AI五小时发现MoE新算法，比人类算法快5倍，成本狂降26%

Core Insights - The article discusses the advancements in AI-driven algorithm creation, highlighting a system called ADRS (AI-Driven Research for Systems) developed by a research team at UC Berkeley, which can create new algorithms faster than human-designed ones by up to five times [1][2]. Group 1: Algorithm Efficiency - The ADRS framework, based on OpenEvolve, has demonstrated significant improvements in algorithm performance across various fields, achieving up to 5 times higher operational efficiency or a 26% reduction in costs [2]. - A new heuristic method was discovered through OpenEvolve, which replaces traditional linear search methods, resulting in a runtime reduction to 3.7 milliseconds, a fivefold improvement over previous implementations [12]. Group 2: Expert Parallelism Load Balancer (EPLB) - The EPLB algorithm aims to optimize load balancing among expert networks in large language models (LLMs) by dynamically adjusting the distribution of experts across GPUs, minimizing load imbalance and maximizing system throughput [6]. - The EPLB algorithm operates in three phases: distributing experts to balance load, creating replicas for hotspot experts, and assigning these replicas to GPUs [6]. - The research team evaluated existing methods, including a greedy "bin packing" strategy, which was slower and less efficient compared to the new EPLB approach [7]. Group 3: Research Team and Contributions - The research team includes notable members such as Audrey Cheng, Shu Liu, and Melissa Pan, who are focused on enhancing system performance through innovative scheduling algorithms and large-scale machine learning [14][16][17]. - The article also references a related development in AI, where a meta-learning algorithm was created to discover new reinforcement learning algorithms, indicating a broader trend of AI innovation in algorithm design [20][22].