Bullion: A Column Store for Machine Learning

• URL: http://arxiv.org/abs/2404.08901v3
• Date: Tue, 19 Nov 2024 07:04:06 GMT
• Title: Bullion: A Column Store for Machine Learning
• Authors: Gang Liao, Ye Liu, Jianjun Chen, Daniel J. Abadi,
• Abstract summary: This paper presents Bullion, a columnar storage system tailored for machine learning workloads. Bundy addresses the complexities of data compliance, optimize the encoding of long sequence sparse features, efficiently manages wide-table projections, introduces feature quantization in storage, and provides a comprehensive cascading encoding framework. Preliminary experimental results and theoretical analysis demonstrate Bullion's improved ability to deliver strong performance in the face of the unique demands of machine learning workloads.
• Score: 4.096087402737292
• License:
• Abstract: The past two decades have witnessed significant success in applying columnar storage to data warehousing and analytics. However, the rapid growth of machine learning poses new challenges. This paper presents Bullion, a columnar storage system tailored for machine learning workloads. Bullion addresses the complexities of data compliance, optimizes the encoding of long sequence sparse features, efficiently manages wide-table projections, introduces feature quantization in storage, enables quality-aware sequential reads for multimodal training data, and provides a comprehensive cascading encoding framework that unifies diverse encoding schemes through modular, composable interfaces. By aligning with the evolving requirements of ML applications, Bullion facilitates the application of columnar storage and processing to modern application scenarios such as those within advertising, recommendation systems, and Generative AI. Preliminary experimental results and theoretical analysis demonstrate Bullion's improved ability to deliver strong performance in the face of the unique demands of machine learning workloads compared to existing columnar storage solutions. Bullion significantly reduces I/O costs for deletion compliance, achieves substantial storage savings with its optimized encoding scheme for sparse features, and improves metadata parsing speed for wide-table projections. These advancements enable Bullion to become an important component in the future of machine learning infrastructure, enabling organizations to efficiently manage and process the massive volumes of data required for training and inference in modern AI applications.

Related papers

• Reprogramming Foundational Large Language Models(LLMs) for Enterprise Adoption for Spatio-Temporal Forecasting Applications: Unveiling a New Era in Copilot-Guided Cross-Modal Time Series Representation Learning [0.0]
  patio-temporal forecasting plays a crucial role in various sectors such as transportation systems, logistics, and supply chain management. We introduce a hybrid approach that combines the strengths of open-source large and small-scale language models (LLMs and LMs) with traditional forecasting methods.
  arXiv  Detail & Related papers  (2024-08-26T16:11:53Z)
• MaxMind: A Memory Loop Network to Enhance Software Productivity based on Large Language Models [13.839564855350295]
  This paper addresses the importance of converting real-time task experiences into system memory. We show that the accumulation and recycling of task memories lead to a steady enhancement in task success rate. The inclusion of memory recycling can also boost the system's task execution efficiency by up to 25%.
  arXiv  Detail & Related papers  (2024-08-07T15:27:22Z)
• Online Adaptation of Language Models with a Memory of Amortized Contexts [82.02369596879817]
  Memory of Amortized Contexts (MAC) is an efficient and effective online adaptation framework for large language models. We show how MAC can be combined with and improve the performance of popular alternatives such as retrieval augmented generations.
  arXiv  Detail & Related papers  (2024-03-07T08:34:57Z)
• Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes [57.62036621319563]
  We introduce CLLM, which leverages the prior knowledge of Large Language Models (LLMs) for data augmentation in the low-data regime. We demonstrate the superior performance of CLLM in the low-data regime compared to conventional generators.
  arXiv  Detail & Related papers  (2023-12-19T12:34:46Z)
• In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD [51.04126395480625]
  Recent years have seen many successful applications of machine learning (ML) to facilitate fluid dynamic computations. As simulations grow, generating new training datasets for traditional offline learning creates I/O and storage bottlenecks. This work offers a solution by simplifying this coupling and enabling in situ training and inference on heterogeneous clusters.
  arXiv  Detail & Related papers  (2023-06-22T14:07:54Z)
• Performance Embeddings: A Similarity-based Approach to Automatic Performance Optimization [71.69092462147292]
  Performance embeddings enable knowledge transfer of performance tuning between applications. We demonstrate this transfer tuning approach on case studies in deep neural networks, dense and sparse linear algebra compositions, and numerical weather prediction stencils.
  arXiv  Detail & Related papers  (2023-03-14T15:51:35Z)
• Retrieval-Enhanced Machine Learning [110.5237983180089]
  We describe a generic retrieval-enhanced machine learning framework, which includes a number of existing models as special cases. REML challenges information retrieval conventions, presenting opportunities for novel advances in core areas, including optimization. REML research agenda lays a foundation for a new style of information access research and paves a path towards advancing machine learning and artificial intelligence.
  arXiv  Detail & Related papers  (2022-05-02T21:42:45Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials [4.221095652322005]
  Topology optimization (TO) is a popular and powerful computational approach for designing novel structures, materials, and devices. To address these issues, we propose a general scalable deep-learning (DL) based TO framework, referred to as SDL-TO. Our framework accelerates TO by learning the iterative history data and simultaneously training on the mapping between the given design and its gradient.
  arXiv  Detail & Related papers  (2020-11-28T17:38:31Z)
• Hardware Acceleration of Sparse and Irregular Tensor Computations of ML Models: A Survey and Insights [18.04657939198617]
  This paper provides a comprehensive survey on the efficient execution of sparse and irregular tensor computations of machine learning models on hardware accelerators. It analyzes different hardware designs and acceleration techniques and analyzes them in terms of hardware and execution costs. The takeaways from this paper include: understanding the key challenges in accelerating sparse, irregular-shaped, and quantized tensors.
  arXiv  Detail & Related papers  (2020-07-02T04:08:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.