Improving Application Performance with Heterogeneous Computing in HarmonyOS Next

This article aims to deeply explore the heterogeneous computing technology in the Huawei HarmonyOS Next system (up to API 12 as of now), and summarize it based on actual development practices. It mainly serves as a vehicle for technical sharing and communication. There may be mistakes and omissions. Colleagues are welcome to put forward valuable opinions and questions so that we can make progress together. This article is original content, and any form of reprint must indicate the source and the original author.

I. Overview and Principles of Heterogeneous Computing

(1) Introduction to Concepts and Principles

In the computing world of HarmonyOS Next, heterogeneous computing is like a symphony orchestra. Different hardware resources, such as CPUs and NPUs, are like different musical instruments in the orchestra, each playing a unique advantage and working together to improve overall computing efficiency. The core principle of heterogeneous computing is to allocate different computing tasks to the most suitable hardware resources according to their characteristics.

For example, as a general - purpose processor, the CPU is good at handling logical control and complex computational tasks, but its efficiency is relatively low when dealing with large - scale parallel computing. The NPU (Neural - Network Processing Unit), on the other hand, is specifically optimized for neural - network computing and can process highly parallel tasks such as matrix operations in deep - learning models with extremely high efficiency. In a heterogeneous computing system, when encountering an application scenario that includes logical judgment and deep - learning inference, the system will intelligently allocate the logical - judgment part to the CPU for processing and the deep - learning inference part to the NPU, thus giving full play to the advantages of both and improving the overall computing speed of the system.

(2) Ways to Improve Efficiency by Utilizing Hardware Resources (Assisted by an Architecture Diagram)

[Insert a simple heterogeneous computing architecture diagram here to show the interaction between hardware such as CPUs, NPUs, and applications]

As can be seen from the architecture diagram, applications interact with different hardware resources through HarmonyOS Next's heterogeneous computing framework (such as the HiAI Foundation Kit). When an application initiates a computing task, the heterogeneous computing framework will decompose and allocate the task to the appropriate hardware according to the type of the task and the status of the hardware resources. For example, in an image - processing application, tasks such as image decoding and pre - processing may be assigned to the CPU because these tasks involve more logical control and data - format conversion. While deep - learning - related tasks such as image feature extraction and recognition will be assigned to the NPU to take advantage of the NPU's efficient parallel - computing capabilities for accelerated processing. In this way, different hardware resources perform their respective duties, avoiding waste of resources and greatly improving computing efficiency.

(3) Advantages Compared with Traditional Computing Methods

1. In terms of Computing Efficiency Traditional computing methods usually rely on a single type of processor (such as a CPU) to handle all computing tasks. When facing complex application scenarios, especially tasks involving a large amount of parallel computing (such as deep learning and graphic rendering), the computing power of the CPU often becomes a bottleneck, resulting in slow computing speed. Heterogeneous computing, by reasonably allocating tasks to different hardware resources, can give full play to the advantages of various hardware and achieve parallel processing of computing tasks, significantly improving computing efficiency. For example, in an image - recognition application, using heterogeneous computing can increase the recognition speed by several times or even dozens of times, which has obvious advantages compared with traditional computing methods.
2. In terms of Resource Utilization Traditional computing methods may lead to some hardware resources being idle and others being over - used due to the inability to fully utilize the characteristics of the hardware, resulting in a waste of resources. Heterogeneous computing can dynamically allocate tasks according to the performance characteristics of the hardware and task requirements, enabling each hardware resource to be fully utilized. For example, the NPU can run efficiently when processing deep - learning tasks, while the CPU can play its role when handling other tasks (such as user - interface interaction, data - storage management, etc.). This improves the overall resource utilization of the system, reduces energy consumption, and extends the battery life of the device.

II. Implementation of Heterogeneous Computing in HarmonyOS Next

(1) Implementation Methods and Interface Explanation of HiAI Foundation Kit

The HiAI Foundation Kit provides developers with rich interfaces and functions to facilitate the implementation of heterogeneous computing in HarmonyOS Next applications. Key interfaces include task - creation interfaces, hardware - resource - specification interfaces, and task - execution interfaces.

Through the task - creation interface, developers can define a computing task, including the input and output data of the task, the computing logic, etc. For example, create an image - classification task, specifying the input as image data and the output as the classification result. Then, use the hardware - resource - specification interface to select the appropriate hardware resource to execute the task according to the characteristics of the task. For example, if it is a deep - learning task, the NPU can be specified for calculation. Finally, start the task through the task - execution interface. The heterogeneous computing framework will allocate the task to the specified hardware resource for execution according to the previous configuration and return the calculation result.

(2) Code Example and Explanation of Specifying Operations to be Executed on Different Hardware

The following is a simple code example showing how to use the HiAI Foundation Kit in a HarmonyOS Next application to implement heterogeneous computing and allocate a simple matrix - multiplication task to the CPU and NPU respectively (simplified version):

import { HiaiEngine } from '@kit.HiAIFoundationKit';

// Define the matrix - multiplication calculation function (assuming the matrix - multiplication algorithm has been implemented here)
function matrixMultiplication(a: number[][], b: number[][]) {
    // Omit the calculation logic. In reality, the matrix - multiplication algorithm needs to be implemented
    return result;
}

// Create a CPU engine instance
let cpuEngine = new HiaiEngine('CPU');
// Create an NPU engine instance
let npuEngine = new HiaiEngine('NPU');

// Define two matrices
let matrixA = [[1, 2, 3], [4, 5, 6]];
let matrixB = [[7, 8], [9, 10], [11, 12]];

// Execute the matrix - multiplication task on the CPU
let cpuResultPromise = cpuEngine.executeTask(() => {
    return matrixMultiplication(matrixA, matrixB);
});

// Execute the matrix - multiplication task on the NPU (assuming the NPU supports matrix - multiplication acceleration operations, this is just an example)
let npuResultPromise = npuEngine.executeTask(() => {
    return matrixMultiplication(matrixA, matrixB);
});

// Wait for the CPU task to complete and get the result
cpuResultPromise.then((result) => {
    console.log('CPU calculation result:', result);
});

// Wait for the NPU task to complete and get the result
npuResultPromise.then((result) => {
    console.log('NPU calculation result:', result);
});

In this example, first, engine instances for the CPU and NPU are created, representing the CPU and NPU hardware resources respectively. Then, two matrices are defined, and the matrix - multiplication task is executed on the CPU and NPU respectively. Through the executeTask method, the computing tasks are allocated to the corresponding hardware resources for execution. After the tasks are completed, the calculation results are obtained and printed through the then method. In practical applications, it is necessary to reasonably select and configure hardware resources according to the specific hardware support and task requirements, as well as optimize the allocation of computing tasks.

(3) Analysis of Performance Improvement Effects on Different Types of Applications

1. Image - Processing Applications In image - processing applications, heterogeneous computing can significantly improve performance. For example, in operations such as image filtering and edge detection, the CPU can be responsible for tasks such as image reading, format conversion, and some logical - control tasks, while the NPU can efficiently execute computationally intensive tasks such as convolution operations. Through this approach, the speed of image processing can be greatly increased. Taking image filtering as an example, after using heterogeneous computing, the time to process a high - definition image (such as 1080p) can be shortened from several hundred milliseconds to several tens of milliseconds, greatly improving the real - time performance of image processing. This enables the application to run more smoothly. For example, in a real - time video - filter application, users can see the filter effect in real - time, enhancing the user experience.
2. Machine - Learning Applications For machine - learning applications, especially the training and inference processes of deep - learning models, the advantages of heterogeneous computing are even more obvious. During the training phase, the NPU can accelerate a large number of matrix operations in the neural network, such as the calculations of convolutional layers and fully - connected layers, greatly shortening the training time. For example, a deep - learning model that originally takes several hours or even several days to complete training on the CPU can have its training time shortened to several hours or even less after being accelerated by the NPU. During the inference phase, heterogeneous computing can enable the model to process input data more quickly and improve the response speed. For example, in an intelligent voice - assistant application, using heterogeneous computing can make the voice - recognition and answer - generation speeds faster, and users can almost get a real - time response, improving the usability of the intelligent assistant and user satisfaction.

III. Application Cases and Future Development of Heterogeneous Computing

(1) Demonstration of Practical Application Cases

Take an intelligent driving assistance system based on HarmonyOS Next as an example. In this system, heterogeneous computing plays a crucial role.

During the vehicle's driving process, the camera continuously collects road - image data. First, tasks such as image data decoding and pre - processing (such as cropping, normalization, etc.) are handled by the CPU. These tasks involve more logical judgment and data - format conversion operations, which the CPU can complete quickly and efficiently. Then, for deep - learning tasks such as object detection (such as vehicles, pedestrians, traffic signs, etc.) and lane - line detection in the images, the system allocates them to the NPU for calculation. The NPU, using its powerful parallel - computing capabilities, can quickly and accurately identify various objects and lane - line information on the road.

Based on the detection results, the system needs to make real - time decisions, such as determining whether to remind the driver to pay attention to the vehicle in front or whether to adjust the vehicle speed. These decisions involve some logical judgment and simple calculations, which are completed by the CPU. For example, if the detected distance to the vehicle in front is too close, the CPU will judge whether to issue an alarm according to the preset safety - distance rules and feed the decision result back to the vehicle control system.

Through heterogeneous computing, this intelligent driving assistance system can process a large amount of image data in real - time, make quick and accurate decisions, and provide timely and effective assistance information to the driver, greatly improving driving safety and comfort. At the same time, because heterogeneous computing improves the system's computing efficiency and reduces energy consumption, the system can run stably under the limited resources of in - vehicle devices.

(2) Discussion on Future Development Trends

1. Adaptation to More Hardware With the continuous development of hardware technology, in the future, heterogeneous computing in HarmonyOS Next will be adapted to more types of hardware resources. In addition to the existing CPUs and NPUs, it may be integrated with emerging hardware technologies such as FPGAs and TPUs. For example, FPGAs (Field - Programmable Gate Arrays) have the characteristics of flexible programming and hardware acceleration and can be customized for acceleration according to specific application requirements. The heterogeneous computing framework can dynamically allocate some computing tasks to the FPGA for execution according to the characteristics of the application, further improving computing efficiency. At the same time, more extensive adaptation to hardware devices from different manufacturers will enable heterogeneous computing to play a role on more types of devices and expand the application scenarios of HarmonyOS Next.
2. Smarter Task Allocation In the future, heterogeneous computing will achieve a smarter task - allocation mechanism. Through the analysis of application behavior and real - time monitoring of hardware resources, the system can more accurately allocate computing tasks to the most suitable hardware resources. For example, according to factors such as the load of hardware resources, energy - consumption status, and task priorities, the task - allocation strategy is dynamically adjusted. When the NPU is highly loaded, some tasks with low real - time requirements are allocated to the CPU for execution. When the device's battery level is low, tasks are preferentially allocated to hardware resources with lower energy consumption to extend the device's battery life. At the same time, using machine - learning technology, the task - allocation strategy can be adaptively adjusted according to the application's usage habits and user needs, further optimizing system performance.

(3) Summary of Importance and Potential

Heterogeneous computing is of great importance and has huge potential in improving the performance of HarmonyOS Next applications. It breaks through the bottleneck of traditional computing methods, gives full play to the advantages of different hardware resources, and enables applications to achieve higher performance under limited resources. Whether it is in improving computing speed, reducing energy consumption, or enhancing the user experience, heterogeneous computing plays a key role.

With the continuous development of technology, heterogeneous computing will continue to evolve and integrate with more hardware and technologies, bringing more innovative applications and services to the HarmonyOS Next ecosystem. Developers can use the capabilities of heterogeneous computing to develop more efficient and intelligent applications to meet the growing needs of users. It is hoped that through the introduction of this article, everyone can have a deeper understanding of the heterogeneous computing technology in HarmonyOS Next, inspire more exploration of heterogeneous - computing application scenarios in actual development, and jointly promote the development of HarmonyOS Next technology. If you encounter other problems during the practice process, you are welcome to communicate and discuss together! Haha!