Publications
Breaking Locality Accelerates Block Gauss-Seidel
We analyze the accelerated block Gauss-Seidel algorithm in the random coordinate sampling setting...
International Conference on Machine Learning (ICML 2017), 06/22/2017
https://arxiv.org/abs/1701.03863
Author(s): Shivaram Venkataraman, Ashia C. Wilson, Alex Gittens, Michael Jordan, Benjamin Recht
Real-Time Machine Learning: The Missing Pieces
Machine learning applications are increasingly deployed not only to serve predictions using static models, but also as tightly-integrated components of feedback loops involving dynamic, real-time decision...
HotOS 2017, 05/10/2017
https://arxiv.org/abs/1703.03924
Author(s): Robert Nishihara, Philipp Moritz, Stephanie Wang, Alexey Tumanov, William Paul, Johann Schleier-Smith, Richard Liaw, Mehrdad Niknami, Michael Jordan, Ion Stoica
ZipG: A Memory-efficient Graph Store for Interactive Queries
ZipG is a distributed memory-efficient graph store for serving interactive graph queries....
SIGMOD '17, 05/14/2017
https://people.eecs.berkeley.edu/~anuragk/papers/zipg.pdf
Author(s): Anurag Khandelwal, Zongheng Yang, Evan Ye, Rachit Agarwal, Ion Stoica
Sub-microsecond Adaptive Voltage Scaling in a 28nm FD-SOI Processor
This work presents a RISC-V system-on-chip (SoC) with integrated voltage regulation and power management implemented in 28nm FD-SOI. A fully integrated switched-capacitor DC-DC converter, coupled with an...
ESSCIRC - ESSDERC 2016, 09/15/2016
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7598294
Author(s): Krste Asanovic, Benjamin Keller, Martin Cochet, Brian Zimmer, Jaehwa Kwak, Alberto Puggelli, Steven Bailey, Borivoje Nikolic, Palmer Dabbelt, et al.
Vector Processors for Energy-Efficient Embedded Systems
High-performance embedded processors are frequently designed as arrays of small, in-order scalar cores, even when their workloads exhibit high degrees of data-level parallelism (DLP). We show that these...
ISCA 2016, 06/15/2016
https://people.eecs.berkeley.edu/~krste/papers/dabbelt-mes2016.pdf
Author(s): Krste Asanovic, Daniel Dabbelt, Colin Schmidt, Eric Love, Howard Mao, Sagar Karandikar
The Renewed Case for the Reduced Instruction Set Computer: Avoiding ISA Bloat with Macro-Op Fusion for RISC-V
This report makes the case that a well-designed Reduced Instruction Set Computer (RISC) can match, and even exceed, the performance and code density of existing commercial Complex Instruction Set Computers (CISC...
Technical Report, 07/15/2016
https://people.eecs.berkeley.edu/~krste/papers/EECS-2016-130.pdf
Author(s): Krste Asanovic, Christopher Celio, Daniel Dabbelt, David A. Patterson
Strober: Fast and Accurate Sample-Based Energy Simulation for Arbitrary RTL
This paper presents a sample-based energy simulation methodology that enables fast and accurate estimations of performance and average power for arbitrary RTL designs. Our approach uses an FPGA to simultaneously...
ISCA 2016, 06/15/2016
https://people.eecs.berkeley.edu/~krste/papers/strober-isca16.pdf
Author(s): Krste Asanovic, Donggyu Kim, Adam Izraelevitz, Christopher Celio, Hokeum Kim, Brian Zimmer
Reprogrammable Redundancy for Cache Vmin Reduction in a 28nm RISC-V Processor
The presented processor lowers SRAM-based cache Vmin by using three architectural techniques–bit bypass (BB), dynamic column redundancy (DCR), and line disable (LD)–that use low-overhead reprogrammable...
A-SSCC’16, 11/15/2016
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7844150
Author(s): Krste Asanovic, B. Zimmer, P. F. Chiu, Bora Nikolic
High Performance Transactions via Early Write Visibility
In order to guarantee recoverable transaction execution, database systems permit a transaction’s writes to be observable only at the end of its execution. As a consequence, there is generally a delay between t...
PVLDB, 01/13/2017
http://www.vldb.org/pvldb/vol10/p613-faleiro.pdf
Author(s): Joe Hellerstein, Jose M. Faleiro, Daniel J. Abadi
Ground: A Data Context Service
Ground is an open-source data context service , a system to manage all the information that informs the use of data. Data usage has changed both philosophically and practically in the last decade, creating an...
CIDR 2017, 8th Biennial Conference on Innovative Data Systems Research, 01/09/2017
http://cidrdb.org/cidr2017/papers/p111-hellerstein-cidr17.pdf
Author(s): Joe Hellerstein, Joseph Gonzalez, Vikram Sreekanti, et al.
Scalable Atomic Visibility with RAMP Transactions
Databases can provide scalability by partitioning data across several servers. However, multipartition, multioperation transactional access is often expensive, employing coordination-intensive locking, validatio...
08/15/2016
https://tinyurl.com/zf5svjr
Author(s): Joe Hellerstein, Ion Stoica, Ali Ghodsi, Alan Fekete, Peter Bailis
Putting logic-based distributed systems on stable grounds
In the Declarative Networking paradigm, Datalog-like languages are used to express distributed computations. Whereas recently formal operational semantics for these languages have been developed, a...
Theory and Practice of Logic Programming, Volume 16, Issue 4,
https://tinyurl.com/zwb4nf8
Author(s): Joe Hellerstein, TOM J. AMELOOT, JAN VAN DEN BUSSCHE, WILLIAM R. MARCZAK, PETER ALVARO
ReStream: Accelerating Backtesting and Stream Replay with Serial-Equivalent Parallel Processing
Real-time predictive applications can demand continuous and agile development, with new models constantly being trained, tested, and then deployed. Training and testing are done by replaying stored event logs,...
SoCC '16 - Proceedings of the Seventh ACM Symposium on Cloud Computing, 10/06/2016
https://tinyurl.com/h9csy93
Author(s): Joe Hellerstein, Erik T. Krogen
Grail Quest: A New Proposal for Hardware-assisted Garbage Collection
Many big data systems are written in garbage-collected languages and GC has a substantial impact on throughput, responsiveness and predicability of these systems. However, despite decades of research, there is s...
Sixth Workshop on Architectures and Systems for Big Data (ASBD 2016), 03/19/2017
https://people.eecs.berkeley.edu/~maas/papers/maas-asbd16-hwgc.pdf
Author(s): Martin Maas, Krste Asanovic, John Kubiatowicz
Time-evolving Graph Processing at Scale
Time-evolving graph-structured big data arises naturally in many application domains such as social networks and communication networks. However, existing graph processing systems lack support for efficient comp...
Graph Data-management Experiences & Systems (GRADES), 06/24/2016
Author(s): Anand Padmanabha Iyer, Li Erran Li, Tathagata Das, Ion Stoica
SparkR: Scaling R Programs with Spark
R is a popular statistical programming language with a number of extensions that support data processing and machine learning tasks. However, interactive data analysis in R is usually limited as the R runtime...
Author(s): Shivaram Venkataraman, Ali Ghodsi, Ion Stoica
Decentralized Anonymous Micropayments
Anonymity-preserving micropayments for Bitcoin-like currencies....
EUROCRYPT 2017, 04/30/2017
http://ia.cr/2016/1033
Author(s): Alessandro Chiesa, Matthew Green, Jingcheng Liu, Peihan Miao, Ian Miers, Pratyush Mishra
Iterative methods for solving factorized linear systems
Stochastic iterative algorithms such as the Kaczmarz and Gauss-Seidel methods have gained recent attention because of their speed, simplicity, and the ability to approximately solve large-scale linear systems of...
01/25/2017
https://arxiv.org/abs/1701.07453
Author(s): Aaditya Ramdas, Anna Ma, Deanna Needell
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy
We propose a method to optimize the representation and distinguishability of samples from two probability distributions, by maximizing the estimated power of a statistical test based on the maximum mean discrepa...
5th International Conference on Learning Representations, Toulon, 2017, 11/14/2016
https://arxiv.org/abs/1611.04488
Author(s):
The p-filter: multi-layer FDR control for grouped hypotheses
In many practical applications of multiple hypothesis testing using the False Discovery Rate (FDR), the given hypotheses can be naturally partitioned into groups, and one may not only want to control the number ...
Journal of the Royal Statistical Society, Series B (Methodology), 11/30/2016
https://arxiv.org/abs/1512.03397
Author(s):
MiniCrypt: Reconciling Encryption and Compression for Big Data Stores.
More and more applications and web services generate larger and larger amounts of confidential data, such as user and financial data. On one hand, these systems must use encryption to ensure confidentiality, whi...
EuroSys 2017,
https://pdfs.semanticscholar.org/5aa9/0f7bc1a8282daf74a65aec86022d7ddbad9b.pdf
Author(s): Wenting Zheng, Raluca Ada Popa, Ion Stoica, Rachit Agarwal, Frank Li
Opaque: An Oblivious and Encrypted Distributed Analytics Platform.
As enterprises move to cloud-based analytics, the risk of cloud security breaches poses a serious threat. Encrypting data at rest and in transit is a major first step. However, data must still be decrypted in me...
NSDI 2017 (USENIX Symposium of Networked Systems Design and Implementation), 03/27/2017
Author(s): Wenting Zheng, Raluca Ada Popa, Ion Stoica, Joseph Gonzalez, Ankur Dave, Jethro Beekman
Random Projection Design for Scalable Implicit Smoothing of Randomly Observed Stochastic Processes
Standard methods for multi-variate time series analysis are hampered by sampling at random timestamps, long range dependencies , and the scale of the data. In this paper we present a novel estimator for...
AISTATS 2017,
https://rise.cs.berkeley.edu/wp-content/uploads/2017/03/Aistats_camera_ready.pdf
Author(s): Francois Belletti, Joseph Gonzalez, Evan Sparks, Alexandre M. Bayen
Decoding from Pooled data: Phase Transitions of Message Passing
We consider the problem of decoding a discrete signal of categorical variables from the observation of several histograms of pooled subsets of it. We present an Approximate Message Passing (AMP) algorithm for re...
short version submitted to International Symposium on Information Theory (ISIT), long version to be submitted to IEEE Transactions on Information Theory (IEEEIT),
https://arxiv.org/abs/1702.02279
Author(s): Aaditya Ramdas, Ahmed El Alaoui, Michael Jordan, Florent Krzakala, Lenka Zdeborova
Decoding from Pooled data: Sharp Information-Theoretic Bounds
Consider a population consisting of n individuals, each of whom has one of d types (e.g. their blood type, in which case d=4). We are allowed to query this database by specifying a subset of the population, and ...
submitted to Annals of Applied Probability (AoAP),
https://arxiv.org/abs/1611.09981
Author(s): Ahmed El Alaoui, Aaditya Ramdas, Michael Jordan, Florent Krzakala, Lenka Zdeborova
Universality of Mallows’ and degeneracy of Kendall’s kernels for rankings
Kernel methods provide an attractive framework for aggregating and learning from ranking data, and so understanding the fundamental properties of kernels over permutations is a question of broad interest. We pro...
https://arxiv.org/abs/1603.08035
Author(s): Horia Mania, Aaditya Ramdas, Martin J. Wainwright, Michael Jordan, Benjamin Recht
Proteus: agile ML elasticity through tiered reliability in dynamic resource markets
Many shared computing clusters allow users to utilize excess idle resources at lower cost or priority, with the proviso that some or all may be taken away at any time. But, exploiting such dynamic resource avail...
ACM European Conference on Computer Systems (EuroSys'2017),
http://www.pdl.cmu.edu/PDL-FTP/BigLearning/proteus_abs.shtml
Author(s): Aaron Harlap, Alexey Tumanov, Andrew Chung, Gregory R. Ganger, Phil Gibbons
Morpheus: Towards Automated SLOs for Enterprise Clusters
Modern resource management frameworks for largescale analytics leave unresolved the problematic tension between high cluster utilization and job’s performance predictability—respectively coveted by operators...
Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI'16).,
https://www.usenix.org/conference/osdi16/technical-sessions/presentation/jyothi
Author(s): C. Curino, I. Menache, S. Narayanamurthy, Alexey Tumanov, J. Yaniv, R. Mavlyutov, I. Goiri, S. Krishnan, J. Kulkarni, S. Rao
Clipper: A Low-Latency Online Prediction Serving System
Machine learning is being deployed in a growing number of applications which demand real-time, accurate, and robust predictions under heavy query load. However, most machine learning frameworks and systems only ...
NSDI 2017,
https://rise.cs.berkeley.edu/wp-content/uploads/2017/02/clipper_final.pdf
Author(s): Dan Crankshaw, Xin Wang, Giulio Zhou, Michael J. Franklin, Joseph Gonzalez, Ion Stoica
A DeVIL-ish approach to inconsistency in interactive visualizations
Declarative languages have a long tradition in both the database systems and data visualization communities, separating specifications from implementations.Declarative Visual Interaction Language presents a...
HILDA 2016,
https://nsfdeclarativevis.github.io/NSFDeclarativeVis/devil-hilda16.pdf
Author(s): Yifan Wu, Joe Hellerstein, Eugene Wu