Large language models (LLMs), such as GPT-4, are revolutionizing software's ability to understand, process, and synthesize language. The authors of this paper believe that this advance in technology is significant enough to prompt introspection in the data management community, similar to previous technological disruptions such as the advents of the world wide web, cloud computing, and statistical machine learning. We argue that the disruptive influence that LLMs will have on data management will come from two angles. (1) A number of hard database problems, namely, entity resolution, schema matching, data discovery, and query synthesis, hit a ceiling of automation because the system does not fully understand the semantics of the underlying data. Based on large training corpora of natural language, structured data, and code, LLMs have an unprecedented ability to ground database tuples, schemas, and queries in real-world concepts. We will provide examples of how LLMs may completely change our approaches to these problems. (2) LLMs blur the line between predictive models and information retrieval systems with their ability to answer questions. We will present examples showing how large databases and information retrieval systems have complementary functionality. 

How Large Language Models Will Disrupt Data Management

Holographic displays promise to deliver unprecedented display capabilities in augmented reality applications, featuring a wide field of view, wide color gamut, spatial resolution, and depth cues all in a compact form factor. While emerging holographic display approaches have been successful in achieving large étendue and high image quality as seen by a camera, the large étendue also reveals a problem that makes existing displays impractical: the sampling of the holographic field by the eye pupil. Existing methods have not investigated this issue due to the lack of displays with large enough étendue, and, as such, they suffer from severe artifacts with varying eye pupil size and location. We show that the holographic field as sampled by the eye pupil is highly varying for existing display setups, and we propose pupil-aware holography that maximizes the perceptual image quality irrespective of the size, location, and orientation of the eye pupil in a near-eye holographic display. We validate the proposed approach both in simulations and on a prototype holographic display and show that our method eliminates severe artifacts and significantly outperforms existing approaches.

Pupil-Aware Holography

The Sia scheduler efficiently assigns heterogeneous deep learning (DL) cluster resources to elastic resource-adaptive jobs. Although some recent schedulers address one aspect or another (e.g., heterogeneity or resource-adaptivity), none addresses all and most scale poorly to large clusters and/or heavy workloads even without the full complexity of the combined scheduling problem. Sia introduces a new scheduling formulation that can scale to the search-space sizes and intentionally match jobs and their configurations to GPU types and counts, while adapting to changes in cluster load and job mix over time. Sia also introduces a low-profiling-overhead approach to bootstrapping (for each new job) throughput models used to evaluate possible resource assignments, and it is the first cluster scheduler to support elastic scaling of hybrid parallel jobs. 

Sia: Heterogeneity-aware, goodput-optimized ML-cluster scheduling


 The increasing size of both deep learning models and training data necessitates the ability to scale out model training through pipeline-parallel training, which combines pipelined model parallelism and data parallelism. However, most of them assume an ideal homogeneous dedicated cluster. As for real cloud clusters, these approaches suffer from the intensive model synchronization overheads due to the dynamic environment heterogeneity. Such a huge challenge leaves the design in a dilemma: either the performance bottleneck of the central parameter server (PS) or severe performance degradation caused by stragglers for decentralized synchronization (like All-Reduce). This approach presents SDPipe, a new
 semi-decentralized
 framework to get the best of both worlds, achieving both high heterogeneity tolerance and convergence efficiency in pipeline-parallel training. To provide high performance, we decentralize the communication model synchronization, which accounts for the largest proportion of synchronization overhead. In contrast, we centralize the process of group scheduling, which is lightweight but needs a global view for better performance and convergence speed against heterogeneity. We show via a prototype implementation the significant advantage of SDPipe on performance and scalability, facing different environments.


SDPipe: A Semi-Decentralized Framework for Heterogeneity-aware Pipeline-parallel Training



Generative language models have witnessed substantial traction, notably with the introduction of refined models aimed at more coherent user-AI interactions—principally conversational models. The epitome of this public attention has arguably been the refinement of the GPT-3 model into ChatGPT and its subsequent integration with auxiliary capabilities such as search features in Microsoft Bing. Despite voluminous prior research devoted to its developmental trajectory, the model’s performance, and applicability to a myriad of quotidian tasks remained nebulous and task specific. In terms of technological implementation, the advent of models such as LLMv2 and ChatGPT-4 has elevated the discourse beyond mere textual coherence to nuanced contextual understanding and real-world task completion. Concurrently, emerging architectures that focus on interpreting latent spaces have offered more granular control over text generation, thereby amplifying the model’s applicability across various verticals. Within the purview of cyber defense, especially in the Swiss operational ecosystem, these models pose both unprecedented opportunities and challenges. Their capabilities in data analytics, intrusion detection, and even misinformation combatting is laudable; yet the ethical and security implications concerning data privacy, surveillance, and potential misuse warrant judicious scrutiny.


Generative Artificial Intelligence: Fundamentals

Transformer models have achieved state-of-the-art performance on various domains of applications and gradually becomes the foundations of the advanced large deep learning (DL) models. However, how to train these models over multiple GPUs efficiently is still challenging due to a large number of parallelism choices. Existing DL systems either rely on manual efforts to make distributed training plans or apply parallelism combinations within a very limited search space. In this approach, we propose Galvatron, a new system framework that incorporates multiple popular parallelism dimensions and automatically finds the most efficient hybrid parallelism strategy. To better explore such a rarely huge search space, we 1) involve a decision tree to make decomposition and pruning based on some reasonable intuitions, and then 2) design a dynamic programming search algorithm to generate the optimal plan. Evaluations on four representative Transformer workloads show that Galvatron could perform automatically distributed training with different GPU memory budgets. Among all evluated scenarios, Galvatron always achieves superior system throughput compared to previous work with limited parallelism.

/pdf/galvatron-efficient-transformer-training-over-multiple-gpus-1ec9csxu.pdf

Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

Recent deep learning approaches have shown remarkable promise to enable high fidelity holographic displays. However, lightweight wearable display devices cannot afford the computation demand and energy consumption for hologram generation due to the limited onboard compute capability and battery life. On the other hand, if the computation is conducted entirely remotely on a cloud server, transmitting lossless hologram data is not only challenging but also result in prohibitively high latency and storage. In this work, by distributing the computation and optimizing the transmission, we propose the first framework that jointly generates and compresses high-quality phase-only holograms. Specifically, our framework asymmetrically separates the hologram generation process into high-compute remote encoding (on the server), and low-compute decoding (on the edge) stages. Our encoding enables light weight latent space data, thus faster and efficient transmission to the edge device. With our framework, we observed a reduction of 76% computation and consequently 83% in energy cost on edge devices, compared to the existing hologram generation methods. Our framework is robust to transmission and decoding errors, and approach high image fidelity for as low as 2 bits-per-pixel, and further reduced average bit-rates and decoding time for holographic videos.

Joint neural phase retrieval and compression for energy- and computation-efficient holography on the edge

Transformer models have achieved state-of-the-art performance on various domains of applications and gradually becomes the foundations of the advanced large deep learning (DL) models. However, how to train these models over multiple GPUs efficiently is still challenging due to a large number of parallelism choices. Existing DL systems either rely on manual efforts to make distributed training plans or apply parallelism combinations within a very limited search space. In this approach, we propose Galvatron, a new system framework that incorporates multiple popular parallelism dimensions and automatically finds the most efficient hybrid parallelism strategy. To better explore such a rarely huge search space, we 1) involve a decision tree to make decomposition and pruning based on some reasonable intuitions, and then 2) design a dynamic programming search algorithm to generate the optimal plan. Evaluations on four representative Transformer workloads show that Galvatron could perform automatically distributed training with different GPU memory budgets. Among all evaluated scenarios, Galvatron always achieves superior system throughput compared to previous work with limited parallelism.

Galvatron

FlexSP: Accelerating Large Language Model Training via Flexible Sequence Parallelism

Transformer models have emerged as the leading approach for achieving state-of-the-art performance across various application domains, serving as the foundation for advanced large-scale deep learning (DL) models. However, efficiently training these models across multiple GPUs remains a complex challenge due to the abundance of parallelism options. Existing DL systems either require manual efforts to design distributed training plans or limit parallelism combinations to a constrained search space. In this paper, we present Galvatron-BMW, a novel system framework that integrates multiple prevalent parallelism dimensions and automatically identifies the most efficient hybrid parallelism strategy. To effectively navigate this vast search space, we employ a decision tree approach for decomposition and pruning based on intuitive insights. We further utilize a dynamic programming search algorithm to derive the optimal plan. Moreover, to improve resource utilization and enhance system efficiency, we propose a bi-objective optimization workflow that focuses on workload balance. Our evaluations on different Transformer models demonstrate the capabilities of Galvatron-BMW in automating distributed training under varying GPU memory constraints. Across all tested scenarios, Galvatron-BMW consistently achieves superior system throughput, surpassing previous approaches that rely on limited parallelism strategies.

pdf/improving-automatic-parallel-training-via-balanced-memory-3vjyx69w.pdf

Improving Automatic Parallel Training via Balanced Memory Workload Optimization

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