23 citations · 26 across the 13 of their papers we have counts for
13 papers
Complete CALM: A Coordination Criterion for Specifications
Joseph M. Hellerstein
The CALM theorem connects coordination-freedom to monotonicity, but is tied to relational transducers and set-inclusion growth. We generalize it to arbitrary concurrent specificati…
Towards Relational Contextual Equality Saturation
Tyler Hou, Shadaj Laddad, Joseph M. Hellerstein
Equality saturation is a powerful technique for program optimization. Contextual equality saturation extends this to support rewrite rules that are conditioned on where a term appe…
Steering Semantic Data Processing With DocWrangler
Shreya Shankar, Bhavya Chopra, Mawil Hasan +5
Unstructured text has long been difficult to automatically analyze at scale. Large language models (LLMs) now offer a way forward by enabling {\em semantic data processing}, where…
The Free Termination Property of Queries Over Time
Conor Power, Paraschos Koutris, Joseph M Hellerstein
Building on prior work on distributed databases and the CALM Theorem, we define and study the question of free termination: in the absence of distributed coordination, what query p…
Flo: a Semantic Foundation for Progressive Stream Processing
Shadaj Laddad, Alvin Cheung, Joseph M. Hellerstein +1
Streaming systems are present throughout modern applications, processing continuous data in real-time. Existing streaming languages have a variety of semantic models and guarantees…
Flow with FlorDB: Incremental Context Maintenance for the Machine Learning Lifecycle
Rolando Garcia, Pragya Kallanagoudar, Chithra Anand +4
In this paper we present techniques to incrementally harvest and query arbitrary metadata from machine learning pipelines, without disrupting agile practices. We center our approac…
Suki: Choreographed Distributed Dataflow in Rust
Shadaj Laddad, Alvin Cheung, Joseph M. Hellerstein
Programming models for distributed dataflow have long focused on analytical workloads that allow the runtime to dynamically place and schedule compute logic. Meanwhile, models that…
Optimizing Distributed Protocols with Query Rewrites [Technical Report]
David Chu, Rithvik Panchapakesan, Shadaj Laddad +6
Distributed protocols such as 2PC and Paxos lie at the core of many systems in the cloud, but standard implementations do not scale. New scalable distributed protocols are develope…
"We Have No Idea How Models will Behave in Production until Production": How Engineers Operationalize Machine Learning
Shreya Shankar, Rolando Garcia, Joseph M Hellerstein +1
Organizations rely on machine learning engineers (MLEs) to deploy models and maintain ML pipelines in production. Due to models' extensive reliance on fresh data, the operationaliz…
Multiversion Hindsight Logging for Continuous Training
Rolando Garcia, Anusha Dandamudi, Gabriel Matute +4
Production Machine Learning involves continuous training: hosting multiple versions of models over time, often with many model versions running at once. When model performance does…
Optimizing the cloud? Don't train models. Build oracles!
Tiemo Bang, Conor Power, Siavash Ameli +2
We propose cloud oracles, an alternative to machine learning for online optimization of cloud configurations. Our cloud oracle approach guarantees complete accuracy and explainabil…
Optimizing Stateful Dataflow with Local Rewrites
Shadaj Laddad, Conor Power, Tyler Hou +2
Optimizing a stateful dataflow language is a challenging task. There are strict correctness constraints for preserving properties expected by downstream consumers, a large space of…
Invited Paper: Initial Steps Toward a Compiler for Distributed Programs
Joseph M. Hellerstein, Shadaj Laddad, Mae Milano +2
In the Hydro project we are designing a compiler toolkit that can optimize for the concerns of distributed systems, including scale-up and scale-down, availability, and consistency…