Author: Muhammed Shafin P
Abstract
This is a conceptual architecture proposal and may have theoretical flaws or possible alternatives. Modern gaming workloads demand low-latency, high-throughput compute and graphics pipelines. Traditional single-CPU, single-GPU architectures have reached efficiency plateaus due to memory latency bottlenecks and the limits of monolithic frequency scaling.
This article proposes a comprehensive board- and kernel-level design concept: a role-based multi-CPU architecture with dedicated, physically proximate RAM banks and intelligent kernel mediation, designed to be perceived as a single unified system by a virtual machine. This approach aims to reduce latency while maximizing throughput for gaming and real-time rendering workloads.
Introduction
Gaming systems today rely on ever-larger CPUs and GPUs, often with diminishing returns. Multi-core CPUs and powerful discrete GPUs exist, but the fundamental bottleneck of memory access latency and inefficient scheduling across multiple CPUs limits performance. Rather than attempting to eliminate latency entirely—which is physically impossible—this concept seeks to minimize latency through hardware-aware design and software-assisted task management.
The architecture combines multiple CPU cores, a single discrete GPU, and integrated GPUs for offload and display handling, while the kernel or hypervisor orchestrates task allocation. To the virtual machine, this complex underlying topology is abstracted into a single, cohesive system. The VM sees one large CPU pool, unified memory, and a single GPU interface, while the underlying hardware and kernel perform intelligent optimizations.
Core Concept
1. Dedicated RAM per CPU
Each CPU or CPU chiplet is paired with its own dedicated memory banks, physically located as close as possible to the memory controller to minimize latency. This ensures that most memory accesses remain local to the CPU, significantly reducing NUMA penalties and providing a deterministic performance baseline.
2. CPU Role Assignment
The architecture assigns specific roles to CPUs:
- Bootstrap CPU (BSP): Dedicated purely to OS initialization and basic system tasks. It does not handle high-performance workloads such as conversions, heavy mathematical computations, or AES-NI encryption; it only handles fundamental OS-level operations to ensure the system boots reliably and supports complex OS environments.
- Main CPU: Optimized for high IPC and frequency, the main CPU handles latency-critical tasks and directly controls the discrete GPU (dGPU) for rendering workloads. Its integrated GPU (iGPU) can be used for display tasks or other high-speed graphical functions.
- Supporting CPUs: Auxiliary CPUs perform background workloads such as AI calculations, asset streaming, and general offload tasks. Their integrated GPUs (if available) may also be used for additional offload tasks or lightweight graphical processing, ensuring the main CPU is free for high-priority tasks.
By aligning hardware roles with software tasks and assigning GPUs accordingly, the system ensures predictable performance and maximizes effective use of multiple CPUs and GPU resources.
3. Single Discrete GPU with Integrated GPU for Display and Offload
The architecture uses one discrete GPU for all heavy rendering tasks. The integrated GPU of the main CPU handles display output and can assist in processing tasks, while integrated GPUs of supporting CPUs can be leveraged for offload workloads. This setup provides high throughput and efficient utilization of all available graphical resources without the complexity of multi-dGPU fusion.
4. Kernel/Hypervisor Mediation
A bare-metal kernel or lightweight hypervisor abstracts the underlying multi-CPU and GPU architecture into a single cohesive system for the VM. The VM sees a unified CPU and GPU resource set, unaware of the underlying NUMA nodes, CPU roles, or memory partitions. The kernel manages:
- CPU task placement and pinning according to role and priority.
- NUMA-aware memory allocation to reduce remote memory access.
- GPU buffer management for zero-copy access where possible.
- Intelligent migration of background tasks to supporting CPUs without affecting latency-sensitive workloads.
- Coordination between the main CPU, its dGPU, and iGPU, as well as offload to supporting CPU iGPUs for auxiliary workloads.
This ensures that even with complex OS initialization or high-demand workloads, the system operates smoothly, presenting a unified, high-performance environment to the VM.
Advantages of the Proposed Architecture
- Reduced latency through physical design: Dedicated RAM per CPU reduces costly cross-node memory access.
- Deterministic CPU performance: Role-based CPU assignment prevents thread migration from introducing jitter.
- Simplified GPU handling with offload support: Main CPU handles dGPU for critical rendering, iGPUs handle display and auxiliary workloads.
- Unified VM perception: Complex underlying hardware appears as one large CPU and memory system, allowing games and applications to operate without modification.
- Scalable and robust: Supporting CPUs and their iGPUs can handle background and offload tasks, while the bootstrap CPU ensures reliable OS initialization without performing high-performance operations.
Challenges and Considerations
- Physical latency remains: Interconnect and coherence traffic introduces unavoidable latency; the goal is reduction, not elimination.
- Firmware complexity: Accurate topology exposure via ACPI/UEFI or Device Tree is essential for kernel task scheduling.
- Driver dependencies: GPU drivers must support features such as VFIO, DMA-buf, or zero-copy to fully exploit the architecture.
- PCB and thermal design complexity: Multi-channel RAM and high-speed interconnects increase layout and power design challenges.
- Software policy sophistication: Intelligent kernel or hypervisor modules are required to effectively manage CPU roles, memory placement, and GPU offload.
Prototyping Path
- Software-only emulation: Use a high-core-count CPU to emulate role-based CPU nodes with NUMA and pinning to validate scheduling strategies.
- Two-socket testing: Measure real inter-socket latency and refine memory allocation heuristics.
- Kernel policy engine: Develop a module or daemon that reads CPU roles, pins tasks, and migrates pages intelligently.
- GPU passthrough experiments: Assign the dGPU to a VM while leveraging iGPUs for display and offload tasks to test unified VM perception.
- Measurement and iteration: Collect frame-time statistics, DMA latency, page migration frequency, and CPU utilization per node to refine architecture and policies.
Conclusion
This role-based multi-CPU architecture is a conceptual design and may have theoretical flaws or better alternatives. While latency cannot be eliminated, careful hardware design combined with intelligent kernel/hypervisor mediation can minimize latency to acceptable levels for gaming and real-time workloads.
By dedicating RAM per CPU, assigning CPU and GPU roles—including main CPU control of dGPU and offload use of iGPUs—and managing memory placement at the kernel level, the system presents a unified, high-performance environment to the VM. The bootstrap CPU ensures reliable OS initialization without handling high-performance tasks, while supporting CPUs handle auxiliary and background workloads efficiently.
This may come to the real world or may not — it has big flaws like high cost and complexity. So the chance of not getting to the real world is also high.
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