The development of ZeroBuf was co-funded by the HBP during the Ramp-up Phase. This page is kept for reference but will no longer be updated. ZeroBuf implements zero-copy, zero-serialize, zero-hassle protocol buffers. It is a replacement for FlatBuffers, resolving the following shortcomings:
Direct get and set functionality on the defined data members
A single memory buffer storing all data members, which is directly serializable
Usable, random read and write access to the the data members
Zero copy of the data used by the (C++) implementation from and to the network
The development of Monsteer was co-funded by the HBP during the Ramp-up Phase. This page is kept for reference but will no longer be updated.
Monsteer is a library for Interactive Supercomputing in the neuroscience domain. Monsteer facilitates the coupling of running simulations (currently NEST) with interactive visualization and analysis applications. Monsteer supports streaming of simulation data to clients (currenty only spikes) as well as control of the simulator from the clients (also kown as computational steering). Monsteer’s main components are a C++ library, an MUSIC-based application and Python helpers.
The development of neuroFiReS was co-funded by the HBP during the Ramp-up Phase. This page is kept for reference but will no longer be updated.
neuroFiReS is a library for performing search and filtering operations using both data contents and metadata. These search operations will be tightly coupled with visualization in order to improve insight gaining from complex data. A first prototype (named spineRet) for searching and filtering over segmented spine data has been developed.
NeuroLOTs is a set of tools and libraries that allow creating neuronal meshes from a minimal skeletal description. It generates soma meshes using FEM deformation and allows to interactively adapt the tessellation level using different criteria (user-defined, camera distance, etc.)
NeuroTessMesh provides a visual environment for the generation of 3D polygonal meshes that approximate the membrane of neuronal cells, starting from the morphological tracings that describe neuronal morphologies. The 3D models can be tessellated at different levels of detail, providing either a homogeneous or an adaptive resolution of the model. The soma shape is recovered from the incomplete information of the tracings, applying a physical deformation model that can be interactively adjusted. The adaptive refinement process performed in the GPU generates meshes, that allow good visual quality geometries at an affordable computational cost, both in terms of memory and rendering time. NeuroTessMesh is the front-end GUI to the NeuroLOTs framework.
The development of DLB was co-funded by the HBP during the second project phase (SGA1). This page is kept for reference but will no longer be updated.
DLB is a library devoted to speedup hybrid parallel applications. And at the same time DLB improves the efficient use of the computational resources inside a computing node. The DLB library will improve the load balance of the outer level of parallelism by redistributing the computational resources at the inner level of parallelism. This readjustment of resources will be done at dynamically at runtime. This dynamism allows DLB to react to different sources of imbalance: Algorithm, data, hardware architecture and resource availability among others.
The first version that was integrated in the HPAC Platform was v1.1.
Used on MareNostrum IV supercomputer for some applications
The development of HCFFT was co-funded by the HBP during the Ramp-up Phase. This page is kept for reference but will no longer be updated.
HCFFT (Hyperbolic Cross Fast Fourier Transform) is a software package to efficiently treat high-dimensional multivariate functions. The implementation is based on the fast Fourier transform for arbitrary hyperbolic cross / sparse grid spaces.
Efficient serialization of events using flatbuffers
The main intention of ZeroEQ is to allow the linking of applications using automatic discovery. Linking can be used to connect multiple visualization applications, or to connect simulators with analysis and visualization codes to implement streaming and steering. One example of the former is the interoperability of NeuroScheme with RTNeuron, and one for the latter is the streaming and steering between NEST and RTNeuron. Both were reported previously, whereas the current extensions focus on the implementation of the request-reply interface.
The development of ViSTA Virtual Reality Toolkit was co-funded by the HBP during the Ramp-up Phase. This page is kept for reference but will no longer be updated.
The ViSTA Virtual Reality Toolkit allows the integration of virtual reality (VR) technology and interactive, 3D visualisation into technical and scientific applications. The toolkit aims to enhance scientific applications with methods and techniques of VR and immersive visualization, thus enabling researchers from multiple disciplines to interactively analyse and explore their data in virtual environments. ViSTA is designed to work on multiple target platforms and operating systems, across various display devices (desktop workstations, powerwalls, tiled displays, CAVEs, etc.) and with various interaction devices.
The development of Equalizer was co-funded by the HBP during the Ramp-up Phase. This page is kept for reference but will no longer be updated.
Equalizer is a parallel rendering framework to create and deploy parallel, scalable OpenGL applications. It provides the following major features to facilitate the development and deployment of scalable OpenGL applications:
Runtime Configurability: An Equalizer application is configured automatically or manually at runtime and can be deployed on laptops, multi-GPU workstations and large-scale visualization clusters without recompilation.
Runtime Scalability: An Equalizer application can benefit from multiple graphics cards, processors and computers to scale rendering performance, visual quality and display size.
Distributed Execution: Equalizer applications can be written to support cluster-based execution. Equalizer uses the Collage network library, a cross-platform C++ library for building heterogeneous, distributed applications.
The development of Deflect Client Library was co-funded by the HBP during the Ramp-up Phase. This page is kept for reference but will no longer be updated.
Deflect is a C++ library to develop applications that can send and receive pixel streams from other Deflect-based applications, for example DisplayCluster. The following applications are provided which make use of the streaming API:
DesktopStreamer: A small utility that allows the user to stream the desktop.
SimpleStreamer: A simple example to demonstrate streaming of an OpenGL application.
The development of RTNeuron in the HPAC Platform was co-funded by the HBP during the second project phase (SGA1). This page is kept for reference but will no longer be updated.
RTNeuron is a scalable real-time rendering tool for the visualisation of neuronal simulations based on cable models. Its main utility is twofold: the interactive visual inspection of structural and functional features of the cortical column model and the generation of high quality movies and images for presentations and publications. The package provides three main components:
A high level C++ library.
A Python module that wraps the C++ library and provides additional tools.
The Python application script rtneuron-app.py
A wide variety of scenarios is covered by rtneuron-app.py. In case the user needs a finer control of the rendering, such as in movie production or to speed up the exploration of different data sets, the Python wrapping is the way to go. The Python wrapping can be used through an IPython shell started directly from rtneuron-app.py or importing the module rtneuron into own Python programs. GUI overlays can be created for specific use cases using PyQt and QML.
RTNeuron is available on the pilot system JULIA and on JURECA as environment module.
Date of release
Version of software
Version of documentation
https://developer.humanbrainproject.eu/docs/projects/RTNeuron/2.11/index.html; Open sourcing scheduled for June 2018