Revolutionizing High-Performance Computing: The Concept of Unified Multi-Processor Architecture in a Single CPU
Keywords:
AI accelerators, CPUs, DVFS, Dynamic power optimization, Energy efficiency, GPUs, Heterogeneous Processing Units (HPC), Interconnect optimization, Latency reduction, NUMA-aware scheduling, Scalability, Shared memory architecture, Task scheduling, TPUs, Unified multi-processor architectureAbstract
High-Performance Computing (HPC) is a crucial enabler for accelerating the pace of innovation across AI, big data analytics, scientific simulations, and emerging technologies. However, traditional HPC systems suffer from hardware fragmentation, inefficient energy usage, and latency issues concerning inter-processor communication because of the use of separate units for a Central Processing Unit (CPUs), Graphic Processing Units (GPUs), and Tensor Processing Units (TPUs). This paper deals with a Unified Multi-Processor Architecture that integrates heterogeneous processing units inside one unified CPU to address these bottlenecks.
It will integrate the CPUs, GPUs, TPUs, and AI accelerators very tightly under unified memory and high-speed interconnects for a cohesive computational ecosystem. Advanced task scheduling algorithms, unified memory architecture, and dynamic power optimization techniques will make the proposed system unparalleled in efficiency and scalability. This research will use algorithms such as NUMA-aware scheduling, Dynamic Voltage and Frequency Scaling (DVFS), and load balancing to achieve the best resource allocation and energy efficiency. Therefore, the unified architecture brings a transformational possibility compared to the existing HPC methodologies. It minimizes latency, boosts computation speed, reduces power consumption, and enables compact hardware for most applications-from robotics and bioinformatics to climate modeling and financial systems. It also addresses some of the concerns regarding thermal management and software compatibility with implementations.
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