The in situ pipeline consists of a set of libraries that can be integrated into neuronal network simulators developed by the HBP to enable live visual analysis during the runtime of the simulation. The library called ‘nesci’ (neuronal simulator conduit interface) stores the raw simulation data into a common conduit format and the library called ‘contra’ (conduit transport) transports the serialized data from one endpoint to another using a variety of different (network) protocols. The pipeline currently works with NEST and Arbor. Support for TVB is currently in development.
Prototypical implementation into the HPAC Platform finalised in February 2019.
Date of release
First released in July 2018 with continuous updates (see also above)
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.
Minimum configuration to configure using cmake, compile and run Monsteer:
A Linux box,
GCC compiler 4.8+,
CMake 2.8+,
Boost 1.54,
MPI (OpenMPI, mvapich2, etc),
NEST simulator 2.4.2,
MUSIC 1.0.7,
Python 2.6,
See also: http://bluebrain.github.io/Monsteer-0.3/_user__guide.html#Compilation
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 InDiProv was co-funded by the HBP during the Ramp-up Phase. This page is kept for reference but will no longer be updated.
This server-side tool is meant to be used for the creation of provenance tracks in context of interactive analysis tools and visualization applications. It is capable of tracking multi-view and multiple applications for one user using this ensemble. It further is able to extract these tracks from the internal data base into a XML-based standard format, such as the W3C Prov-Model or the OPM format. This enables the integration to other tools used for provenance tracking and will finally end up in the UP.
Written in C++ , Linux environment, MySQL server 5.6, JSON library for annotation, CodeSynthesis XSD for XML serialization and parsing, ZeroMQ library, Boost library, xercex-c library and mysqlcppcon library
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.
The development of ZeroEQ was co-funded by the HBP during the second project phase (SGA1). This page is kept for reference but will no longer be updated.
ZeroEQ is a cross-platform C++ library to publish and subscribe for events. It provides the following major features:
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 new version 1.15 provides the following new features as compared to version 1.14 that was part of the HBP-internal Platform Release in M18. It is available on SourceForge: http://sourceforge.net/projects/vistavrtoolkit/
The development of PyCOMPSs was co-funded by the HBP during the Ramp-up Phase. This page is kept for reference but will no longer be updated, apart from release notes.
PyCOMPSs is the Python binding of COMPSs, (COMP Superscalar) a coarse-grained programming model oriented to distributed environments, with a powerful runtime that leverages low-level APIs (e.g. Amazon EC2) and manages data dependencies (objects and files). From a sequential Python code, it is able to run in parallel and distributed.
COMPSs screenshot
Releases
PyCOMPSs is based on COMPSs. COMPSs version 1.3 was released in November 2015, version 1.4 in May 2016 and version 2.0 in November 2016.
New features in COMPSs v1.3
Runtime
Persistent workers: workers can be deployed on computing nodes and persist during all the application lifetime, thus reducing the runtime overhead. The previous implementation of workers based on a per task process is still supported.
Enhanced logging system
Interoperable communication layer: different inter-nodes communication protocol is supported by implementing the Adaptor interface (JavaGAT and NIO implementations already included)
Simplified cloud connectors interface
JClouds connector
Python/PyCOMPSs
Added constraints support
Enhanced methods support
Lists accepted as a tasks’ parameter type
Support for user decorators
Tools
New monitoring tool: with new views, as workload and possibility of visualizing information about previous runs
Enhanced tracing mechanism
Simplified execution scripts
Simplified installation on supercomputers through better scripts
New features in COMPSs v1.4
Runtime
Added support for Docker
Added support for Chameleon Cloud
Object cache for persistent workers
Improved error management
Added connector for submitting tasks to MN supercomputer from external COMPSs applications
Bug-fixes
Python/PyCOMPSs
General bug-fixes
Tools
Enhanced Tracing mechanism:
Reduced overhead using native Java API
Added support for communications instrumentation added
Added support for PAPI hardware counters
Known Limitations
When executing Python applications with constraints in the cloud the initial VMs must be set to 0
New features in COMPSs v2.0 (released November 2016)
Runtime:
Upgrade to Java 8
Support to remote input files (input files already at workers)
Integration with Persistent Objects
Elasticity with Docker and Mesos
Multi-processor support (CPUs, GPUs, FPGAs)
Dynamic constraints with environment variables
Scheduling taking into account the full tasks graph (not only ready tasks)
Support for SLURM clusters
Initial COMPSs/OmpSs integration
Replicated tasks: Tasks executed in all the workers
Explicit Barrier
Python:
Python user events and HW counters tracing
Improved PyCOMPSs serialization. Added support for lambda and generator parameters.
C:
Constraints support
Tools:
Improved current graph visualization on COMPSs Monitor
Improvements:
Simplified Resource and Project files (NO retrocompatibility)
Improved binding workers execution (use pipes instead of Java Process Builders)
Simplifies cluster job scripts and supercomputers configuration
Several bug fixes
Known Limitations:
When executing python applications with constraints in the cloud the initial VMs must be set to 0
New features in PyCOMPSs/COMPSs v2.1 (released June 2017)
New features:
Runtime:
New annotations to simplify tasks that call external binaries
Integration with other programming models (MPI, OmpSs,..)
Support for Singularity containers in Clusters
Extension of the scheduling to support multi-node tasks (MPI apps as tasks)
Support for Grid Engine job scheduler in clusters
Language flag automatically inferred in runcompss script
New schedulers based on tasks’ generation order
Core affinity and over-subscribing thread management in multi-core cluster queue scripts (used with MKL libraries, for example)
Python:
@local annotation to support simpler data synchronizations in master (requires to install guppy)
Support for args and kwargs parameters as task dependencies
Task versioning support in Python (multiple behaviors of the same task)
New Python persistent workers that reduce overhead of Python tasks
Support for task-thread affinity
Tracing extended to support for Python user events and HW counters (with known issues)
C:
Extension of file management API (compss_fopen, compss_ifstream, compss_ofstream, compss_delete_file)
Support for task-thread affinity
Tools:
Visualization of not-running tasks in current graph of the COMPSs Monitor
Improvements
Improved PyCOMPSs serialization
Improvements in cluster job scripts and supercomputers configuration
Several bug fixes
Known Limitations
When executing Python applications with constraints in the cloud the <InitialVMs> property must be set to 0
Tasks that invoke Numpy and MKL may experience issues if tasks use a different number of MKL threads. This is due to the fact that MKL reuses threads in the different calls and it does not change the number of threads from one call to another.
New features in PyCOMPSs/COMPSs v2.3 (released June 2018)
Runtime
Persistent storage API implementation based on Redis (distributed as default implementation with COMPSs)
Support for FPGA constraints and reconfiguration scripts
Support for PBS Job Scheduler and the Archer Supercomputer
Java
New API call to delete objects in order to reduce application memory usage
Python
Support for Python 3
Support for Python virtual environments (venv)
Support for running PyCOMPSs as a Python module
Support for tasks returning multiple elements (returns=#)
Automatic import of dummy PyCOMPSs AP
C
Persistent worker with Memory-to-memory transfers
Support for arrays (no serialization required)
Improvements
Distribution with docker images
Source Code and example applications distribution on Github
Automatic inference of task return
Improved obsolete object cleanup
Improved tracing support for applications using persistent memory
Improved finalization process to reduce zombie processes
Several bug fixes
Known limitations
Tasks that invoke Numpy and MKL may experience issues if a different MKL threads count is used in different tasks. This is due to the fact that MKL reuses threads in the different calls and it does not change the number of threads from one call to another.
New features in PyCOMPSs/COMPSs v2.5 (released June 2019)
Runtime:
New Concurrent direction type for task parameter.
Multi-node tasks support for native (Java, Python) tasks. Previously, multi-node tasks were only posible with @mpi or @decaf tasks.
@Compss decorator for executing compss applications as tasks.
New runtime api to synchronize files without opening them.
Customizable task failure management with the “onFailure” task property.
Enabled master node to execute tasks.
Python:
Partial support of numba in tasks.
Support for collection as task parameter.
Supported task inheritance.
New persistent MPI worker mode (alternative to subprocess).
Support to ARM MAP and DDT tools (with MPI worker mode).
C:
Support for task without parameters and applications without src folder.
Improvements:
New task property “targetDirection” to indicate direction of the target object in object methods. Substitutes the “isModifier” task property.
Warnings for deprecated or incorrect task parameters.
Improvements in Jupyter for Supercomputers.
Upgrade of runcompss_docker script to docker stack interface.
Several bug fixes.
Known Limitations:
Tasks that invoke Numpy and MKL may experience issues if a different MKL threads count is used in different tasks. This is due to the fact that MKL reuses threads in the different calls and it does not change the number of threads from one call to another.
C++ Objects declared as arguments in a coarse-grain tasks must be passed in the task methods as object pointers in order to have a proper dependency management.
Master as worker is not working for executions with persistent worker in C++.
Coherence and concurrent writing in parameters annotated with the “Concurrent” direction must be managed by the underlaying distributed storage system.
Delete file calls for files used as input can produce a significant synchronization of the main code.
PyCOMPSs/COMPSs PIP installation package
This is a new feature available since January 2017.
Installation:
Check the dependencies in the PIP section of the PyCOMPSs installation manual (available at the documentation section of compss.bsc.es). Be sure that the target machine satisfies the mentioned dependencies.
The installation can be done in various alternative ways:
Use PIP to install the official PyCOMPSs version from the pypi live repository:
sudo -E python2.7 -m pip install pycompss -v
Use PIP to install PyCOMPSs from a pycompss.tar.gz
The development of OmpSs was co-funded by the HBP during the Ramp-up Phase. This page is kept for reference but will no longer be updated.
OmpSs is a fine-grained programming model oriented to shared memory environments, with a powerful runtime that leverages low-level APIs (e.g. CUDA/OpenCL) and manages data dependencies (memory regions). It exploits task level parallelism and supports asynchronicity, heterogeneity and data movement.
The new version 15.06 provides the following new features as compared to version 15.04 that was part of the HBP-internal Platform Release in M18:
Socket aware (scheduling taking into account processor socket)
Reductions (mechanism to accumulate results of tasks more efficiently)
Work sharing (persistence of data in the worker) mechanisms
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.
Support for Stereo and Immersive Environments: Equalizer supports stereo rendering head tracking, head-mounted displays and other advanced features for immersive Virtual Reality installations.
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.
This website describes the results of the “High Performance Analytics and Computing” (HPAC) Platform of the Human Brain Project (HBP) from the first three project phases (Ramp-up Phase 10/2013-03/2016, SGA1 04/2016-03/2018 and SGA2 04/2018-03/2020).
Due to a major project-internal reorganisation, this website will no longer be updated after March 2020.
More recent information can be found on humanbrainproject.eu and ebrains.eu.
Information about the Fenix Research Infrastructure and the ICEI project, including resource access, are available on their website.
Follow EBRAINS Computing Services@HBPHighPerfComp and Fenix RI@Fenix_RI_eu on Twitter to learn about the most recent developments and to get to know about upcoming opportunities for calls and collaborations!
to create and deploy parallel, scalable OpenGL applications. It provides the following major features to facilitate the development and deployment of scalable OpenGL applications: