👉 MH Computing, or Multi-Heterogeneous Computing, is an architectural approach that integrates various types of computing resources—such as CPUs, GPUs, FPGAs, and specialized accelerators—into a single system to optimize performance and efficiency for diverse computational tasks. This approach leverages the unique strengths of each resource type, allowing for better handling of complex workloads that span different types of data and algorithms. For example, GPUs excel at parallel processing for tasks like deep learning, while CPUs are better suited for sequential operations and general-purpose computing. By intelligently distributing tasks across these heterogeneous components, MH Computing aims to achieve higher performance, energy efficiency, and cost-effectiveness compared to traditional homogeneous computing systems.
