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Heterogeneous compute

What is heterogeneous compute?

Heterogeneous compute is the concept of using different types of processing units or accelerators in conjunction.

In a heterogeneous compute environment, multiple processors or accelerators with different architectures, capabilities, and strengths work together to solve computational problems. A classic example of heterogeneous compute is using a CPU and GPU together. Going beyond that, you can combine many types of processing units, such as CPUs, GPUs, DSPs (Digital Signal Processors), FPGAs (Field-Programmable Gate Arrays), or specialized AI accelerators like TPUs (Tensor Processing Units) or other domain-specific accelerators.

Why use heterogeneous compute?

Each type of processing unit in a heterogeneous system is designed to excel in specific types of computations. For example, CPUs are well-suited for general-purpose computing tasks, GPUs are highly efficient for parallel processing and graphics-intensive workloads, DSPs excel in signal processing applications, and FPGAs offer flexibility for custom hardware implementations. By leveraging the strengths of each processing unit, heterogeneous computing systems can achieve improved performance, energy efficiency, and task-specific optimization.

Heterogeneous computing enables more efficient and powerful computing systems by leveraging diverse processing units to tackle different types of workloads. It plays an important role in various domains including HPC, AI, or computer vision.

Some benefits of heterogeneous compute include performance improvement, task-specific optimization, energy efficiency, flexibility, and scalability.

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