Heterogeneous computing is the process of using two or more different kinds of processors to boost performance and energy efficiency. It typically consists of one type of CPU, as well as other, dissimilar coprocessors. Typically, the different types of processors have different processing capabilities. This is particularly useful for applications that require specialized processing. This is why the name heterogeneous computing is a catch-all term for a variety of different systems.
As personal computers become more complex, the power of these individual cores must be harnessed to perform more diverse tasks. For instance, the CPU is great for general-purpose, single-threaded jobs. Other cores are more efficient for different types of tasks. For example, graphics processors were designed to run games at high resolutions, and they are good at parallel number crunching. Moreover, other hardware engines handle cryptography and video encoding. In this scenario, the power of the CPU is used to perform other tasks.
Another challenge of heterogeneous computing is memory. The CPU has its own pool of memory, separate from the GPU. When the GPU executes a computation, it must first copy data from system memory to its GPU’s RAM. This is inefficient because the two devices cannot access the same data. The CPU and GPU have different types of memory, and the GPU has to copy data back and forth between the two. Hence, it can only do so efficiently if both components are used as a single platform.
Similarly, single-processor systems can only handle single-threaded tasks. This limits AI-enabled Industry 4.0 environments, limiting worker safety and productivity. Heterogeneous computing is the best way to harness the power of different types of data and extract the maximum value from each. It is especially effective for AI systems. The same can be said for genomic computing. So, why is heterogeneous computing the way it is?
Heterogeneous computing is important for AI and other applications. It allows the different types of data to be analyzed differently and thus improves performance. It is also the best choice for AI-enabled applications that can handle both parallel and serial tasks. It is crucial to ensure that your application is optimized for a heterogeneous computing platform to get the most out of it. It is possible to use multiple processors in a single machine, and even multiple machines can share one memory.
Historically, computers have specialized cores that are designed to perform dedicated tasks. For instance, CPUs are excellent for general-purpose, single-threaded jobs. Other cores can handle different tasks more effectively. For example, graphics processors are specialized for games. They can also handle parallel number-crunching tasks. Other components of a computer are used to handle different types of work. Unlike desktop and laptop computers, hybrid PCs can be configured to use various types of computing.
Currently, the Heterogeneous Compute market is dominated by Arm-led systems. These systems are able to match the performance of traditional x86 systems. According to Wikibon, Arm-led systems are expected to dominate the enterprise Heterogeneous Computing market over the next decade. For AI systems, the company is forecasting that the vast majority of AI-related hardware will be dedicated to inference systems.
The main benefit of using heterogeneous computing is scalability. While homogeneous computing is the process of adding more cores and GPUs to increase the number of available resources, heterogeneous computing can handle the increasing number of requests. It is also better at handling tasks that aren’t commonly performed in parallel. It is a multi-tasking platform that uses different types of hardware. The power of each core is harnessed to improve performance.
Today’s personal computers are based on a heterogeneous architecture. Each core has specific tasks and is more efficient at handling those tasks. For example, the CPU is designed to perform general-purpose, single-threaded tasks, while graphics processors are geared towards a particular task. The CPUs are not the only type of computing device on the market. They can handle video encoding and image processing, for example.
To optimize performance and energy efficiency, heterogeneous computing systems have multiple specialized types of hardware. The main components of such systems are multi-core processors and GPUs. In some cases, the CPU is a specialized device and the GPU is a general-purpose device. HSA members can also use dedicated processors in combination with a common CPU. Heterogeneous systems are also more flexible than homogeneous systems.