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Ji-Dong Zhai

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EcoxPU: Autonomous Multi-Agent Orchestration for Energy-Efficient Disaggregated LLM Inference on Heterogeneous xPUs

Large Language Model (LLM) inference is increasingly served on disaggregated clusters that combine different accelerator types, including NVIDIA GPUs, Huawei Ascend NPUs, and Kunlunxin AI processors. We show that these heterogeneous xPUs exhibit distinct energy-performance behaviors across inference phases and model mo...

Dong Dong, Cheng-Zhang Wu, Zheng Chen et al. · 0 citations
Book Open access Sep 2026

StreamTrace: Fast Trace Analysis for Large-Scale Parallel Applications on a Single Node

StreamTrace is presented, a streaming trace analysis system that enables efficient analysis of massive traces on a single computing node with bounded memory consumption and introduces two key techniques: communication pattern-aware chunk partitioning that minimizes cross-chunk dependence and dynamic priority-based chun...

Yu-Yang Jin, Ji-Dong Zhai · 0 citations
Book Open access Sep 2026

StreamTrace: Fast Trace Analysis for Large-Scale Parallel Applications on a Single Node

Trace-based performance analysis provides essential insights for understanding and optimizing large-scale parallel applications. However, traces from applications running on tens of thousands of processes can easily exceed terabytes, far surpassing the memory capacity of typical computing nodes. Existing approaches eit...

Yuyang Jin, Ji-Dong Zhai · 0 citations
#artificial intelligence Preprint Sep 2026

Decoupling Readiness from Release for Tail-Aware Scheduling of Agentic LLM Workflows

This work presents a tail-risk-aware turn release scheduling method that jointly decides which ready turn to release next and how much released but unfinished work to maintain, and reduces the P95 of workflow flow time under contention, achieving up to a \(3.50\times\) speedup.

Bo-Chao Feng, Jian-Jiang Li, Haojie Wang et al. · 0 citations
#small language model Preprint Sep 2026

Epoch: Compiling Diffusion Blocks for Sparse MoE Serving

A serving system that treats the diffusion block as a compilation unit that improves end-to-end execution time by up to 2.7 times over the strongest surviving baseline under the same 8-GPU placement and remains feasible at the largest batch sizes where multiple baselines run out of memory, while preserving task quality...

Jia-Nian Zhu, Hang Wu, Ying-Hui Li et al. · 0 citations
Book Open access Jul 2026

UniEP: Unified Expert-Parallel MegaKernel MoE for LLM Training

UniEP fuses the MoE communication and computation into MegaKernels, effectively transforming complex architectural tuning into a unified parameter search space for automated adaptability and incorporates a deterministic token ordering mechanism that guarantees numerical consistency with sequential execution, even under...

Size Zheng, Xuegui Zheng, Li-Wen Chang et al. · 1 citation
Book Open access Jul 2026

Taming Dynamic Diffusion LLM Inference through Virtual Static Execution

Prism abstracts the highly dynamic diffusion workload into a predictable, static execution flow transparent to the compiler, and achieves this via three techniques: spatial regularization, temporal stabilization, and specialized kernels that selectively bypass padding data.

Jianian Zhu, Hang Wu, Yinghui Li et al. · 0 citations

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