A major challenge in modelling and simulation is the need to combine expertise in both
software technologies and a given scientific domain. When High-Performance Computing …

In recent years, there has been a renewed interest in languages and systems for large scale
distributed computing. Unfortunately, most systems available to the end user use a custom …

The cloud, rack-scale computing, and multi-core are the basis for today's computing
platforms. Their intrinsic parallelism is a challenge for programmers, specially in areas …

The advent of new sequencing technologies has generated extremely large amounts of
information. To successfully apply bioinformatics tools to such large datasets, they need to …

With the growing amount of computational resources available to researchers today and the
explosion of scientific data in modern research, it is imperative that scientists be able to …

Most big‐data analysis systems require users to adopt restricted abstractions to achieve
scaling and system stability. While highly effective at establishing data locality and …

The continued exponential growth of storage capacity has catalyzed the broad acquisition of
scientific data which must be processed. While today's large data analysis systems are …

Scientific applications often harness the concurrency in their workloads to partition and
operate them as independent tasks and achieve reasonable performance. To improve …

Empirical analysis of statistical algorithms often demands time-consuming experiments
which are best performed on high performance computing clusters. We present two R …