arXiv subjects

Xianglong Deng

Publications and source records attributed to Xianglong Deng.

6 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

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 Future of Fully Homomorphic Encryption System: from a Storage I/O Perspective

Fully Homomorphic Encryption (FHE) allows computations to be performed on encrypted data, significantly enhancing user privacy. However, the I/O challenges associated with deploying FHE applications remains understudied. We analyze the impact of storage I/O on the performance of FHE applications and summarize key lessons from the status quo. Key results include that storage I/O can degrade the performance of ASICs by as much as 357$\times$ and reduce GPUs performance by up to 22$\times$.

cs.CR↗

arXiv2024

Trinity: A General Purpose FHE Accelerator

In this paper, we present the first multi-modal FHE accelerator based on a unified architecture, which efficiently supports CKKS, TFHE, and their conversion scheme within a single accelerator. To achieve this goal, we first analyze the theoretical foundations of the aforementioned schemes and highlight their composition from a finite number of arithmetic kernels. Then, we investigate the challenges for efficiently supporting these kernels within a unified architecture, which include 1) concurrent support for NTT and FFT, 2) maintaining high hardware utilization across various polynomial lengths, and 3) ensuring consistent performance across diverse arithmetic kernels. To tackle these challenges, we propose a novel FHE accelerator named Trinity, which incorporates algorithm optimizations, hardware component reuse, and dynamic workload scheduling to enhance the acceleration of CKKS, TFHE, and their conversion scheme. By adaptive select the proper allocation of components for NTT and MAC, Trinity maintains high utilization across NTTs with various polynomial lengths and imbalanced arithmetic workloads. The experiment results show that, for the pure CKKS and TFHE workloads, the performance of our Trinity outperforms the state-of-the-art accelerator for CKKS (SHARP) and TFHE (Morphling) by 1.49x and 4.23x, respectively. Moreover, Trinity achieves 919.3x performance improvement for the FHE-conversion scheme over the CPU-based implementation. Notably, despite the performance improvement, the hardware overhead of Trinity is only 85% of the summed circuit areas of SHARP and Morphling.

cs.AR↗

arXiv2024

Taiyi: A high-performance CKKS accelerator for Practical Fully Homomorphic Encryption

Fully Homomorphic Encryption (FHE), a novel cryptographic theory enabling computation directly on ciphertext data, offers significant security benefits but is hampered by substantial performance overhead. In recent years, a series of accelerator designs have significantly enhanced the performance of FHE applications, bringing them closer to real-world applicability. However, these accelerators face challenges related to large on-chip memory and area. Additionally, FHE algorithms undergo rapid development, rendering the previous accelerator designs less perfectly adapted to the evolving landscape of optimized FHE applications. In this paper, we conducted a detailed analysis of existing applications with the new FHE method, making two key observations: 1) the bottleneck of FHE applications shifts from NTT to the inner-product operation, and 2) the optimal α of KeySwitch changes with the decrease in multiplicative level. Based on these observations, we designed an accelerator named Taiyi, which includes specific hardware for the inner-product operation and optimizes the NTT and BConv operations through algorithmic derivation. A comparative evaluation of Taiyi against previous state-of-the-art designs reveals an average performance improvement of 1.5x and reduces the area overhead by 15.7%.

cs.CR↗