Scientific software has emerged as an essential discipline in its own right.
Because computational models, computer architectures, and scientific software
projects have become extremely complex, the Computational Science & Engineering
(CSE) community now has a unique opportunity -and in implicit mandate- to
address pressing challenges in scientific software productivity, quality, and
sustainability. This presentation will focus on software engineering best
practices and efforts in the CSE community to foster natural incentives for
improving software quality on the way to improving computational science.
Visualization of larger data sets on HPC resources requires some special considerations. In this session, we will survey the visualization applications available to scientists on TACC resources, summarize best practices/strategies for visualizing larger data-sets and provide quick-start guide to remote visualization.
Docker and Singularity are a products for delivering container solutions.
Containers help in standardizing the process of packaging software and the
associated dependencies such that the issues related to the differences in
environment (e.g., different versions of the libraries) during the development
and release phases of a software can be mitigated. In doing so, Containers
simplifyPetasc the software installation processes by providing an easy way to
share working environments, which in turn makes it easy to use libraries with
complicated setups. Distributing a Docker or Singularity image means
distributing both the code itself as well as the environment used to run that
code allowing images to run identically on OSX, Windows, and Linux, so
generated analyses will run on any other machine and will have reproducible
Cloud Computing has become a mainstay platform for providing Information Technology services: storage, databases, networking, software development, and data analytics, to just name a few. The cloud has become very useful in application development, data recovery, hosting, audio and video streaming, software on demand, and machine learning. As such, it has spawned off multiple new services for the technology community which include Infrastructure-as-a-service (IaaS), Platform-as-a-service (PaaS), and Software-as-a-service (SaaS). Why is there a need to implement a cloud environment for computational science? Scientific data is only valid if it can be replicated. The end goal is to teach how to build a producible, scalable environment for high throughput and high performance scientific computing. Docker and Singularity containers are a key piece of technology to achieve this end goal. Hence, with containers, one can write applications that are portable across different cloud computing platforms such as Amazon AWS, Google cloud, Microsoft Azure, and Jetstream (an NSF funded cloud offering for open-science ) as well as packaging and importing software stacks to use on an many HPC resources.