Subrata Mitra

Retrieval-Augmented Generation (RAG) is often used with Large Language Models (LLMs) to infuse domain knowledge or user-specific information. In RAG, given a user query, a retriever extracts chunks of relevant text from a knowledge base. These chunks are sent to an LLM as part of the input prompt. Typically, any given chunk is repeatedly retrieved across user questions. However, currently, for every question, attention layers in LLMs fully compute the Keys and Values (KVs) repeatedly for the input chunks, as state-of-the-art methods cannot reuse KV-caches when chunks appear at arbitrary locations or with arbitrary contexts. Naive reuse leads to output quality degradation. This leads to potentially redundant computations on expensive GPUs and increases latency. In this work, we propose Cache-Craft , a system for managing and reusing precomputed KVs corresponding to the text chunks (which we call chunk-caches ) in RAG-based systems. We present how to identify chunk-caches that are reusable, how to efficiently perform a small fraction of recomputation to fix the cache and maintain output quality, and how to efficiently store and evict chunk-caches in the hardware for maximizing reuse while masking any overheads. With real production workloads as well as synthetic datasets, we show that Cache-Craft reduces redundant computation by 51% over SOTA prefix-caching and 75% over full recomputation. Additionally, with continuous batching on a real production workload, we get a 1.6× speed up in throughput for both the LLama-3-8B and 70B models and a 2.1× and 2× reduction in end-to-end response latency respectively, compared to prefix-caching, while maintaining generation quality.

Interactive visualization interfaces enable users to efficiently explore, analyze, and make sense of their datasets. However, as data grows in size, it becomes increasingly challenging to build data interfaces that meet the interface designer's desired latency expectations and resource constraints. Cloud DBMSs, while optimized for scalability, often fail to meet latency expectations, necessitating complex, bespoke query execution and optimization techniques for data interfaces. This involves manually navigating a huge optimization space that is sensitive to interface design and resource constraints, such as client vs server data and compute placement, choosing which computations are done offline vs online, and selecting from a large library of visualization-optimized data structures. This paper advocates for a Physical Visualization Design (PVD) tool that decouples interface design from system design to provide design independence. Given an interfaces underlying data flow, interactions with latency expectations, and resource constraints, PVD checks if the interface is feasible and, if so, proposes and instantiates a middleware architecture spanning the client, server, and cloud DBMS that meets the expectations. To this end, this paper presents Jade, the first prototype PVD tool that enables design independence. Jade proposes an intermediate representation called Diffplans to represent the data flows, develops cost estimation models that trade off between latency guarantees and plan feasibility, and implements an optimization framework to search for the middleware architecture that meets the guarantees. We evaluate Jade on six representative data interfaces as compared to Mosaic and Azure SQL database. We find Jade supports a wider range of interfaces, makes better use of available resources, and can meet a wider range of data, latency, and resource conditions.

Enterprise data lakes often suffer from substantial amounts of duplicate and redundant data, with data volumes ranging from terabytes to petabytes. This leads to both increased storage costs and unnecessarily high maintenance costs for these datasets. In this work, we focus on identifying and reducing redundancy in enterprise data lakes by addressing the problem of "dataset containment". To the best of our knowledge, this is one of the first works that addresses table-level containment at a large scale. We propose R2D2: a three-step hierarchical pipeline that efficiently identifies almost all instances of containment by progressively reducing the search space in the data lake. It first builds (i) a schema containment graph, followed by (ii) statistical min-max pruning, and finally, (iii) content level pruning. We further propose minimizing the total storage and access costs by optimally identifying redundant datasets that can be deleted (and reconstructed on demand) while respecting latency constraints. We implement our system on Azure Databricks clusters using Apache Spark for enterprise data stored in ADLS Gen2, and on AWS clusters for open-source data. In contrast to existing modified baselines that are inaccurate or take several days to run, our pipeline can process an enterprise customer data lake at the TB scale in approximately 5 hours with high accuracy. We present theoretical results as well as extensive empirical validation on both enterprise (scale of TBs) and open-source datasets (scale of MBs - GBs), which showcase the effectiveness of our pipeline.

In recent years, deep learning models have revolutionized computer vision, enabling diverse applications. However, these models are computationally expensive, and leveraging them for video analytics involves low-level imperative programming. To address these efficiency and usability challenges, the database community has developed video database management systems (VDBMSs). However, existing VDBMSs lack extensibility and composability and do not support holistic system optimizations, limiting their practical application. In response to these issues, we present our vision for EVA, a VDBMS that allows for extensible support of user-defined functions and employs a Cascades-style query optimizer. Additionally, we leverage Ray's distributed execution to enhance scalability and performance and explore hardware-specific optimizations to facilitate runtime optimizations. We discuss the architecture and design of EVA, our achievements thus far, and our research roadmap.