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Found 13 papers, 3 papers with code
Accelerating Mixture-of-Experts Training with Adaptive Expert Replication
The key insight of SwiftMoE is to decouple the placement of expert parameters from their large optimizer state.
AI Metropolis: Scaling Large Language Model-based Multi-Agent Simulation with Out-of-order Execution
With more advanced natural language understanding and reasoning capabilities, large language model (LLM)-powered agents are increasingly developed in simulated environments to perform complex tasks, interact with other agents, and exhibit emergent behaviors relevant to social science and gaming.
Cloud Atlas: Efficient Fault Localization for Cloud Systems using Language Models and Causal Insight
We evaluate Atlas across a range of fault localization scenarios and demonstrate that Atlas is capable of generating causal graphs in a scalable and generalizable manner, with performance that far surpasses that of data-driven algorithms and is commensurate to the ground-truth baseline.
ReCycle: Resilient Training of Large DNNs using Pipeline Adaptation
We describe a prototype for ReCycle and show that it achieves high training throughput under multiple failures, outperforming recent proposals for fault-tolerant training such as Oobleck and Bamboo by up to $1. 46\times$ and $1. 64\times$, respectively.
cedar: Optimized and Unified Machine Learning Input Data Pipelines
Across eight pipelines, cedar improves performance by up to 1. 87x to 10. 65x compared to state-of-the-art input data systems.
SGLang: Efficient Execution of Structured Language Model Programs
SGLang consists of a frontend language and a runtime.
FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Models
Sequence-based deep learning recommendation models (DLRMs) are an emerging class of DLRMs showing great improvements over their prior sum-pooling based counterparts at capturing users' long term interests.
RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure
RecD addresses immense storage, preprocessing, and training overheads caused by feature duplication inherent in industry-scale DLRM training datasets.
RecShard: Statistical Feature-Based Memory Optimization for Industry-Scale Neural Recommendation
EMBs exhibit distinct memory characteristics, providing performance optimization opportunities for intelligent EMB partitioning and placement across a tiered memory hierarchy.
Understanding Data Storage and Ingestion for Large-Scale Deep Recommendation Model Training
Innovations that improve the efficiency and performance of DSI systems and hardware are urgent, demanding a deep understanding of DSI characteristics and infrastructure at scale.
INFaaS: A Model-less and Managed Inference Serving System
This paper introduces INFaaS, a managed and model-less system for distributed inference serving, where developers simply specify the performance and accuracy requirements for their applications without needing to specify a specific model-variant for each query.
DNN Dataflow Choice Is Overrated
Many DNN accelerators have been proposed and built using different microarchitectures and program mappings.
Distributed, Parallel, and Cluster Computing
Learning Memory Access Patterns
In this paper, we demonstrate the potential of deep learning to address the von Neumann bottleneck of memory performance.
