arXiv subjects

Ishwar Bhati

Publications and source records attributed to Ishwar Bhati.

3 recordsLinked to original sources

arXiv2024

Toward Optimal Search and Retrieval for RAG

Retrieval-augmented generation (RAG) is a promising method for addressing some of the memory-related challenges associated with Large Language Models (LLMs). Two separate systems form the RAG pipeline, the retriever and the reader, and the impact of each on downstream task performance is not well-understood. Here, we work towards the goal of understanding how retrievers can be optimized for RAG pipelines for common tasks such as Question Answering (QA). We conduct experiments focused on the relationship between retrieval and RAG performance on QA and attributed QA and unveil a number of insights useful to practitioners developing high-performance RAG pipelines. For example, lowering search accuracy has minor implications for RAG performance while potentially increasing retrieval speed and memory efficiency.

cs.CL↗

arXiv2023

Similarity search in the blink of an eye with compressed indices

Nowadays, data is represented by vectors. Retrieving those vectors, among millions and billions, that are similar to a given query is a ubiquitous problem, known as similarity search, of relevance for a wide range of applications. Graph-based indices are currently the best performing techniques for billion-scale similarity search. However, their random-access memory pattern presents challenges to realize their full potential. In this work, we present new techniques and systems for creating faster and smaller graph-based indices. To this end, we introduce a novel vector compression method, Locally-adaptive Vector Quantization (LVQ), that uses per-vector scaling and scalar quantization to improve search performance with fast similarity computations and a reduced effective bandwidth, while decreasing memory footprint and barely impacting accuracy. LVQ, when combined with a new high-performance computing system for graph-based similarity search, establishes the new state of the art in terms of performance and memory footprint. For billions of vectors, LVQ outcompetes the second-best alternatives: (1) in the low-memory regime, by up to 20.7x in throughput with up to a 3x memory footprint reduction, and (2) in the high-throughput regime by 5.8x with 1.4x less memory.

cs.LG↗

arXiv2018

MARS: Memory Aware Reordered Source

Memory bandwidth is critical in today's high performance computing systems. The bandwidth is particularly paramount for GPU workloads such as 3D Gaming, Imaging and Perceptual Computing, GPGPU due to their data-intensive nature. As the number of threads and data streams in the GPUs increases with each generation, along with a high available memory bandwidth, memory efficiency is also crucial in order to achieve desired performance. In presence of multiple concurrent data streams, the inherent locality in a single data stream is often lost as these streams are interleaved while moving through multiple levels of memory system. In DRAM based main memory, the poor request locality reduces row-buffer reuse resulting in underutilized and inefficient memory bandwidth. In this paper we propose Memory-Aware Reordered Source (\textit{MARS}) architecture to address memory inefficiency arising from highly interleaved data streams. The key idea of \textit{MARS} is that with a sufficiently large lookahead before the main memory, data streams can be reordered based on their row-buffer address to regain the lost locality and improve memory efficiency. We show that \textit{MARS} improves achieved memory bandwidth by 11\% for a set of synthetic microbenchmarks. Moreover, MARS does so without any specific knowledge of the memory configuration.

cs.PF↗