Abstract:Porting deep learning algorithms to new hardware accelerators requires builders to repeatedly apply the same low-degree optimizations — quantization, reminiscence entry coalescing, tile size tuning, and architecture-specific workarounds — to each Triton kernel of their code-base. This guide, repetitive effort is a significant bottleneck: every kernel demands the same cycle of trial-and-error profiling in opposition to hardware constraints that range throughout units, but the underlying optimization patterns stay largely consistent. We current Xe-Forge, a multi-stage LLM-powered pipeline that automates this course of for Intel GPU. Given a functionally correct Triton kernel, the system applies as much as nine optimization phases — from algorithmic restructuring and operator fusion via block pointer modernization, GPU-particular tuning, and open-ended discovery — each pushed by a chain-of-Verification-and-Refinement (Cover) agent that generates candidates, validates them on actual hardware, and iterates on failures. A curated data base encodes Intel GPU constraints (power-of-two warp counts, GRF modes, SLM sizing) that are absent from LLM coaching knowledge, protecting the mannequin inside architecturally legitimate bounds. We consider Xe-Forge on ninety seven Level-2 KernelBench kernels and Flash Attention on the Intel Arc Pro B70, achieving a 1.17x geometric imply speedup over PyTorch eager with 67% of kernels improving, nine kernels exceeding 5x (as much as 82x), and 2–13.3x speedups on Flash Attention throughout all tested configurations with out regression — demonstrating that structured domain knowledge with hardware-in-the-loop verification can systematically eliminate the repetitive porting effort that currently gates algorithm deployment on new accelerators.
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Abstract:We present AlphaLab, an autonomous analysis harness that leverages frontier LLM agentic capabilities to automate the full experimental cycle in quantitative, computation-intensive domains. Given solely a dataset and a pure-language objective, AlphaLab proceeds by means of three phases without human intervention: (1) it adapts to the domain and explores the info, writing analysis code and producing a research report; (2) it constructs and adversarially validates its personal evaluation framework; and (3) it runs giant-scale GPU experiments by way of a Strategist/Worker loop, accumulating area information in a persistent playbook that functions as a type of on-line immediate optimization. All area-particular habits is factored into adapters generated by the mannequin itself, so the same pipeline handles qualitatively totally different tasks without modification. We evaluate AlphaLab with two frontier LLMs (GPT-5.2 and Claude Opus 4.6) on three domains: CUDA kernel optimization, where it writes GPU kernels that run 4.4x faster than this http URL on average (up to 91x); LLM pretraining, where the full system achieves 22% decrease validation loss than a single-shot baseline utilizing the identical model; and visitors forecasting, where it beats standard baselines by 23-25% after researching and implementing revealed mannequin households from the literature. The 2 models uncover qualitatively totally different solutions in each area (neither dominates uniformly), suggesting that multi-mannequin campaigns present complementary search protection. We moreover report results on financial time series forecasting in the appendix, and release all code at this https URL.
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