EUROPEAN UNION Polish Infrastructure for Supporting Computational Science in the European Research Space The Capabilities of the GridSpace2 Experiment Workbench Jan Meizner and Distributed Computing Environments (DICE) Team Academic Computer Centre CYFRONET i3: Internet - Infrastruktury - Innowacje

Motivation  Complex scientific applications on modern computing infrastructures  Clusters, Grids, Clouds  Diverse software packages  Applications (Gaussian, NAMD,…)  Web Services  Scripts: Perl, Python, Ruby  Different users  Chemists, biologists  Programmers  End users  Various data types  Files, databases, URLs  Exploratory programming  Unstructured, dynamic, prototyping  Collaboration  Teams, communities

GridSpace2 Objectives  Facilitate dealing with application throughout its entire lifecycle (development, deployment, sharing, operation, maintenance) from single “workbench” where all available software is integrated  Reflect and support a natural daily style of work with a suite of software – workflows, (not formalized) procedures, task paths etc.  Addresses a specific type of application called experiments

GridSpace2 Features  Platform – as opposed to concrete application  General-purpose  Exploits Web 2.0 opportunities in facilitating application development, operation, provisioning

GridSpace2 Experiment  Experiment - a process that combines a sequence of activities (usage of programs, services) that act on input data in order to produce experiment results  Experiment plan – a specification of the sequence of activities  Experiment run – an enactment of the experiment plan on particular input data, producing particular results  Complex workflow going beyond manual simple and repeatable execution of single programs  Exploratory programming  Unstructured, dynamic, prototyping, further activities not known a priori

GridSpace2 Experiment Plan  Combines steps realized on a range of software environments, platforms, tools, languages etc  Developed, shared and reused collaboratively amongst ad-hoc researching teams  Composed of collaboratively owned libraries and services used (called gems) and experiment parts (called snippets)

Exploratory Programming  Involves experimentation and exploring – step by step programming where steps are likely not known in advance but rather provided ad-hoc basing on the results of previous ones  Experiment needs to be re-enacted many times with some ad-hoc customization made dynamically while the workflow enactment has already started  Cannot be fully automated and needs continuous supervision, validation or even intrusion  Dynamic nature of experiment plan – certain decisions taken at runtime (e.g. code provided from input data)  But: Despite its indirect development process experiment still needs to be traceable, verifiable, easily re-runnable and its outcome – straightforwardly reproducible,

Security in GridSpace2  Connectivity via:  HTTPS (browser Experiment Workbench)  SSH/SCP/SFTP (ExperimentWorkbench Experiment Host)  User account context on Experiment Host  OS-level accessibility rights to files  Snippet code can contain a „secret” literal introduced by meta-markup  During the execution this meta-markup is replaced with secret value taken from personal secret database called Wallet  Available Wallet implementations:  Simple file database located on the Experiment Host  Remote Central Wallet (ReCeW)  ReCeW (Remote Central Wallet) – key features:  Security – HTTPS protected REST API and AES-256 encryption of stored credentials  Highly efficient implementation as native (C++) application  Extendable through plug-in mechanism (4 types of plug-ins)

9 Working with GridSpace2  Easy access using Web browser  Experiment Workbench  Constructing experiment plans from code snippets  Interactively run experiments  Experiment Execution Environment  Multiple interpreters  Access to libraries, programs and services (gems)  Access to computing infrastructure  Cluster, grid, cloud

Application: Analysis of water solutions of aminoacids  Involving multiple steps realized with many tools, langauges and libraries used for  Packmol – molecular dynamics simulations of packing molecules in a defined regions of space  Jmol – visualization of solution  Gaussian – computing a spectrum of the solution  Python/CCLIB – extracting spectrum info  jqPlot – displaying plot Collaboration with computational chemists of ACC Cyfronet AGH and Departament of Chemistry, Jagiellonian University, Dr. Mariusz Sterzel, Klemens Noga

11 Conclusions  Complex scientific applications need dedicated tools and approaches.  In-silico experiments are supported by Virtual Laboratory powered by GridSpace2 technology.  Applications:  Bioinformatics  Computational chemistry  More are welcome!  Virtual laboratory is open for PL-Grid users.

12 References  – open the Virtual Laboratory in your browser  – learn more about GridSpace technology  – Distributed Computing Environemnts Team (DICE) website