CPU-GPU Hybrid Serving Infrastructure

• CPU-GPU hybrid serving infrastructures are platforms combining CPU flexibility with GPU parallelism to optimize throughput, memory usage, and workload allocation.
• They employ task decomposition and dynamic scheduling to assign irregular tasks to CPUs and compute-intensive kernels to GPUs, ensuring efficient load balancing.
• Empirical results demonstrate significant speedups and scalability, making these systems vital for high-performance computing, data analytics, and scientific simulations.

A CPU-GPU hybrid serving infrastructure refers to systems and software frameworks that orchestrate both CPUs and GPUs to collaboratively execute computational workloads. Such infrastructures exploit the unique strengths of CPUs (flexibility, irregular computation, low-latency control path) and GPUs (massive parallelism, high memory bandwidth, throughput for regular workloads) within a unified execution model. They have become increasingly central in large-scale scientific simulations, machine learning inference, data analytics, graph processing, and high-performance computing, where performance, scalability, and efficient resource utilization are critical.

1. Foundational Principles of CPU-GPU Hybrid Serving

The principal motivation behind hybrid infrastructures is the observation that heterogeneous computational resources can be synergistically leveraged to maximize throughput, minimize latency, and cope with memory constraints. Purely GPU- or CPU-focused systems often underutilize available resources, exhibit bottlenecks for irregular or data-dependent workloads, or face hardware resource limitations (notably, GPU memory for large models or datasets).

Hybrid models decompose a computational pipeline into subtasks, assigning each to the processing unit (CPU or GPU) best suited for its computational pattern:

• CPUs execute control-heavy, irregular, or memory-constrained phases. This includes branch-heavy graph algorithms (Mishra et al., 2020), random-walk data generation (Zhu et al., 2019), or the nonlinear solver stages requiring double precision (Chen et al., 2011).
• GPUs accelerate massively parallel, compute- or bandwidth-bound kernels. This includes particle integration in PIC (Chen et al., 2011), dense matrix operations (Das et al., 2022), large batched tensor algebra (Menczer et al., 2023), or throughput-oriented batch attention computations in LLM inference (Fan et al., 3 Jun 2025).

Effective hybrid infrastructures minimize redundant data transfers, orchestrate pipelined or concurrent execution, and balance loads dynamically according to profiling or runtime observation.

2. Architectural Patterns and Task Decomposition

Hybrid serving architectures employ several microarchitectural and software organizing principles:

• Segregation of computational phases based on workload characteristics (e.g., in implicit PIC, the JFNK nonlinear solver remains on the CPU in double precision, while the adaptive particle mover is offloaded to the GPU in single precision (Chen et al., 2011)).
• Task pipelining and batch splitting. In LLM inference, for example, query, key, value projection and feed-forward phases may run on the GPU in one pipeline stage, while self-attention is either run concurrently on the CPU or partitioned across CPU and GPU depending on dynamic scheduler decisions (Fan et al., 3 Jun 2025).
• Dynamic load-balancing and scheduling. Some frameworks profile execution times offline (or iteratively online) to inform a workload split that minimizes total wall-time or maximizes resource utilization (e.g., the APEX scheduler for LLMs maintains maximal concurrency with profiling-informed asynchronous overlap (Fan et al., 3 Jun 2025); autotuning parameters in fast multipole methods are adjusted at runtime to minimize overall runtime (Holm et al., 2013)).
• Memory hierarchy management. In memory-constrained scenarios (large-scale LLMs, ultra-large datasets), state (e.g., the key-value cache, model experts, or multi-level solver matrices) is selectively kept or offloaded between CPU DRAM and GPU memory, with dynamic prefetching and caching (as in HybriMoE for MoE inference (Zhong et al., 8 Apr 2025)).
• Distributed and shared-memory models. Multi-node or multi-device systems may combine intra-node shared memory (to synchronize CPU cores and GPUs on a single host) with inter-node MPI communication for cluster-wide scaling (Hassan et al., 2011, Zhu et al., 2019).

3. Scheduling and Dynamic Adaptation

A central technical challenge is dynamic scheduling—assigning work to heterogeneous processors so as to maximize throughput despite changing workloads, irregular computation, or unpredictable data distributions.

• Profiling-informed dispatch is used to model per-batch or per-layer execution times. In APEX (Fan et al., 3 Jun 2025), an offline profiler measures the latency of each transformer layer’s subcomponents, allowing the scheduler to solve inequalities such as:

$$T_{\text{gpuonly}} = T_{\text{glinear}} + T_{\text{gatt}}$$

$$T_{\text{overlap}} \approx 2\cdot T_{\text{glinear}} + T_{\text{gatt}}$$

and to compute whether hybrid or pure-GPU execution maximizes token throughput.

• Intra-layer dynamic routing and impact-driven prefetch. In HybriMoE (Zhong et al., 8 Apr 2025), expert activation instability in MoE inference is addressed using runtime simulation of the execution timeline, determining expert assignment to CPU or GPU based on cache status and estimated compute load, with prefetch decisions made via impact-driven simulation of preloading effects on future pipeline stalls.
• Dynamic autotuning. For kernel-based solvers (e.g., FMM (Holm et al., 2013)), autotuners monitor per-phase runtimes and iteratively adjust task-sharing parameters (such as tree level at which the split between CPU and GPU occurs, or the multipole separation tolerance $\theta$) to maintain workload balance and minimize overall job completion time.
• Work queue orchestration. In data analytics and spatial join workloads (Gowanlock, 2018), central work queue management ensures that dense or regular queries are sent in large batches to the GPU, while sparse or control-divergent queries are processed on multicore CPUs, dynamically reserving or reassigning tasks as backpressure or idle periods arise.

4. Performance Optimization, Memory Management, and Data Locality

Hybrid infrastructures realize performance gains and resource utilization improvements through careful code and systems optimization:

• Mixed-precision and low-level arithmetic optimization. For example, replacing high-latency IEEE division and sqrt operations with faster device-specific intrinsics, applying Newton–Raphson iterations for accuracy, and implementing mixed-precision kernels where alternate hardware units specialize by phase precision requirements (Chen et al., 2011, Das et al., 2022).
• Memory traffic minimization and overlap. In multigrid solvers (Ganesan et al., 2020), only matrices required for the current level of the hierarchy are loaded onto the GPU, with overlapped data transfers (CUDA streams) minimizing global memory residency and enabling extremely large systems to be solved on a single GPU with minimal device memory. In LLM/decoder serving, key-value cache offloading and deferred synchronization are used to deal with exponential memory growth during long autoregressive decoding sessions (Fan et al., 3 Jun 2025).
• Cache management and predictive prefetching. In MoE models, traditional LRU/LFU caching is inadequate due to erratic expert activation. Instead, dynamic score-based policies such as Minus Recent Score (MRS) weight historical activation probability and current routing scores to retain experts likely to be reused—increasing cache hit rates and reducing unnecessary PCIe transfers (Zhong et al., 8 Apr 2025).
• Throughput-maximizing batching. Large monolithic or adaptive batches are used to sustain GPU saturation in high-density workloads, while multi-thread splitting is applied within large kernels to hide kernel launch latency and balance irregular work at warp or thread block granularity (Gowanlock, 2018, Menczer et al., 2023).
• Overlapping CPU and GPU execution. Frameworks explicitly pipeline data processing, instruction dispatch, and post-processing on the CPU or in host memory while the GPU processes data already transferred—doubling effective pipeline throughput compared to sequential execution (Hassan et al., 2011, Zhu et al., 2019, Menczer et al., 2023).

Table: Illustrative Example—Work Assignment in Hybrid Infrastructures

Workload Domain	CPU Assignment	GPU Assignment
Implicit PIC simulation (Chen et al., 2011)	JFNK nonlinear solver (double precision)	Particle mover (single precision, adaptive)
Node embedding (Zhu et al., 2019)	Online random walk sampling, augmentation	Parallel negative sampling, SGD on embeddings
MoE LLM Inference (Zhong et al., 8 Apr 2025)	Low-load, uncached experts, expert management	High-load/cached experts, heavy tensor ops

5. Empirical Performance, Robustness, and Scalability

Quantitative evaluation in CPU-GPU hybrid infrastructures demonstrates substantial improvements across diverse workloads:

• Order-of-magnitude speedup is common when the computationally dominant, regular phase is offloaded to the GPU. For example, the implicit particle-in-cell (PIC) solver’s hybrid implementation achieves up to 100–300× speedup over a CPU-only double-precision run, with GPU efficiency hitting 20–25% of peak theoretical FLOPS and energy/charge conservation maintained within $10^{-6}$ throughout demanding long-timescale simulations (Chen et al., 2011).
• Memory efficiency enabling larger problem sizes. Hybrid AMG solvers solve systems up to 7× larger than GPU-only implementations at similar performance, using only 1/7th the GPU memory (Ganesan et al., 2020).
• Dynamic scalability. Through distributed design and hierarchical communication (node-level shared memory, cluster-level MPI), frameworks handle petascale data analysis (e.g., up to 2.5 teravoxels/sec in astronomical volume rendering (Hassan et al., 2011)) and scale to tens of millions of nodes and billions of edges in graph embedding (Zhu et al., 2019).
• Resource utilization. Studies consistently find that hybrid systems maintain high (>90%) utilization of both devices, as opposed to one idling while the other is overburdened (Kothapalli et al., 2013, Soldado et al., 2015, Gowanlock, 2018). For instance, in fine-grained graph benchmarks, CPU and GPU partitions process edge-centric workloads with dynamic work-stealing to avoid resource starvation (Rossi et al., 2016).
• Robustness under shifting workloads. Dynamic autotuning adapts to changing workload characteristics (e.g., dynamic clustering in FMM, variable expert activation in MoE), ensuring stable throughput and error tolerances without manual parameter reconfiguration (Holm et al., 2013, Zhong et al., 8 Apr 2025).

6. Limitations, Deployment Considerations, and Applicability

Deployment of CPU-GPU hybrid serving infrastructure presents several challenges:

• Partitioning and scheduling complexity. Partition determination (work sharing, task allocation, workload split) is nontrivial, particularly for highly irregular or data-dependent tasks (such as sparse matrix kernels, or NP-hard optimal task mapping in irregular graph algorithms) (Kothapalli et al., 2013).
• Communication and PCIe bottlenecks. PCIe or NVLink bandwidth remains a limiting factor for latency-sensitive workloads or those with heavy intermediate data movement (e.g., exchanging partial results, key-value or expert transfers). Solutions include minimizing transfer scope, maximizing in-device reuse, and using overlap mechanisms (Chen et al., 2011, Fan et al., 3 Jun 2025, Zhong et al., 8 Apr 2025).
• Algorithm redesign requirements. Existing homogeneous (CPU-only or GPU-only) algorithms often require substantial structure revisions to exploit hybrid execution effectively (e.g., the iterative refinement in betweenness centrality, queue hierarchy in IWPP, or strided batching for tensor networks) (Teodoro et al., 2012, Mishra et al., 2020, Menczer et al., 2023).
• Overhead and sensitivity for small workloads. For low-variant or short-duration tasks, the management overhead of dual-device execution can negate any throughput improvements, as empirically observed in hybrid evolutionary computation simulations (Eynaliyev et al., 16 Feb 2025).
• Adaptive capacity for workload variation. Highly variable or unpredictable workloads necessitate periodic re-profiling and adaptive re-allocation for sustained gains (Holm et al., 2013, Eynaliyev et al., 16 Feb 2025).

Hybrid infrastructures find greatest applicability in environments where:

• Heterogeneous workload characteristics preclude any single optimal device allocation.
• Large memory footprints, bandwidth limitations, or data-dependent branching inhibit the scalability of exclusive GPU serving.
• Multi-user, cloud, or edge environments require flexible, cost-effective, and fault-tolerant allocation of both CPU and GPU resources (Buniatyan, 2019, Fan et al., 3 Jun 2025).

7. Future Directions and Outlook

Recent research highlights several promising directions for hybrid serving systems:

• Refined scheduling and autotuning. Further advances are anticipated in online, performance-model-informed scheduling that exploits asynchronous overlap, dynamic batch adjustment, and load-balancing without incurring significant compute or communication overheads (Holm et al., 2013, Fan et al., 3 Jun 2025).
• Expanded algebraic and graph workload support. Integration with next-generation hardware architectures (e.g., unified CPU-GPU memory, SmartNIC offload, specialized AI accelerators) may further lower synchronization and data movement costs, making hybrids even more attractive for large-scale and real-time applications (Das et al., 2022, Menczer et al., 2023).
• Generalization to distributed and serverless contexts. The use of hybrid infrastructures in distributed cloud environments, with advanced resource provisioning (mixing spot and on-demand compute) and distributed file systems, will continue to enable scalable, fault-tolerant operation at petaflop scale (Buniatyan, 2019).
• Automated hybridization tools. Programming model innovations—such as explicit annotation for hybrid task assignment or automated autotuning of partitioning—are likely to increase adoption in application domains that have hitherto relied on manual tuning or homogeneous deployments (Soldado et al., 2015, Zhong et al., 8 Apr 2025).

In summary, CPU-GPU hybrid serving infrastructures represent a mature and highly effective paradigm for scientific computation, machine learning, and data analytics, combining flexible resource allocation, dynamic scheduling, and algorithmic co-design to overcome the bottlenecks of pure CPU or GPU execution. Quantitative performance gains, scalability, and robustness across diverse workload patterns are well substantiated in the contemporary research literature.