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Google Research Google AI Learn about all our AI Google DeepMind Explore the frontier of AI Google Labs Try our AI experiments Research Resources Conferences & events Careers Blog About Search Home People Laxman Dhulipala I am research scientist at Google Research with the Graph Mining team and also an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park. I am broadly interested in efficient parallel algorithms, e.g., for parallel clustering and parallel graph processing. I am also interested in models of parallel computation motivated by emerging hardware and exploring these models theoretically and practically. Research Areas Algorithms and theory Authored Publications See Filters results Filter by: Clear Publications Google 6 Other 0 Years 2024 3 2023 1 2022 1 2020 1 Research Areas Algorithms and Theory 3 Distributed Systems and Parallel Computing 2 Teams Sort By Title Title, descending Year Year, descending chip template Remove MUVERA: Multi-Vector Retrieval via Fixed Dimensional Encodings Rajesh Jayaram Laxman Dhulipala Majid Hadian Jason Lee Vahab Mirrokni NeurIPS 2024 (2024) Preview Preview abstract Neural embedding models have become a fundamental component of modern information retrieval (IR) pipelines. These models produce a single embedding x ∈ R^d per data-point, allowing for fast retrieval via highly optimized maximum inner product search (MIPS) algorithms. Recently, beginning with the landmark ColBERT paper, multi-vector models, which produce a set of embedding per data point, have achieved markedly superior performance for IR tasks. Unfortunately, using these models for IR is computationally expensive due to the increased complexity of multi-vector retrieval and scoring. In this paper, we introduce MUVERA (Multi-Vector Retrieval Algorithm), a retrieval mechanism which reduces multi-vector similarity search to single-vector similarity search. This enables the usage of off-the-shelf MIPS solvers for multi-vector retrieval. MUVERA asymmetrically generates Fixed Dimensional Encodings (FDEs) of queries and documents, which are vectors whose inner product approximates multi-vector similarity. We prove that FDEs give high-quality ε-approximations, thus providing the first single-vector proxy for multi-vector similarity with theoretical guarantees. Empirically, we find that FDEs achieve the same recall as prior state-of-the-art heuristics while retrieving 2-5× fewer candidates. Compared to prior state of the art implementations, MUVERA achieves consistently good end-to-end recall and latency across a diverse set of the BEIR retrieval datasets, achieving an average of 10% improved recall with 90% lower latency. View details MUVERA: Simple and Effective Multi-Vector Retrieval via Fixed Dimensional Encodings Rajesh Jayaram Vahab Mirrokni Laxman Dhulipala Jason Lee Majid Hadian 2024 Preview Preview abstract Neural embedding models have become a fundamental component of modern information retrieval (IR) pipelines. These models produce a single embedding $x \in \mathbb{R}^d$ per data-point, allowing for fast retrieval via highly optimized maximum inner product search (MIPS) algorithms. Recently, beginning with the landmark ColBERT paper, multi-vector models, which produce a set of embedding per data point, have achieved markedly superior performance for IR tasks. Unfortunately, using these models for IR is computationally expensive due to the increased complexity of multi-vector retrieval and scoring. In this paper, we introduce MUVERA (Multi-Vector Retrieval Algorithm), a retrieval mechanism which reduces multi-vector similarity search to single-vector similarity search. This enables the usage of off-the-shelf MIPS solvers for multi-vector retrieval. MUVERA asymmetrically generates Fixed Dimensional Encodings (FDEs) of queries and documents, which are vectors whose inner product approximates multi-vector similarity. We prove that FDEs give high-quality $1+\epsilon$-approximations, thus providing the first single-vector proxy for multi-vector similarity with theoretical guarantees. Empirically, we find that FDEs achieve the same recall as prior state-of-the-art heuristics while retrieving 2-5x fewer candidates. Compared to prior state of the art implementations, MUVERA achieves consistently good end-to-end recall and latency across a diverse set of the BEIR retrieval datasets, achieving an average of 10% improved recall with 90% lower latency. View details It’s Hard to HAC Average Linkage! Rajesh Jayaram Mohammad "Hossein" Bateni Laxman Dhulipala Kishen Gowda David Ellis Hershkowitz Jakub Łącki 2024 Preview Preview abstract Average linkage Hierarchical Agglomerative Clustering (HAC) is an extensively studied and applied method for hierarchical clustering. Recent applications to massive datasets have driven significant interest in near-linear-time and efficient parallel algorithms for average linkage HAC. We provide hardness results that rule out such algorithms. On the sequential side, we establish a runtime lower bound of n^{3/2−ϵ} on n node graphs for sequential combinatorial algorithms under standard fine-grained complexity assumptions. This essentially matches the best-known running time for average linkage HAC. On the parallel side, we prove that average linkage HAC likely cannot be parallelized even on simple graphs by showing that it is CC-hard on trees of diameter 4. On the possibility side, we demonstrate that average linkage HAC can be efficiently parallelized (i.e., it is in NC) on paths and can be solved in near-linear time when the height of the output cluster hierarchy is small. View details TeraHAC: Hierarchical Agglomerative Clustering of Trillion-Edge Graphs Jason Lee Jakub Łącki Laxman Dhulipala Vahab Mirrokni SIGMOD'24 (2023) Preview Preview abstract We introduceTeraHAC, a (1+epsilon)-approximate hierarchical agglomerative clustering (HAC) algorithm whichs cales to trillion-edge graphs. Our algorithm is based on a new approach to computing (1+epsilon)-approximate HAC, which is a novel combination of the nearest-neighbor chain algorithm and the notion of (1+epsilon)-approximate HAC. Our approach allows us to partition the graph among multiple machines and make significant progress in computing the clustering within each partition before any communication with other partitions is needed.We evaluate TeraHAC on a number of real-world and synthetic graphs of up to 8 trillion edges. We show that TeraHAC requires over 100x fewer rounds compared to previously known approaches for computing HAC. It is up to 8.3x faster than SCC, the state-of-the-art distributed algorithm for hierarchical clustering, while achieving 1.16x higher quality. In fact, TeraHAC essentially retains the quality of the celebrated HAC algorithm while significantly improving the running time. View details ParHAC: Hierarchical Agglomerative Graph Clustering in Poly-Logarithmic Depth David Eisenstat Jessica Shi Jakub Łącki Laxman Dhulipala Vahab Mirrokni NeurIPS'22 (2022) Preview Preview abstract Obtaining scalable algorithms for hierarchical agglomerative clustering (HAC) is of significant interest due to the massive size of real-world datasets. At the same time, efficiently parallelizing HAC is difficult due to the seemingly sequential nature of the algorithm. In this paper, we address this issue and present ParHAC, the first efficient parallel HAC algorithm with sublinear depth for the widely-used average-linkage function. In particular, we provide a (1+ϵ)-approximation algorithm for this problem on m edge graphs using O(m polylog m) work and poly-logarithmic depth. Moreover, we show that obtaining similar bounds for exact average-linkage HAC is not possible under standard complexity-theoretic assumptions. We complement our theoretical results with a comprehensive study of the ParHAC algorithm in terms of its scalability, performance, and quality, and compare with several state-of-the-art sequential and parallel baselines. On a broad set of large publicly-available real-world datasets, we find that ParHAC obtains a 50.1x speedup on average over the best sequential baseline, while achieving quality similar to the exact HAC algorithm. We also show that ParHAC can cluster one of the largest publicly available graph datasets with 124 billion edges in a little over three hours using a commodity multicore machine. View details Parallel Graph Algorithms in Constant Adaptive Rounds: Theory meets Practice Hossein Esfandiari Jakub Łącki Laxman Dhulipala Soheil Behnezhad Vahab Mirrokni Warren J Schudy VLDB 2020 Preview Preview abstract We study fundamental graph problems such as graph connectivity, minimum spanning forest (MSF), and approximate maximum (weight) matching in a distributed setting. In particular, we focus on the Adaptive Massively Parallel Computation (AMPC) model, which is a theoretical model that captures MapReduce-like computation augmented with a distributed hash table. We show the first AMPC algorithms for all of the studied problems that run in a constant number of rounds and use only O(n^ϵ) space per machine, where 0 View details Search on Google Scholar Join us We're always looking for more talented, passionate people. 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