This chapter describes the design and features of a visualization tool called ParaView
(http://www. paraview. org), a tool that allows scientists to visualize and analyze extremely …

Visualization and analysis tools, techniques, and algorithms have undergone a rapid
evolution in recent decades to accommodate explosive growth in data size and complexity …

As scientific supercomputing moves toward petascale and exascale levels, in situ
visualization stands out as a scalable way for scientists to view the data their simulations …

Parallel supercomputing has traditionally focused on the inner kernel of scientific
simulations: the solver. The front and back ends of the simulation pipeline---problem …

Continuing improvements in CPU and GPU performances as well as increasing multi-core
processor and cluster-based parallelism demand for flexible and scalable parallel rendering …

The ever-increasing amounts of simulation data produced by scientists demand high-end
parallel visualization capability. However, image compositing, which requires interprocessor …

Sort-last parallel rendering is an efficient technique to visualize huge datasets on COTS
clusters. The dataset is subdivided and distributed across the cluster nodes. For every frame …

Collective communication operations can dominate the cost of large-scale parallel
algorithms. Image compositing in parallel scientific visualization is a reduction operation …

The only proven method for performing distributed-memory parallel rendering at large
scales, tens of thousands of nodes, is a class of algorithms called sort last. The fundamental …

Parallel volume rendering offers a feasible solution to the large data visualization problem
by distributing both the data and rendering calculations among multiple computers …