Present days cores composing a system-on-chip might be interconnected by means of both
dedicated channels or shared buses. Nevertheless, future systems will have strong …

Data‐driven applications are essential to handle the ever‐increasing volume, velocity, and
veracity of data generated by sources such as the Web and Internet of Things (IoT) devices …

Current de-facto parallel programming models like OpenMP and MPI make it difficult to
extract task-level dataflow parallelism as opposed to bulk-synchronous parallelism. Task …

Automated program repair has excited researchers for more than a decade, yet it has yet to
find full scale deployment in industry. We report our experience with SAPFIX: the first …

The CloudMdsQL polystore provides integrated access to multiple heterogeneous data
stores, such as RDBMS, NoSQL or even HDFS through a big data analytics framework such …

Managing data produced in the Internet of Things according to the traditional data-center
based approach is becoming no longer appropriate. Devices are improving their …

The performance of distributed-memory applications, many of which are written in MPI,
critically depends on how well the applications can ameliorate the long latency of data …

Distributed-memory matrix multiplication (MM) is a key element of algorithms in many
domains (machine learning, quantum physics). Conventional algorithms for dense MM rely …

We present a novel strategy to improve load balancing for large scale Bayesian inference
problems. Load imbalance can be particularly destructive in generation based uncertainty …

This paper describes Petal, a prototype tool that uses compiler-analysis techniques to
automate code transformations to hide communication costs behind computation by …