Solving Large-Scale Granular Resource Allocation Problems Efficiently

D. Narayanan, F. Kazhamiaka, F. Abuzaid, P. Kraft, A. Agrawal, S. Kandula, S. Boyd, M. Zaharia. Solving Large-Scale Granular Resource Allocation Problems Efficiently With POP, SOSP 2021.

Solving Flow Problems on Wide-Area Networks

F. Abuzaid, S. Kandula, B. Arzani, I. Menache, P. Bailis, M. Zaharia. Contracting Wide-area Network Topologies to Solve Flow Problems Quickly, NSDI 2021.

Machine Learning to detect Atrial Fibrillation

In this collaboration with Dr. Sanjiv Narayan’s Computational Arrhythmia Research Laboratory, we demonstrate that convolutional neural networks can identify potential treatment sites for atrial fibrillation, a common form of heart arrhythmia. In our evaluation, we show that CNNs can identify these sites with 95.0% accuracy.

M. Alhusseini, F. Abuzaid, A. Rogers, J. Zaman, T. Baykaner, P. Clopton, P. Bailis, M. Zaharia, P. Wang, W-J. Rappel, and S. Narayan. Machine Learning to Classify Intracardiac Electrical Patterns During Atrial Fibrillation, Circulation: Arrhythmia and Electrophysiology 2020.

Optimus + Maximus

In this paper, we show that blocked matrix multiply—a naive, hardware-optimized approach—surprisingly outperforms the state-of-the-art MIPS solvers by up to 12x for some (but not all) inputs. In response, we present a novel MIPS solution, Maximus, that takes advantage of hardware efficiency and pruning of the search space; we also introduce a new data-dependent optimizer, Optimus, that selects online with minimal overhead the best MIPS solver for a given set of inputs. Together, Optimus and Maximus outperform state-of-the-art MIPS solvers by 3.2x on average, and up to 10.9x, on widely studied MIPS datasets.

F. Abuzaid, G. Sethi, P. Bailis, and M. Zaharia. To Index or Not to Index: Optimizing Exact Maximum Inner Product Search, ICDE 2019.

MacroBase SQL

DIFF, a new SQL operator, provides a generalizable interface for finding explanations in large-scale datasets. Our implementation of DIFF in MacroBase SQL (a fork of MacroBase) outperforms other state-of-the-art explanation engines by up to an order of magnitude.

F. Abuzaid, P. Kraft, S. Suri, E. Gan, E. Xu, A. Shenoy, A. Ananthanarayan, J. Sheu, E. Meijer, X. Wu, J. Naughton, P. Bailis, and M. Zaharia. DIFF: A Relational Interface for Large-Scale Data Explanation, VLDB 2019.

MacroBase

MacroBase is a new a data analytics engine that prioritizes end-user attention in high-volume fast data streams. MacroBase enables efficient, accurate, and modular analyses that highlight and aggregate important and unusual behavior, acting as a search engine for fast data. MacroBase is able to deliver order-of-magnitude speedups over alternatives by optimizing the combination of explanation (i.e., feature selection) and classification tasks and by leveraging a new reservoir sampler and heavy-hitters sketch specialized for fast data streams. As a result, MacroBase delivers accurate results at speeds of up to 2M events per second per query on a single core. The system has delivered meaningful results in production, including at a telematics company monitoring hundreds of thousands of vehicles.

F. Abuzaid, P. Bailis, J. Ding, E. Gan, S. Madden, D. Narayanan, K. Rong, S. Suri (alphabetical). MacroBase: Prioritizing Attention in Fast Data, ACM Transactions on Database Systems (TODS) - Best of SIGMOND 2017 Papers.

Sparser

22x speed-ups for parsing JSON, Avro, and Parquet data.

S. Palkar, F. Abuzaid, P. Bailis, and M. Zaharia. Filter Before You Parse: Faster Analytics on Raw Data with Sparser, VLDB 2018.

NoScope

100x speedups for CNN evaluation on video streams.

D. Kang, J. Emmons, F. Abuzaid, P. Bailis, and M. Zaharia. NoScope: Optimizing Neural Network Queries over Video at Scale, VLDB 2017.

Yggdrasil

A project on training deep decision trees at scale. Compatible with Spark MLlib 1.6+.

F. Abuzaid, J. Bradley, F. Liang, A. Feng, L. Yang, M. Zaharia, and A. Talwalkar. Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale, NIPS 2016.