arXiv subjects

Leshan Li

Publications and source records attributed to Leshan Li.

5 recordsLinked to original sources

arXiv2026

ForgeKernel: Self-Evolving Kernels Catching and Surpassing Experts and Agents

Purpose-built kernel agents can write high-performance GPU kernels, but existing systems generally lack cross-run knowledge accumulation and architectural evolution. Each run starts from the same initial knowledge, and later iterations largely optimize within an architecture fixed early in the search. We present ForgeKernel, a kernel-generation system that accumulates and reuses knowledge across runs while enabling continued evolution of kernel architectures. It analyzes the hardware behavior of a high-performance reference and distills validated architectural decisions into reusable knowledge of hardware mechanisms rather than imitating source code. It then seals a kernel that passes correctness and performance validation and uses it as the reference for the next optimization run, enabling architectural decisions to be revisited. Across 27 operators and 29 input configurations on NVIDIA Hopper and Blackwell GPUs, ForgeKernel matches or beats its expert reference on 25 configurations, with a geometric-mean performance gain of 10%. On Ascend 910C, it outperforms the per-operator public best on the public case set of all 8 level-4 operators in the CANN-Bench Season 1 final leaderboard, with a geometric-mean gain of 25.1%, while its full-suite submission ranks first on the A3 leaderboard. As of 2026-10-30, public submissions generated by ForgeKernel rank first on all three CANN-Bench 2.0 solution leaderboards (Ascend 910C, 950DT, and 950PR) and on the SOL-ExecBench FlashInfer-Bench track.

cs.DC↗

arXiv2026

JustGoal: With Only a Goal, the AI Baseline Is Above the Expert Kernels

A small number of GPU kernels often determines the efficiency of large language model training and inference. High-performance kernel development typically relies on expert knowledge through manual implementation, collaboration with general-purpose models, or specialized agents equipped with domain knowledge, evaluation methods, and search workflows. We investigate how much value this knowledge provides as foundation models become stronger. We introduce JustGoal, a goal-only baseline in which a general-purpose agent receives only the optimization objective and constraints, without kernel-specific infrastructure, knowledge bases, or predefined workflows, and builds its own evaluation loop. On NVIDIA B200 GPUs, JustGoal outperforms open-source libraries on 8 of 10 operators, achieving per-operator geometric mean speedups of 1.14x to 2.34x. It also surpasses the fastest publicly released specialized-agent kernel on 5 of 9 operators with available comparisons. In a controlled same-model comparison using Fable 5.1 on single shapes, the KDA-0.5 harness reaches 0.95x of JustGoal's performance, while single-mechanism ablations show no consistent benefit from domain knowledge or predefined workflows. These results indicate that specialized machinery should be justified by clear gains over a same-model, same-scope goal-only baseline and reevaluated whenever the foundation model changes.

cs.DC↗

arXiv2026

ForgeTrain: Forging Production-Grade Training Frameworks via Harness-Driven AI Development

Training large models still relies on general-purpose frameworks such as Megatron-LM, whose generality tax constrains scenario-specific optimization and adds runtime overhead through accumulated abstraction. AI code generation reduces the cost of building a framework, and makes it affordable to forge one per scenario. We propose Forge Engineering: building a dedicated implementation from scratch for each scenario and iteratively optimizing it toward peak performance under correctness and usability constraints. Dedicated implementations inherit no abstraction boundaries, so they can integrate optimizations across the stack and reach a higher performance ceiling. We instantiate this paradigm for training frameworks as ForgeTrain, which holds a trusted framework as a golden reference and relaxes equivalence monotonically from Bit-for-Bit to Surpass. Experiments across multiple model--hardware configurations show that ForgeTrain consistently produces correct training engines and improves MFU over established training frameworks by 4.7--33.2%. To our knowledge this is the first production-grade training framework forged end-to-end by AI to match or surpass its human reference.

cs.SE↗

arXiv2026

ForgeMegakernel: A General Framework for Efficient Auto-Regressive Model Decode Megakernels

Auto-regressive model decode is bandwidth-bound, since every weight and key/value-cache byte crosses high-bandwidth memory once per token. A megakernel is an ideal solution, but existing automatic megakernel generation approaches cannot achieve both generalization across models and correctness guarantees. We present ForgeMegakernel, which generates a per-model high-performance decode megakernel using coding agents. ForgeMegakernel pairs a universal knowledge base of ten progressive milestones with an independent mid-state test oracle. The milestones provide the megakernel's structural properties: a fine-grained instruction stream for each SM, dependency counters replacing the global synchronization, and a shared-memory buffer pool for workload balance across SMs and greater parallelism. The test oracle derives the mid-states of the megakernel and checks the performance, error and precision during the generation process, guaranteeing a correct and trustworthy forged megakernel. We evaluated ForgeMegakernel on 14 representative decoding operations across eight model families spanning 0.6B-13B parameters. The generated megakernels achieved 50.5-85.9% MBU and geometric mean speedups of 1.21x over SGLang 0.5.18 and 1.54x over a megakernel compiler under identical configurations. Inside SGLang, evaluated on GSM8K with ragged prompts, all 14 megakernels decoded faster than the SGLang engine at comparable answer accuracy.

cs.DC↗

arXiv2025

The Professional Challenges of Industrial Designer in Industry 4.0

The Industry 4.0 refers to a industrial ecology which will merge the information system, physical system and service system into an integrate platform. Since now the industrial designers either conceive the physical part of products, or design the User Interfaces of computer systems, the new industrial ecology will give them a chance to redefine their roles in R&D work-flow. In this paper we discussed the required qualities of industrial designer in the new era, according to an investigation among Chinese enterprises. Additionally, how to promote these qualities though educational program.

cs.HC↗