A Berkeley View of Systems Challenges for AI
A Berkeley View of Systems Challenges for AI Ion Stoica Dawn Song Raluca Ada Popa David A. Patterson Michael W. Mahoney Randy H. Katz Anthony D. Joseph Michael Jordan Joseph M. Hellerstein Joseph Gonzalez Ken Goldberg Ali Ghodsi David E. Culler Pieter Abbeel Electrical Engineering and Computer Sciences University of California at Berkeley Technical Report No. UCB/EECS-2017-159 http://www2.eecs.ber
G-OLA: Generalized Online Aggregation for Interactive Analysis on Big Data
Nearly 15 years ago, Hellerstein, Haas and Wang proposed online aggregation (OLA), a technique that allows users to (1) observe the progress of a query by showing iteratively refined approximate answers, and (2) stop the query execution once its result achieves the desired accuracy. In this demonstration, we present G-OLA, a novel mini-batch execution model that generalizes OLA to support general
[1511.06051] SparkNet: Training Deep Networks in Spark
Training deep networks is a time-consuming process, with networks for object recognition often requiring multiple days to train. For this reason, leveraging the resources of a cluster to speed up training is an important area of work. However, widely-popular batch-processing computational frameworks like MapReduce and Spark were not designed to support the asynchronous and communication-intensive
Advances and Challenges in Log Analysis - ACM Queue
December 20, 2011 Volume 9, issue 12 PDF Advances and Challenges in Log Analysis Logs contain a wealth of information for help in managing systems. Adam Oliner, UC Berkeley; Archana Ganapathi, Splunk; Wei Xu, Google Computer-system logs provide a glimpse into the states of a running system. Instrumentation occasionally generates short messages that are collected in a system-specific log. The conte
The lab that created Spark wants to speed up everything, including cures for cancer – Old GigaOm
Unknown to most of the world, the University of California, Berkeley’s AMPLab has already left an indelible mark on world of information technology, and even the web. But we haven’t yet experienced full impact of the group, which launched in 2011 under a six-year grant. Not even close. AMPLab (the AMP in which is short for algorithms, machines and people) is best known for pushing projects such as
Peter Bailis
I build data-intensive systems. I am the CEO and Founder of Sisu Data. Before Sisu, I was an assistant professor of CS at Stanford, where I founded the DAWN project. PhD Students and Postdocs Edward Gan (PhD 2020, software engineer at Databricks) Kexin Rong (PhD 2021, faculty at Georgia Tech; w/ Phil Levis) Kai Sheng Tai (PhD 2021, research scientist at Facebook AI; w/ Greg Valiant) Cody Coleman (
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Yanpei Chen's Homepage
GitHub - amplab/SparkNet: Distributed Neural Networks for Spark
Asynchronous Complex Analytics in a Distributed Dataflow Architecture | the morning paper
AMPLab’s co-creator on where big data is headed, and why Spark is so big
SampleClean: Fast and Accurate Query Processing on Dirty Data
GitHub - sjyk/sampleclean-async
AMP Camp 5: SampleClean - Sanjay Krishnan, Jiannan Wang, Daniel Haas
AMP Camp 5: Velox - Dan Crankshaw, Peter Bailis, Joey Gonzalez
For Big Data, Moore’s Law Means Better Decisions
When Data Cleaning Meets Crowdsourcing
UC Berkeley AMP Camp | The UC Berkeley Big Data AMP Camp, featuring tutorials on popular open-source software including Spark, Shark, Hive, and Mesos; overviews of the Berkeley Data Analytics System (BDAS); and Machine Learning tutorials.
Berkeley Data Systems and Foundations (DSF) Group
https://cloud.github.com/downloads/SWIMProjectUCB/SWIM/MapReduceWorkloadMASCOTSCameraReady.pdf
GitHub - SWIMProjectUCB/SWIM: Statistical Workload Injector for MapReduce - Project at UC Berkeley AMP Lab