Pie: Pooling CPU Memory for LLM Inference (2411.09317v1)

Abstract: The rapid growth of LLMs has revolutionized natural language processing and AI analysis, but their increasing size and memory demands present significant challenges. A common solution is to spill over to CPU memory; however, traditional GPU-CPU memory swapping often results in higher latency and lower throughput. This paper introduces Pie, an LLM inference framework that addresses these challenges with performance-transparent swapping and adaptive expansion. By leveraging predictable memory access patterns and the high bandwidth of modern hardware like the NVIDIA GH200 Grace Hopper Superchip, Pie enables concurrent data swapping without affecting foreground computation, expanding effective memory without added latency. Adaptive expansion dynamically adjusts CPU memory allocation based on real-time information, optimizing memory usage and performance under varying conditions. Pie maintains low computation latency, high throughput, and high elasticity. Our experimental evaluation demonstrates that Pie achieves optimal swapping policy during cache warmup and effectively balances increased memory capacity with negligible impact on computation. With its extended capacity, Pie outperforms vLLM by up to 1.9X in throughput and 2X in latency. Additionally, Pie can reduce GPU memory usage by up to 1.67X while maintaining the same performance. Compared to FlexGen, an offline profiling-based swapping solution, Pie achieves magnitudes lower latency and 9.4X higher throughput.

• The paper’s main contribution is performance-transparent swapping that enables concurrent CPU-GPU data transfers, reducing latency in LLM inference.
• It introduces adaptive expansion that dynamically adjusts CPU memory allocation based on real-time workloads, delivering up to 1.9× throughput gains and halving latency.
• The framework offers practical benefits by minimizing dependence on high-capacity GPUs and optimizing resource utilization across CPU and GPU architectures.

Pie: Pooling CPU Memory for LLM Inference

The research paper titled "Pie: Pooling CPU Memory for LLM Inference," authored by Yi Xu, Ziming Mao, Xiangxi Mo, Shu Liu, and Ion Stoica, investigates a novel framework called Pie designed to enhance the efficiency of LLM inference by addressing the memory constraints of current GPU architectures. The primary innovation lies in performance-transparent swapping and adaptive expansion, which leverage CPU memory to augment effective GPU memory capacity, thereby mitigating latency and throughput limitations commonly associated with traditional GPU-CPU memory management strategies.

Key Contributions

Pie sets itself apart by introducing performance-transparent swapping, which facilitates concurrent data transfer operations between CPU and GPU without impeding ongoing computations on the GPU. This mechanism capitalizes on predictable memory access patterns inherent in LLM workloads, ensuring that data is prefetched with high efficiency, minimizing computational delays associated with memory paging.

Adaptive expansion is another significant contribution of this work. It dynamically adjusts the allocation of CPU memory based on real-time workload and system conditions, ensuring that memory resources are optimally utilized without compromising performance. This adaptative approach contrasts with static offline profiling that fails to account for runtime variations in workloads or system configurations, a limitation evident in preceding solutions like FlexGen.

From a performance evaluation perspective, Pie demonstrates commendable improvements over existing LLM inference frameworks. Specifically, it achieves superior throughput compared to vLLM, with reported increases of up to 1.9 times and latency reductions up to 2 times. Furthermore, compared to FlexGen, Pie exhibits substantially lower latency and considerably higher throughput, indicating its efficiency in managing memory resources without resorting to extensive offline profiling.

Theoretical and Practical Implications

Theoretically, Pie proposes an advanced methodology for effectively expanding GPU memory capacity by integrating CPU memory without introducing latency penalties generally linked to data transfers. It highlights the potential for leveraging high-bandwidth interconnects, such as NVIDIA's NVLink, and characteristics of modern GPUs, like the NVIDIA GH200 Grace Hopper Superchip, to enhance memory management strategies.

Practically, Pie's framework offers tangible improvements in LLM serving systems, potentially reducing dependency on expensive high-capacity GPUs by effectively utilizing the broader memory resources available across GPU and CPU architectures. This presents a cost-effective approach for processing larger batches and handling more substantial workloads within the constraints of existing GPU configurations.

Future Directions

The framework proposed in this paper opens several avenues for future research. One area of exploration could involve integrating Pie with distributed systems to evaluate its scalability across multiple nodes. Additionally, the framework's adaptability to other types of machine learning models beyond LLMs could be examined to assess its versatility in varied computational contexts.

Furthermore, there is scope for exploring the integration of Pie with emerging hardware technologies that offer even greater bandwidth and memory capacities, potentially driving more significant performance gains in the context of evolving AI workloads.

In summary, Pie represents a significant step forward in the ongoing effort to optimize LLM inference through innovative memory management strategies, offering a paradigm that effectively balances performance efficiency with resource utilization.