The ability to scale distributed optimization to large node counts has been one of the main
enablers of recent progress in machine learning. To this end, several techniques have been …

Over the past decade, many programming languages and systems for parallel-computing
have been developed, eg, Fork/Join and Habanero Java, Parallel Haskell, Parallel ML, and …

Self-adjusting networks (SANs) utilize novel optical switching technologies to support dynamic
physical network topology reconfiguration. SANs rely on online algorithms to exploit this …

The design and implementation of efficient concurrent data structures has seen significant
attention. However, most of this work has focused on concurrent data structures providing …

The paper presents the first concurrency-optimal implementation of a binary search tree (BST).
The implementation, based on a standard sequential implementation of a partially-…

Self-adjusting networks (SANs) have the ability to adapt to communication demand by
dynamically adjusting the workload (or demand) embedding, ie, the mapping of communication …

Dynamic algorithms are used to compute a property of some data while the data undergoes
changes over time. Many dynamic algorithms have been proposed but nearly all are …

Background Many cancer genomes are extensively rearranged with highly aberrant
chromosomal karyotypes. Structural and copy number variations in cancer genomes can be …

A standard design pattern found in many concurrent data structures, such as hash tables or
ordered containers, is an alternation of parallelizable sections that incur no data conflicts and …

Demand-aware communication networks are networks whose topology is optimized toward
the traffic they need to serve. These networks have recently been enabled by novel optical …