Zero-knowledge succinct non-interactive arguments of knowledge (zkSNARKs) are a key technology to privacy-preserving applications today. The complexity of proof generation, however, heavily constrains throughput in latency-sensitive environments. The computational burden primarily stems from two fundamental algorithms: Multi-Scalar Multiplication (MSM) and the Number Theoretic Transform (NTT). We propose a series of optimizations for these two kernels, including computation-transfer pipelining, load balancing, and memory access fusion, achieving 1.97 × to 2.16 × proof generation speedup over a state-of-the-art open source GPU acceleration library. Our design also supports out-of-core computation, enabling the generation of large-scale ZKP proofs.
Xinwei Qiang, Liukun Yu, Xiyu Wang et al.· IEEE International Symposium...· 0 citations
The increasing use of renewable energy in data centers creates an opportunity to reduce the carbon footprint of energy-intensive LLM inference workloads. Unlike traditional stable power supply, renewable generation fluctuates over time, making it difficult to match computation demand with available energy. However, existing LLM serving systems primarily optimize latency and throughput without considering energy supply dynamics, leading to underutilization of renewable energy and unnecessary reliance on thermal power, and consequently, higher carbon emissions. We present GreenAlign, a renewable-aware scheduling framework that addresses this mismatch by treating best-effort (BE) requests as temporally shiftable load. GreenAlign enforces a power-constrained policy that executes BE requests using only residual renewable energy under normal conditions, and introduces a backlog risk metric to selectively relax this constraint when deadline violations are imminent. To ensure responsiveness, it maintains standby capacity to absorb unpredictable latencycritical (LC) bursts and uses lightweight length estimation to handle request uncertainty. Simulation results show that GreenAlign significantly reduces thermal energy usage while preserving LC latency and BE deadline satisfaction.
Chang Liu, Jiacheng Liu, Xiaofeng Hou et al.· Fall Joint Computer Conferen...· 0 citations
Prefix caching has become a key technique for LLM serving, and nowadays the reusable KVCache contents are often hosted on distributed servers. For long-context LLM inferences with high cache hit ratio, cross-server KVCache transmission has become an emerging performance bottleneck; such network-intensive LLM inferences are increasingly prevalent in the coming era of agentic AI. However, existing LLM inference engines are essentially compute-centric; we find that they are highly inefficient when serving such workloads due to compute-stage service blocking and ignorance of KVCache-transfer cost. To efficiently serve network-intensive LLM inferences, in this paper, we design Sanic, an optimized LLM engine that treats KVCache transmission as a first-class citizen. Viewing KVCache loading and computation as equally-significant stages, Sanic decouples their service control and allows each stage to progress autonomously in an asynchronous manner, thereby improving the overall resource utilization. Moreover, when scheduling competing LLM inferences, Sanic treats the KVCache loading delay as an independent factor in service cost modeling, which is more accurate and can yield better scheduling decisions. Our testbed experiments with diverse benchmarks show that, Sanic can substantially enhance the service efficiency of network-intensive LLM inferences, improving the SLO-attainment by up to 61.67%.
Weiye Wang, Chen Chen, Junxue Zhang et al.· Asia-Pacific Workshop on Net...· 0 citations
Federated Learning (FL) allows edge clients to collaborate in model training with data privacy preserved, yet it is known to suffer low training efficiency and model accuracy. Given that efficiency and accuracy are usually conflicting objectives, existing practices increasingly employ an adaptive scheme that changes the FL configurations (e.g., quantization or sparsification level) based on runtime training status, for which accurate training diagnosis—used for guiding the optimization actions—is crucial. However, while training diagnosis is a common task shared by different optimization schemes, existing works propose their diagnosis methods in an ad-hoc manner, which yield multiple limitations. First, the diagnosis metric in an optimization scheme may sometimes be less accurate than others; second, existing schemes fail to fully exploit the diagnosis result by applying it for only one optimization action; third, existing methods usually do not perceive cross-client data heterogeneity, failing to simultaneously enhance FL accuracy. To tackle those limitations, we make a systematical study on the training diagnosis methods of multiple optimization schemes, and propose metric grafting—replacing a scheme’s diagnosis metric with a better one to improve the training performance. Moreover, to fully exploit the potential of training diagnosis, we build a system platform that supports flexible combinations of training diagnosis and optimization actions (i.e., single-diagnosis-multiple-actions and multiple-diagnosis-multiple-actions). Evaluation on testbeds show that, with metric grafting and advanced diagnosis-action combinations, we can substantially improve the efficiency and accuracy performance of FL.
Jiayi Zhang, Zuo Gan, Chen Chen et al.· IEEE Transactions on Mobile...· 0 citations