains the same.

Compiling OpenMP Target Code

Compiling an application that uses OpenMP for offloading to accelerators requires including the proper header files and adding the necessary linking options. The OneAPI environment provides the necessary include directories and link flags. Developers can use the OneAPI compilers (icpc, icpc++, ifort) and specify the target option to indicate that the code should be compiled for offload execution on devices.

Compiling with Data Parallel C++

Data Parallel C++ (DPC++) is another programming model that developers can use alongside OpenMP in the OneAPI HPC Toolkit. DPC++ allows developers to write high-level, data-parallel applications for heterogeneous systems, utilizing GPUs and other accelerators. Compiling with DPC++ involves including the oneapi header file and using the sycl::queue class to manage device execution.

Optimizing Parallel Applications with OpenMP Offload

Optimizing parallel applications that use OpenMP offload involves effectively mapping parallel algorithms to GPUs, controlling data movement, and ensuring efficient execution. OpenMP provides mechanisms like the target construct and the map clause to facilitate data offloading to devices and specify the necessary data management. Hierarchical parallelism and data parallelism can be combined to maximize GPU utilization and achieve high-performance execution.

Mapping Parallel Algorithms to GPUs

By utilizing the parallelism available in their applications, developers can effectively map algorithms to GPUs for acceleration. OpenMP provides constructs like parallel loops (omp parallel for) and nested parallelism to express different levels of parallelism. By breaking down computations into smaller tasks and distributing them among Threads and teams on GPUs, developers can fully utilize the massive parallelism available.

Controlling Data Movement in OpenMP Offload

Managing data movement between the host and device is crucial for optimizing performance in OpenMP offload programming. OpenMP provides data mapping options through the map clause, allowing developers to control where data resides and how it is moved. By understanding the memory hierarchy and characteristics of the target architecture, developers can minimize data transfers and maximize utilization of the GPU's computational power.

Using Unified Shared Memory in OpenMP

Unified Shared Memory (USM) is a powerful feature in OpenMP that simplifies data management by unifying the memory spaces between the host and device. With USM, developers can allocate and access data seamlessly on both the host and device without the need for explicit data transfers. OpenMP offload programs can take advantage of USM to reduce communication overhead and optimize performance.

Prefetching Data in USM with OpenMP

OpenMP with USM also supports prefetching data, allowing developers to hint at data access Patterns to optimize performance further. Prefetching can be especially useful for improving cache utilization and reducing memory latency. By prefetching data into the cache or other intermediate storage, applications can minimize stalls and keep the GPU's compute units busy, resulting in improved performance.

Combining OpenMP and DPCT for Optimal Performance

Developers can combine the features and capabilities of OpenMP and Data Parallel C++ (DPCT) in the OneAPI HPC Toolkit to achieve optimal performance. DPCT is another programming model that leverages the power of GPUs and accelerators. By utilizing both models, developers can write highly efficient, parallel applications that make use of the extensive parallelism available in heterogeneous systems.

Writing High-Performance Parallel Applications with OpenMP Offload

To write high-performance parallel applications with OpenMP offload, developers should consider the hardware architecture, parallel algorithm design, and data management. By carefully utilizing parallelism, controlling data movement, and optimizing for the target architecture, developers can harness the full power of GPUs and other accelerators. OpenMP provides the necessary tools and constructs to express parallelism and manage data, enabling developers to write efficient and scalable parallel applications.

Conclusion

The OneAPI HPC Toolkit, incorporating OpenMP offload capabilities, provides developers with a powerful set of tools for writing high-performance parallel applications. By leveraging OpenMP's directives and features, developers can express parallelism, control data movement, and optimize for GPUs and accelerators. The combination of OpenMP and other programming models in the toolkit allows for flexible and efficient application development. Explore the features and capabilities of the OneAPI HPC Toolkit to unlock the full potential of your parallel applications.

Thank you for your attention.