The development of VTK-m was co-funded by the HBP during the second project phase (SGA1). This page is kept for reference but will no longer be updated.
VTK-m is a scientific visualization and analysis framework that offers a wealth of building blocks to create visualization and analysis applications. VTK-m facilitates scaling those applications to massively parallel shared memory systems, and it will – due to its architecture – most likely also run efficiently on future platforms.
HPX is a task-based programming model. It simplifies the formulation of well-scaling, highly-parallel algorithms. Integrating this programming model into VTK-m streamlines the formulation of its parallel building blocks and thus makes their deployment on present and emerging HPC platforms more efficient. Since neuroscientific applications require more and more compute power as well as memory, harnessing the available resources will become a challenge in itself. By combining VTK-m and HPX into task-based analysis and visualization, we expect to provide suitable tools to effectively face this challenge and facilitate building sophisticated interactive visual analysis tools, tailored to the neuroscientists’ needs.
Parallel primitive algorithms required for VTK-m have been added to HPX along with API support to enable the full range of visualization algorithms developed for VTK-m. A new scheduler has been developed that accepts core/numa placement hints from the programmer such that cache reuse can be maximized and traffic between sockets minimized. High performance tasks that access data shared by application and visualization can use this capability to improve performance. The thread pool management was improved to allow visualization tasks, communication tasks, and application tasks to execute on different cores if necessary, which reduces latency between components and improves the overall throughput of the distributed application. RDMA primitives have been added to the HPX messaging layer. These improvements make it possible to scale HPX applications to very high node/core counts. Respective tests have been successful on 10k nodes using 650k cores.
|Date of release||November 2017|
|Version of software||1.1|
|Version of documentation||1.1|
|Responsible||John Biddiscombe (EPFL): firstname.lastname@example.org|
|Requirements & dependencies|
|Target system(s)||HPC platforms|