Large-scale visualization systems are typically designed to efﬁ-
ciently “push” datasets through the graphics hardware. However,
exploratory visualization systems are increasingly expected to support scalable data manipulation, restructuring, and querying capabilities in addition to core visualization algorithms. We posit that new emerging abstractions for parallel data processing, in particular computing clouds, can be leveraged to support large-scale data exploration through visualization. In this paper, we take a ﬁrst step in evaluating the suitability of the MapReduce framework to implement large-scale visualization techniques. MapReduce is a lightweight, scalable, general-purpose parallel data processing framework increasingly popular in the context of cloud computing. Speciﬁcally, we implement and evaluate a representative suite of visualization tasks (mesh rendering, isosurface extraction, and
mesh simpliﬁcation) as MapReduce programs, and report quantitative performance results applying these algorithms to realistic datasets. For example, we perform isosurface extraction of up to l6 isovalues for volumes composed of 27 billion voxels, simpliﬁcation of meshes with 30GBs of data and subsequent rendering with image resolutions up to 800002 pixels. Our results indicate that the parallel scalability, ease of use, ease of access to computing resources, and fault-tolerance of MapReduce offer a promising foundation for a combined data manipulation and data visualization.