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In collaborative software development, program merging is <italic>the</italic> mechanism to integrate changes from multiple programmers. Merge algorithms in modern version control systems report a conflict when changes interfere textually. Merge conflicts ...

Collaborative software development is an integral part of the modern software development life cycle, essential to the success of large-scale software projects. When multiple developers make concurrent changes around the same lines of code, a merge ...

Program merging is standard practice when developers integrate their individual changes to a common code base. When the merge algorithm fails, this is called a merge conflict. The conflict either manifests as a textual merge conflict where the merge ...

Testing is widely recognized as an important stage of the software development lifecycle. Effective software testing can provide benefits such as bug finding, preventing regressions, and documentation. In terms of documentation, unit tests express a unit'...

Recent trends towards large machine learning models require both training and inference tasks to be distributed. Considering the huge cost of training these models, it is imperative to unlock optimizations in computation and communication to obtain best ...

Collective communication algorithms are an important component of distributed computation. Indeed, in the case of deep-learning, collective communication is the Amdahl's bottleneck of data-parallel training.

This paper introduces SCCL (for Synthesized ...

Multilingual data-parallel pipelines, such as Microsoft's Scope and Apache Spark, are widely used in real-world analytical tasks. While the involvement of multiple languages (often including both managed and native languages) provides much convenience ...

Fully Homomorphic Encryption (FHE) refers to a set of encryption schemes that allow computations on encrypted data without requiring a secret key. Recent cryptographic advances have pushed FHE into the realm of practical applications. However, ...

Building scalable big data programs currently requires programmers to combine relational (SQL) with non-relational code (Java, C#, Scala). Relational code is declarative - a program describes what the computation is and the compiler decides how to ...

Heterogeneous hardware is central to modern advances in performance and efficiency. Mainstream programming models for heterogeneous architectures, however, sacrifice safety and expressiveness in favor of low-level control over performance details. The ...

Query languages for big data analysis provide user extensibility through a mechanism of user-defined operators (UDOs). These operators allow programmers to write proprietary functionalities on top of a relational query skeleton. However, achieving ...

Many applications compute with estimated and uncertain data. While advances in probabilistic programming help developers build such applications, debugging them remains extremely challenging. New types of errors in probabilistic programs include 1) ...

List comprehensions provide a powerful abstraction mechanism for expressing computations over ordered collections of data declaratively without having to use explicit iteration constructs. This paper puts forth effectful comprehensions as an elegant way ...

This paper proposes an efficient parallel algorithm for an important class of dynamic programming problems that includes Viterbi, Needleman--Wunsch, Smith--Waterman, and Longest Common Subsequence. In dynamic programming, the subproblems that do not ...

The performance-intensive part of a large-vocabulary continuous speech-recognition system is the Viterbi computation that determines the sequence of words that are most likely to generate the acoustic-state scores extracted from an input utterance. This ...

This article proposes efficient parallel methods for an important class of dynamic programming problems that includes Viterbi, Needleman-Wunsch, Smith-Waterman, and Longest Common Subsequence. In dynamic programming, the subproblems that do not depend ...

User-defined aggregations (UDAs) are integral to large-scale data-processing systems, such as MapReduce and Hadoop, because they let programmers express application-specific aggregation logic. System-supported associative aggregations, such as counting ...

This paper presents Yinyang K-means, a new algorithm for K-means clustering. By clustering the centers in the initial stage, and leveraging efficiently maintained lower and upper bounds between a point and centers, it more effectively avoids unnecessary ...

Computing distances among data points is an essential part of many important algorithms in data analytics, graph analysis, and other domains. In each of these domains, developers have spent significant manual effort optimizing algorithms, often through ...

Building correct, efficient systems that reason about the approximations produced by sensors, machine learning, big data, humans, and approximate hardware and software requires new standards and abstractions. The Uncertain &lt;;T&gt;; software abstraction ...